礦產勘探機器學習
本頁面列出了礦物勘探和機器學習的資源，通常包含有用的程式碼和範例。機器學習和資料科學是一個巨大的領域，這些是我在實踐中發現有用和/或有趣的資源。目前指向儲存庫分支的連結是因為我更改了要使用的內容並放入清單中以供參考。也為數據分析、轉換和視覺化提供了資源，因為這是大部分工作。
歡迎提出建議：展開討論、問題或拉取請求。
目錄
- 前景
- 地質學
- 自然語言處理
- 遙感
- 數據品質
- 社群
- 雲端提供者
- 網域
- 概述
- 網路服務
- 數據入口網站
- 工具
- 本體論
- 圖書
- 數據集
- 文件
- 其他
- 一般興趣
地圖
框架
- UNCOVER-ML框架
- EIS 工具包 -> 來自 EIS Horizon EU 專案的礦物前景圖繪製的 Python 庫
- PySpatialML -> 有助於自動預測和處理柵格機器學習到 geotiff 等的庫。
- scikit 地圖
- TorchGeo -> 用於遙感樣式模型的 Pytorch 函式庫
- terratorch -> 地理空間基礎模型的靈活微調框架
- 火炬空間
- 地質學家
- Geo Deep Learning -> 基於RGB的簡單深度學習框架
- AIDE：人工智慧解決極端問題
- ExPloRA -> ExPLoRA：參數高效的擴展預訓練以適應域轉移下的視覺 Transformer
- （https://www.researchgate.net/profile/Miguel-Angel-Fernandez-Torres/publication/381917888_The_AIDE_Toolbox_Artificial_intelligence_for_disentangling_extreme_events/links/66846648714edisentangling_extreme_events/links/66846648714e30m/oolM-TM-TM-TM-3856648714e0m/ool/TM-TM-Tic/TM3-TM,385030002 月-disentang林-極端事件.pdf)
右
- CAST -> 時空模型的插入符號應用
- geodl -> 使用基於卷積神經網路的深度學習對地理空間資料進行語義分割
管道
前景
澳洲
- 氧化鐵銅金礦潛力圖
- 用於地質測繪的機器學習：演算法和應用 -> 帶有程式碼和數據的博士論文
- 鎳鈷紅土遠景圖
- Transform 2022 教學 -> 隨機森林範例
- 錫鎢合金
- 斑岩銅時空勘探
- minpot-toolkit -> Hoggard 等實驗室沉積銅邊界分析範例
- MPM-WofE -> 礦物潛力測繪 - 證據權重
探險家挑戰
- Explorer Challenge -> OZ Minerals 舉辦資料科學競賽
南澳大利亞
- Gawler_MPM -> 鈷、鉻、鎳
- 高勒克拉通的地球物理資料聚類
- [Zenodo Data]（利用地球物理資料與無監督機器學習自動偵測與礦化相關的克拉通結構）
Explore SA - 南澳能源與礦業部競賽
- 獲獎者 -> SARIG 數據信息
- Caldera -> Caldera Analytics 分析
- 因瑟托數據
- 巴特沃斯和巴尼特 -> 巴特沃斯和巴尼特條目
- 數據驅動的礦化圖
北美洲
加拿大
- 轉移預期學習
- 論文 -> 透過先驗地質遷移學習利用不平衡資料進行斑岩型礦產遠景測繪
南美洲
巴西
- Mapa Predivo -> 巴西學生項目
- Course_Predictive_Mapping_USP -> 課程項目
- 礦產前景圖
- 3D 證據權重
- 地質複雜性 SMOTE -> 包括分形分析
- MPM Jurena -> Jurena 礦產省
中國
- MPM 整合學習 -> 中國青城子鉛鋅銀金多金屬區
- 礦物前景預測卷積神經網路 -> 具有一些架構的 CNN 範例 [作者的一篇論文使用 GoogleNet]
- CSAE 礦產前景預測
- CAE 礦產前景預測
蘇丹
挪威
- 基於機器學習的方法，結合機載電磁學和土壤數據，對敏感的冰川黏土進行區域尺度測繪
地質學
- 巴西預測地質圖 -> 巴西地質調查局的工作
- 基岩深度（評估基岩深度映射的空間機器學習方法）
- DL-RMD -> 用於深度學習應用的地球物理約束電磁電阻率模型資料庫
- 地質影像分類器
- 人工智慧時代的地質測繪 -> 人工智慧時代的地質測繪
- GeolNR -> 用於三維結構地質建模應用的地質隱式神經表示
- 地圖_litologico_preditiv
- 以機器學習溫壓法繪製全球岩石圈地函壓力-溫度條件
- 神經岩石分型
- West Musgraves 地質學不確定性 -> 使用熵分析進行不確定性圖預測：非常有用
- 非平穩緩解變壓器
- 基岩與沉積物
- 自動編碼器_遙感
- 論文 -> 透過堆疊自動編碼器和聚類進行地質測繪的遙感框架
訓練資料
- 進入 Noddyverse -> 用於機器學習和反演應用的 3D 地質模型海量資料存儲
岩性
- 深度學習岩性
- 岩石原岩預測器
- SA地質岩性預測
- 自動測井關聯
- dawson-facies-2022 -> 地質影像的遷移學習
- 論文 - > 資料集大小和卷積神經網路架構對碳酸鹽岩分類遷移學習的影響
- 岩相分類 -> 使用隨機森林進行火山相分類
- 使用地質和地球物理資料進行 3D 建模的多視圖整合機器學習方法
- SedNet
鑽孔
- 異構鑽孔 - Nicta/Data61 專案報告，用於查看使用不夠遠的鑽孔進行建模
- corel -> 智慧型電腦視覺模型，可辨識岩相並在岩心影像上執行岩石分類
古山谷
- Sub3DNet1.0：用於區域尺度 3D 地下結構測繪的深度學習模型
地層學
- Predicatops -> 為碳氫化合物設計的地層預測
- 地層幾何 -> 根據地下測井預測地層幾何
結構性
- APGS -> 構造地質包
- 使用板塊驅動力一致性測試評估板塊重建模型 -> Jupyter 筆記本和數據
- 板狀
- [構造地質學食譜](https://github.com/gcmatos/structural-geology-cookbook]
- GEOMAPLEARN 1.0 -> 使用機器學習從地質圖中偵測地質結構
- 線形學習 -> 透過位勢場深度學習和聚類進行故障預測和繪圖
- LitMod3D -> 岩石圈和底層上地函的 3D 整合地球物理學-岩石學交互建模
- 其他的
模擬
- GebPy -> 生成岩石和礦物的地質數據
- OpenGeoSys -> 開發用於模擬多孔和裂縫介質中的熱-水-機械-化學 (THMC) 過程的數值方法
- Stratigraphics.jl -> 從 2D 地質統計過程創建 3D 地層學
地球動力學
- 荒地 -> 盆地和景觀動力學
- CitcomS -> 有限元素程式碼，旨在解決與地函相關的可壓縮熱化學對流問題。
- LaMEM -> 模擬各種熱力地球動力學過程，例如地函-岩石圈相互作用
- PTatin3D -> 研究與地球動力學相關的長時間尺度過程[原始動機：能夠研究岩石圈變形的高解析度三維模型的工具包]
- 黑社會 -> 地球動力學有限元素建模
地球物理學
基礎模型
- 跨域基礎模型適應：用於地球物理數據分析的開創性電腦視覺模型 -> 即將發布的一些程式碼
- 地震基礎模型->“新一代地球物理深度學習模型”
澳洲
風化層深度
- 風化層深度 -> 模型
- 具有建模填充的完整澳洲輻射測量網格
AEM 插值
電磁學
- TEM-NLnet：具有雜訊學習功能的瞬態電磁訊號深度去噪網絡
反轉
- 機器學習與地球物理反演 -> 重建 Y. Kim 和 N. Nakata 的論文（Theleading Edge，第 37 卷，第 12 期，2018 年 12 月）
歐拉反捲積
- https://legacy.fatiando.org/gallery/gravmag/euler_moving_window.html
- 最終口琴版？ https://hackmd.io/@fatiando/development-calls-2024?utm_source=preview-mode&utm_medium=rec
- https://notebook.community/joferkington/tutorials/1404_Euler_devolving/euler-devolving-examples
- https://github.com/ffigura/Euler-devolving-plateau
重力
- [透過卷積神經網路使用重力資料恢復 3D 地下室浮雕]
- 使用深度學習實現重力位勢場的穩定向下延續
- 透過機器學習方法對 3D 密度結構進行快速成像
磁學
- 透過 Adapted-SRGAN 繪製高解析度航磁圖
- MagImage2Geo3D
地震
- StorSeismic -> 一種預先訓練神經網路來儲存地震資料特徵的方法
- PINNtomo -> 使用物理資訊神經網路進行地震層析成像
地震學
岩石物理學
構造學
- 使用機器學習識別古代俯衝帶中俯衝板塊的分離 -> 筆記本
- Colab 筆記本 -> 用於 ML-SEISMIC 出版品基準測試結果的 Google Colab 輸入文件
- 釋放地球動力學中機器學習的力量
- 用於基於速率和狀態摩擦定律的斷層滑動模擬的物理資訊神經網絡
- 慢滑事件的模擬和摩擦參數估計
- 論文 -> 用於估計慢滑移區域摩擦參數空間分佈的物理資訊深度學習
地球化學
- CODAinPractice -> 成分資料分析實踐
- 地理科達
- DAN-GRF -> 連接到地理隨機森林的深度自動編碼器網絡，用於空間感知地球化學異常檢測
- Dash 地球化學勘探 -> 使用 K 均值對河流沉積物進行分類的 Web 應用程序
- 增強含單斜輝石岩漿的機器學習溫壓測量
- 論文 -> 增強單斜輝石軸承岩漿的 ML 熱壓測量
- 鋯石肥沃度模型 -> 預測斑岩銅礦床中肥沃鋯石的決策樹
- 用於預測斑岩礦床類型和資源規模的機器學習鋯石微量元素工具
- geology_class0 -> 透過鋯石微量元素判別火成岩和礦床的機器學習方法
- 紙
- 演示應用程式
- https://colab.research.google.com/drive/1-bOZgG6Nxt2Rp1ueO1SYmzIqCRiyyYcT
- 地球化學印刷
- 全球地球化學
- ICBMS Jacobina -> 金礦床黃鐵礦化學分析
- Bor 和 Cukaru Peki 對鋯石微量元素化學的解釋：傳統方法和隨機森林分類
- Indicator_minerals -> PCA 能否講述電氣石起源的故事？
- 地球化學勘探雜誌 - 流形
- LewisML -> 路易斯地層分析
- 雲母 -> 化學成分，閃亮
- 用於稀土元素地球化學異常檢測的多元統計分析和客製化偏差網路建模
- 透過地球化學數據分析繪製稀土元素的遠景圖 -> 透過地球化學數據分析繪製稀土元素的遠景圖
- QMineral Modeller -> 來自巴西地質調查局的礦物化學虛擬助手
- 太古代期間俯衝發生的長期變化 -> Zenodo 代碼存檔
- [論文] https://www.researchgate.net/publication/380289934_Secular_Changes_in_the_Occurrence_of_Subduction_During_the_ArcheanA 透過鋯石微量元素辨別火成岩和礦床的機器學習方法
克里金法
- DKNN：用於可解釋地理空間內插的深度克里格神經網絡
自然語言處理
- 文字擷取 -> 從文件中提取文字：付費的 ML 作為服務，但效果很好，可以有效地提取表格
- NASA 概念標籤 -> 關鍵字預測
- 岩相學報告資料擷取器
- SA 探索主題建模 -> 來自探索報告的主題建模
- 地層儀
- 地質語料庫
- 葡萄牙語 BERT
- 伯特 CWS
- 自動提取礦業公司鑽孔結果
詞嵌入
- 地球科學語言模型 -> 處理程式碼管道和模型 [Glove、BERT] 根據加拿大的地球科學文件進行重新訓練
- 數據集 -> 支持模型的數據
- 論文 -> 地球科學語言模型及其內在評價
- 論文 -> 自然語言處理在地球科學文本資料與前景建模的應用
- GeoVec -> 在 30 萬篇地球科學論文上訓練的單字嵌入模型
- GeoVec 模型 -> GeoVec 模型的 OSF 存儲
- 紙
- GeoVecto Litho -> 根據單字嵌入進行 3D 模型內插
- GeoVEC Playground -> 使用 Padarian GeoVec 手套詞嵌入模型
- GloVe -> 用於生成詞嵌入的斯坦福庫
- gloVE python glove, glove-python 在 Windows 上有很大問題：這裡安裝 Windows 的二進位版本：
- 手套 -> 在記憶體中向量化手套實現
- PetroVec -> 石油和天然氣行業的葡萄牙語詞彙嵌入：開發和評估
- wordembeddingsOG -> 葡萄牙語石油與天然氣字嵌入
- 葡萄牙語詞嵌入
- 西班牙詞嵌入
- 多語言對齊
命名實體識別
- Geo NER 模型 -> 命名實體識別
- GeoBERT - 模型的擁抱臉部儲存庫
- [論文]https://www.researchgate.net/publication/359186219_Few-shot_learning_for_name_entity_recognition_in_geological_text_based_on_GeoBERT
- INDUS -> NASA 科學客製化的法學碩士套件
- 如何使用 Amazon Comprehend 在不掌握 NLP 的情況下找到文本中的關鍵地球科學術語
- OzRock - OzRock：地質（礦產勘探）領域實體識別的標記資料集
本體論
- GAKG -> 多模態地球科學學術知識圖譜（中文）
- GeoERE-Net -> 使用深度學習方法理解以知識圖為基礎的地質報告
- 地質故障本體論
- geosim -> 語意觸發的地質過程定性模擬
- [https://www.duo.uio.no/handle/10852/111467]（數位地質學知識建模）-> 博士論文，收錄兩篇論文
- SIRIUS GeoAnnotator -> 上面的網站範例
- 本體CWS
- 地層知識圖（StraKG）
大型語言模型
- 地球科學大型語言模型
- GeoGalacica -> 地球科學中更大的基礎語言模型
- GeoChat -> 用於遙感的接地大視覺語言模型
- LAGDAL -> LLM 將地質圖資訊與位置實驗相匹配
聊天機器人
遙感
- CNN Sentinel -> 基於開放資料集的 CNN 衛星資料土地利用分類概述
- DEA 筆記本 -> 可擴展的機器學習範例，但這裡有很多有用的東西
- EASI 食譜筆記本 -> 用於 ODC 風格分析的 CSIRO 地球分析平台介紹
- DS_UNet -> Unet 融合 Sentinel-1 合成孔徑雷達 (SAR) 與 Sentinel-2 多光譜成像儀
- 多藉口屏蔽自動編碼器 (MP-MAE)
- 數據
- 分段地理空間 -> 對地理空間用途的任何內容進行分段
- SamGIS -> 對應用於 GIS 的任何內容進行分段
- SatMAE++ -> 重新思考多光譜衛星影像的 Transformers 預訓練
- grid-mae -> 研究在 Vision Transformer Masked Autoencoder 中使用多尺度網格
- 尺度梅
- CIMAE -> CIMAE - 通道獨立屏蔽自動編碼器
- fork -> 為其指定名稱以供參考
- [遙感自監督表示學習] -> 碩士論文包括以上內容以及幾種模型的比較
- U穀倉
- 地球網
- GeoTorchAI -> GeoTorchAI：時空深度學習框架
- [pytorcheo](https://github.com/earthpulse/pytorchEO -> 深度學習對地觀測應用與研究
- AiTLAS -> 一個開源基準套件，用於評估地球觀測中影像分類的最先進深度學習方法
- Segmentation Gym -> Gym 被設計為“一站式商店”，用於“ND”上的影像分割 - 多光譜影像中任意數量的重合波段
- 深度學習改變區
- 很棒的採礦帶比集合 -> 簡單帶比集合用於突出顯示各種礦物
- 很棒的遙感基礎模型
- Clay -> 地球的開源人工智慧模型與介面
- IBM-NASA-地理空間 Prithvi
- 透過基礎模型微調進行影像分割 -> For Prithvi
- AM-RADIO：凝聚視覺基礎模型
- RemoteCLIP -> 遙感視覺語言基礎模型
- 光譜GPT
- zenodo) -> 針對光譜資料客製化的遙感基礎模型
加工
- ASTER 轉換 -> 從 ASTER hd5 到 geotiff NASA github 的轉換
- HLS 資料資源 -> 協調 Landsat Sentinel 爭論
- sarsen -> 基於xarray的SAR影像處理和校正
- openEO -> openEO 開發開放 API 將 R、Python、JavaScript 和其他客戶端連接到 EO 雲端後端
光譜分解
- 2024 年高光譜影像分類從傳統到變壓器的調查
- 高光譜深度學習評論
- 高光譜自動編碼器
- 深度學習恆指
- 3DCAE-高光譜-分類
- 脫氫IC
- 修訂網
- 論文 -> 具有光譜變異性的高光譜分解的可逆生成網絡
- Pysptools -> 也有有用的啟發式演算法
- 光譜蟒蛇
- 光譜資料集 RockSL -> 開啟光譜資料集
- 分離
高光譜
- CasFormer：用於融合感知計算高光譜成像的級聯變壓器
- Keras 的光譜歸一化
- S^2HM^2 -> S2HM2：用於大規模高光譜影像自監督特徵學習和分類的頻譜空間分層掩模建模框架
視覺化
- 從視覺化中提取深層色彩圖
- 用於從地形圖中提取歷史露天採礦擾動的語意分割 -> 以煤礦為例
- 國際年代地層顏色代碼 -> RGB 代碼以及電子表格和其他格式的其他代碼
- LithClass -> USGS 版本的岩性顏色代碼
- 彩色版
- SeisWiz -> 輕量級 python SEG-Y 檢視器
質地
- 使用深度卷積神經網路進行礦物結構分類：斑岩銅礦床鋯石的應用
模擬
幾何學
- Deep Angle -> 使用深度學習快速計算斷層掃描影像中的接觸角
其他
- 礦物學系統的網絡分析
- 地理分析和機器學習
- 機器學習地下
- 機器學習地球科學
- 成為地球科學偵探
- Earth ML -> PyData 方法的一些基本教學
- GeoMLA -> 用於空間和時空資料的機器學習演算法
平台
指南
- 地理空間 CLI - 地理空間命令列工具列表
- 衛星影像深度學習
- 地球觀測
- 地球人工智慧
- 開源 GIS -> 生態系統全面概述
數據品質
- 機器學習的地球科學資料品質 -> 機器學習的地球科學資料質量
- 澳洲重力資料 -> 重力站資料概述與分析
- Geodiff -> 向量資料比較
- Redflag -> 資料分析與偵測問題的概述
機器學習
- Dask-ml -> 一些常見 ML 演算法的分散式版本
- geospatial-rf -> 協助空間上下文中的隨機森林應用的函數和包裝器
- Geospatial-ml -> 一次安裝多個通用包
潛在空間
- 嵌套融合
- 論文 -> 嵌套融合：M2020 PIXL RGBU 和 XRF 資料的多尺度嵌套資料的降維和潛在結構分析
指標
- 分數 -> 使用 xarray 驗證和評估模型和預測
機率論
- NG Boost -> 機率回歸
- 機率機器學習
- 使用 BO 進行 Bagging PU -> 使用貝葉斯優化進行正向無標記 Bagging
聚類
自組織映射
- GisSOM -> 來自芬蘭地質調查局的以地理空間為中心的自組織地圖
- SimpSOM -> 自組織映射
其他
貝葉斯
可解釋性
- InterpretML -> 解釋表格資料模型
- InterpretML -> 社群添加
深度學習
- 深度色彩圖擷取 -> 嘗試從圖片中擷取資料比例
- 從地球科學文件中提取圖像並對其進行分類
數據
- Xbatcher -> 基於 Xarray 的深度學習資料讀取
- 使用 Zarr 和 Xarray 用於機器學習的雲端原生資料載入器
- zen3geo -> 使用 pytorch 的 Xbatcher 風格資料科學
可解釋性
- 形狀值
- 慧儷輕體 -> 分析網路的訓練狀況
- 體重觀察者.ai
- Weightwatcher-ai.com -> 專業網頁版
自我監督學習
- 自監督 -> 多種演算法的 Pytorch 閃電實現
- 西姆克勒
- 很棒的自我監督學習 -> 精選列表
超參數
編碼環境
社群
- Software Underground - 有興趣探索地下和代碼交集的人們的社區
- 聊天註冊 - SWUNG 社區聊天註冊
- Mattermost-社區聊天服務
- 舊的 Slack Channel（已棄用，請參閱上面的 Mattermost）
- 地球科學開源搭配
- 影片
- 很棒的開放地球科學[注意石油和天然氣偏見]
- Transform 2021 黑客範例
- Segysak 2021 教程
- T21 地震筆記本
- 使用 Python 進行實用地震
- 2021 年辛佩格轉型
- 潘吉奧
- 澳洲數位地球
- 開源地理空間基金會
- OSGeoLive -> 可啟動 DVD/USB，附有大量開源地理空間軟體
- ASEG -> 來自澳洲勘探地球科學家協會的視頻
- 用於地質建模和繪圖的人工智慧 -> 會議當天的視頻
- 會議
雲端提供者
AWS
- ec2 Spot 實驗室 -> 讓 Spot 實例的自動工作變得更容易
- Sagemaker 地理空間機器學習
- Sagemaker -> ML 託管服務
批次
- Shepard -> AWS Batch Pipelines 的自動雲形成設定：這很棒
套餐
一般的
- 深度學習容器
- Loguru -> 日誌庫
- AWS GDAL Robot -> Lambda 和 geotiff 的批次
- 無伺服器地震處理
- LIhops -> 多雲分散式運算框架
概述
網域
網路服務
如果列出，則假定它們通常是數據，如果只是像 WMS 這樣的圖片，它會這樣說。
世界
澳洲
- 澳洲GIN
- 澳洲地球科學
- 礦產潛力 -> WMS
- 澳洲地球科學目錄服務
地質學
- AUSLAMP - > Tennant Creek - MtIsa
- 野外地質學
- 深層岩石圈 -> 深層岩石圈礦產潛力
- 地質年代學 -> 地質年代學
- 地質省份
- WMS -> WMS圖片
- EGGS -> 地質和地球物理表面的估計
- 元古代鹼性岩石 - 元古代鹼性岩石資料集 WFS {還有 WMS}
- 新生代
- 中生代
- 古生代
- 太古代
- 地層學 -> 地層單位
地球物理學
- 地球物理調查
- 地震勘測 -> 陸上地震勘測
- 大地電磁 -> 澳洲北部 AUSLAMP 站
其他
- Ni-Cu-PEGE -> 侵入型鎳銅 PGE 礦床
- EFTF 領域 -> 探索未來領域
- 溫度 -> 解釋溫度
- DEA -> 澳洲數位地球
- 土地覆蓋
- 水體
- BOM -> 氣象局水文地球化學
新南威爾斯州
- 新南威爾斯州
- 世界CS
- WFS 礦物鑽孔
- WFS 石油鑽井
- WFS 煤炭鑽孔
- 地震 -> 地震及其他
昆士蘭州
- 昆士蘭州
- 地球科學 -> 地球物理學和報告索引
- 地質學
- 區域性
- 狀態
- 唐大樓
- 道路
- 河道
南澳大利亞
- 薩里格
- 鑽孔
- 地質學
- 地球物理學
- 前景
- 礦產和礦山
- 遙感
- 地震
- 唐大樓
北領地
塔斯馬尼亞
維多利亞
西澳大利亞州
紐西蘭
南美洲
巴西
秘魯
墨西哥
阿根廷
哥倫比亞
烏拉圭
其他
歐洲
EGDI -> EGDI 礦物
瑞典
芬蘭
- GTK -> 芬蘭地質調查局
- 北極礦物 -> 北極 1M 礦點
丹麥
葡萄牙
西班牙
- 西班牙
- 地質 -> 200K
- 1M -> 1M
- 50K -> 50K
- IGME晶洞
- 地球物理學
- 銅 - 銅
- GeoFPI - > 地質與礦產 南葡萄牙區
- 水
烏克蘭
愛爾蘭
英國
- BGS -> 英國地質調查局
- 地理索引 -> 礦物產狀範例
- 休息 -> BGS 休息服務和 Inspire 625
德國
捷克共和國
斯洛伐克
匈牙利
羅馬尼亞
- IGR -> 僅 WMS
- IGR 分鐘 -> 僅 WMS
波蘭
北美洲
加拿大
美國
- 美國地質勘探局世界礦產
- 美國地質勘探局MRDS
- 明尼蘇達州
亞洲
- 中國 -> WMS 礦床 wap
- 礦田 -> 礦物賦存點
- 印度礦產 -> WMS
- 印度地球物理
非洲
- 非洲地理門戶 -> 休息服務
- 非洲 10M -> 非洲 10M 礦產情況 https://pubs.usgs.gov/of/2005/1294/e/OF05-1294-E.pdf
- IPIS 手工礦山 -> 也有 WMS 版本
- github
- 烏幹達 -> GMIS WMS
一般的
- 礦產勘探 Web 服務 -> QGIS 插件，可存取許多相關的 Web 服務
其他
蜜蜂
- 開放資料 API -> GSQ 開放資料入口網站 API
- 核心 -> 開放研究文本
- 分享 -> 開放科學 API
- 美國地質調查局出版物
- 交叉引用
- xDD -> 前 GeoDeepDive
- ADEPT -> GUI 到 xDD 搜尋 15M 收穫的論文
- 開放亞歷克斯
- 應用程式介面
- diophila Python 庫
- Python庫
- 宏觀策略
- OpenMinData -> 方便從 Mindat API 查詢和檢索礦物和地質材料的數據
數據入口網站
世界
- 地球模型協作 -> 存取各種地球模型、用於模型預覽的視覺化工具、提取模型資料/元資料的設施以及存取貢獻的處理軟體和腳本。
- ISC通報 -> 地震震源機制檢索
- [磁性資訊聯盟[(https://www2.earthref.org/MagIC/search) -> 古地磁、地磁、岩磁
澳洲
澳洲地球科學
- 澳洲地球科學資料目錄
- 澳洲AEM
- 澳洲地球科學入口網站
- 探索未來入口網站 -> 澳洲地球科學入口網站提供下載信息
- 澳洲AEM
- 澳洲燈
- 地質年代學和同位素
- 水文地質 流域 -> 搜尋流域層
- 關鍵礦物測繪計劃
- 澳洲地層單位
- 澳洲鑽孔地層單位 -> 沉積單位地下水的編制
- 澳洲地球科學地球物理學線程 -> OpendDAP 和 https 訪問
- MORPH gdb -> 馬斯格雷夫官員鑽井數據
聯邦科學與工業研究組織
- CSIRO 資料存取門戶
- 風化層深度
- TWI -> 地形濕度指數
- ASTER 地球科學地圖 -> 網站
- FTP -> CSIRO ftp 站點
- ASTER 地圖註釋 -> 上述註釋
奧斯科普
燕鷗
氣象局
基礎空間數據
南澳大利亞
- SARIG -> 南澳大利亞地質調查局基於地理空間地圖的搜索
- SARIG 目錄 -> 資料目錄
- 3D模型
- 資料包 - 年度更新
- s3 報告 -> s3 儲存桶中的報告和文字版本（帶有 Web 介面）
- 報告
- 地震
北領地
- 罷工 -> 北領地地質調查局
- 寶石管理資訊系統
- 麥克阿瑟盆地 -> 3D 模型
- 地球物理調查
- 地球物理學 -> 參考
- 鑽井和地球化學 -> 參考
昆士蘭州
西澳大利亞州
- GEOVIEW -> 西澳大利亞地質調查局
- DMIRS -> DMIRS 數據和軟體中心
- 下載 URLS -> 下載連結資料集
- 鑽井和地球化學
- 下載包 - 改進？
- 地球化學
- 有深度的石油井
- 資料WA子集
新南威爾斯州
- MINVIEW -> 新南威爾斯地質調查局
- DiGS -> 出版品和岩土工程收藏
塔斯馬尼亞
維多利亞
- 地球資源
- GeoVIC -> 網路地圖需要註冊才能更有用
紐西蘭
- 探索資料庫 -> 在線
- GERM -> 紐西蘭地質資源圖
- 地質 -> 網路地圖
- https://maps.gns.cri.nz/gns/wfs
南美洲
巴西
- CPRM -> 巴西地質調查局
- 下載 -> 巴西地質調查局下載
- Rigeo -> 地球科學機構知識庫
秘魯
- Ingemmet GeoPROMINE -> 秘魯地質調查局
- 地理MAPE
墨西哥
阿根廷
哥倫比亞
烏拉圭
智利
歐洲
- EGDI -> 歐洲地球科學
- 世界金融高峰會
- 普羅米因
- 啟發 -> 啟發地理門戶
丹麥
芬蘭
- 礦物質4EU
- GTK -> 芬蘭地質調查局
- 地球化學圖 -> 僅 pdf！
瑞典
西班牙
葡萄牙
愛爾蘭
- GSI -> 愛爾蘭地質調查局
- GSI - 地圖檢視器
- 金礦 -> 地圖及文獻檢索
- data.gov.ie -> 國家入口網站視圖
- isde -> 愛爾蘭空間資料交換
挪威
- NGU -> 挪威地質調查局
- 資料庫 -> 礦產資源和地層查找
- github
- 應用程式介面
- Geoporta -> 地球物理學
- GEONORGE -> 帶下載的資料目錄
英國
烏克蘭
俄羅斯
- 俄羅斯地質研究所 -> 目前無法訪問
- RGU -> 存款GIS項目
德國
- 地理門戶
- 地理地圖 -> M
- Atom -> Atom 資料來源
- GDI -> 德國 3D 模型
法國
克羅埃西亞
捷克共和國
斯洛維尼亞
斯洛伐克
匈牙利
羅馬尼亞
波蘭
英國
- 英國陸上地球物理圖書館
- 作業系統資料中心英國地質學
- 地質學625
北美洲
加拿大
- 加拿大自然資源部
- github
- 地球科學資料儲存庫 -> DAP 伺服器
- 礦業網路地圖門戶
- DEM -> COG 格式的加拿大 DEM
- CDEM -> 數位高程模型 (2011)
- 安大略省
- 魁北克
- SIGEOM資料庫
- 不列顛哥倫比亞省
- 礦物賦存資料庫
- 育空地區
- 新斯科細亞省
- 省
- 愛德華王子島
- 薩斯喀徹溫省
- 礦產賦存資料庫
- 紐芬蘭 -> 在 Chrome 中不起作用，在 Edge 中嘗試過
- 亞伯達省
- 互動式地圖應用
- 西北地區
- 礦權
美國
- 美國地質勘探局 -> 地圖資料庫
- MRDS -> 礦產資源資料系統
- 地球探索者 -> USGS 遙感資料門戶
- 國家地圖資料庫
- 國家地圖資料庫
- 阿拉斯加州
- ReSci -> 國家地質與地球物理資料保存計劃科學館藏登記
- 密西根州
非洲
- 地籍
- 水文地質學 -> 地下水圖集的水文地質學和地質學
- 西非 -> 礦藏
- 納米比亞
- 礦物產狀
- 礦工
- 南非 -> 南非地質調查局
- 礦物產狀 -> 需要登入才能下載的範例
- 烏幹達 -> GMIS 門戶
- 金屬礦物
- 坦尚尼亞
- 礦物產狀
- 地雷
- SIGM -> 突尼斯地質與採礦
- 尚比亞 -> 尚比亞房產這裡
亞洲
中國
印度
- 布科甚 -> 印度地質調查局
- 注意拉賈斯坦邦的地質學除了零碎的工作之外不起作用，這是痛苦的 - 如果你想要它，請告訴我
沙烏地阿拉伯
其他
地質學
- 策略資料庫
- GEM全球活躍故障
- RRuff 礦物特性
- 文章 -> 礦物學演化系統
- 地質學
- 目錄
伊朗
地質學
一般的
- OSF -> 開放科學基金會
- 沉積物託管賤金屬 -> 沉積物託管賤金屬
- 岩石圈流圈邊界 -> LAB Hoggard/Czarnota
- 地質調查清單
報告
澳洲
- 北領地 GEMIS
- 南澳大利亞SARIG
- 西澳大利亞 WAMEX
- 昆士蘭州
- 新南威爾斯挖掘
- PorterGEO -> 世界礦藏資料庫及摘要概述
- 永續礦物研究所 -> 昆士蘭大學附屬研究人員組織製作資料集和知識
加拿大
- 不列顛哥倫比亞省
- 安大略省 -> 礦產評估報告
- 亞伯達省
- 育空地區
- 腳印
- 馬尼托巴省
- 刊物
- 紐芬蘭及拉布拉多
- 西北地區
- 新斯科細亞省
- 魁北克
- 薩斯喀徹溫省
美國
- 亞利桑那
- 蒙大拿
- 內華達州
- 新墨西哥州
- 明尼蘇達州
- 密西根州
- json
- 阿拉斯加州
- 華盛頓
其他
- 英國地質調查局NERC
- 礦產潛力
- 搜尋
- API範例
- 刊物
- MEIGA -> MEIGA 600 BGS 礦產勘探計畫報告
- GeoLagret -> 瑞典
- MinData -> 世界各地岩石位置的彙編
- 礦物資料庫 -> 可導出的具有科學性質和年齡的礦物列表
- 美國太空總署
- ResearchGate -> 研究員與專業網絡
工具
地理資訊系統
- QGIS -> GIS 資料視覺化和分析開源桌面應用程序，具有一些 ML 工具：對於快速輕鬆的檢視是必不可少的
- QGIS 中的 2D 地質學 -> QGIS NA 2020 研討會為學生和愛好者介紹地質圖和橫斷面
- OpenLog -> 鑽孔插件測試版
- Geo-SAM -> QGIS 插件，用於使用柵格分割任何內容
- 證據權重
- 外掛
- 草
- 傳奇 -> Sourceforge 的鏡像
3D
地理空間綜合
- 地球科學的 Python 資源
- geoutils -> 地理空間分析並促進其他 Python GIS 套件之間的互通性。
向量數據
Python
- 大貓熊
- 達斯克大熊貓
- geofileops -> 透過資料庫函數和 geopackage 提高空間連接速度
- Kart -> 資料的分散式版本控制
- PyESRIDump -> 用於從 ESRI Rest 伺服器大規模取得資料的庫
右
- 順豐
- terra -> terra 提供了以「柵格」和「向量」形式操作地理（空間）資料的方法。
柵格數據
C
- excextract -> C 中的命令列區域統計
茱莉亞
Python
- Rasterio -> 用於柵格資料處理的 python 基礎庫
- georeader -> 處理來自不同衛星任務的柵格數據
- Rasterstats -> 基於向量幾何總結地理空間柵格資料集
- Xarray -> 多維標記數組處理和分析
- Rioxarray -> 出色的高級 API，用於 xarray 處理柵格數據
- Geocube -> 向量資料 api 的光柵化
- ODC-GEO -> 用於基於遙感的柵格處理的工具，具有許多非常方便的工具，例如著色、網格工作流程
- COG Validator -> 檢查雲優化的 geotiff 的格式
- serverless-datacube-demo -> xarray 透過生石花 / 卷繞 / 模態
- Xarray Spatial -> 柵格資料的統計分析，例如自然斷裂等分類
- xdggs -> 其他類型的網格
- xgcm -> 標籤的直方圖
- xrft -> 基於 Xarray 的傅立葉變換
- xvec -> Xarray 的向量資料立方體
- xarray-einstats -> xarray 的統計、線性代數和 einops
右
- 柵格 -> R 庫
- terra -> 提供以「柵格」和「向量」形式操作地理（空間）資料的方法。
- 星星 -> 時空數組：柵格和向量資料立方體
- extracr -> R 的柵格區域統計
基準測試
- raster-benchmark -> 對 python 和 R 中的一些光柵庫進行基準測試
桂
- Whitebox Tools -> python api、gui等已用於地形濕度指數計算
數據收集
- PiAutoStage ->“用於自動收集高解析度顯微鏡圖像的開源 3D 列印工具；”專為礦物樣品而設計。
資料轉換
- AEM 到 seg-y
- ASEG GDF2
- CGG 輸出文件閱讀器
- Geosoft 網格到柵格
- 循環地理軟體網格
- Harmonica Geosoft Grid -> 正在轉換為 xarray 時拉取請求
- AuScope -> 來自二進位 GOCAD 模型的數據
- GOCAD SG 網格閱讀器
- geomodel-2-3dweb -> 在這裡，他們有一種從二進位 GOCAD SG 網格中提取資料的方法
- 跨越式網格閱讀器
- OMF -> 用於事物之間轉換的開放挖掘格式
- PDF礦工
- VTK 轉 DXF
地球化學
- Pygeochemtools -> 庫和命令列，可實現地球化學數據的快速 QC 和繪圖
- SA 地球化學地圖 -> 來自 SA 地質調查局的南澳大利亞地球化學數據的數據分析和繪圖
- 地球化學平衡
- 史考特·哈雷的地球化學教程
- 元素週期表
地統計學
地質年代學
- 地質時標 -> 產生代碼，但也有一個很好的時代常規 csv
地質學
Gempy -> 隱式建模
Gemgis -> 地理空間資料分析幫助
LoopStructural -> 隱式建模
手冊 python geologia -> 地質資料分析
Map2Loop -> 3D 建模自動化
pybedforms
SA 地層學 -> 地層學資料庫編輯器 webapp
條形日誌
Analise_de_Dados_Estruturais_Altamira
全球構造 -> 用於建構的開源資料集、板塊、邊緣等。
澤諾多補充
岩性
pyGP板
教程數據
地球物理學
- 澳洲地球科學公用事業公司
- 實踐地球科學家的地球物理學
- 位勢場工具箱 -> 一些基於 xarray 的快速傅立葉變換濾波器 - 導數、偽重力、rpg 等。
- 筆記本 -> 帶有一些範例的類別 [垂直導數、偽重力、向上延續等）
- 計算地球物理沙箱
- RIS 基底沉積物 -> 南極洲磁基底深度
- 訊號影像處理
電磁
- 澳洲地球科學 AEM
- 呃電磁學 -> 了解該領域的課程筆記本
- AEM解讀
- EMag Py -> FDEM
- ResIPy -> DC / IP
重力和磁力
- 口琴
- 過濾器範例 -> 透過 xarray 基於快速傅立葉變換的處理
- 澳洲重力數據
- 蠕蟲
- 蠕蟲更新 <- 潛在領域蠕蟲創建，並進行一些小的更新以處理新的 networkx api *github 鏡像
- Osborne Magnetic -> 測量資料處理範例
地震
- 賽吉歐
- Segysak -> 基於 Xarray 的 seg-y 資料處理與分析
- 地球物理筆記->地震資料處理
大地電磁學
- 吡咯烷酮
- 教學
- MtPy -> 更新上述內容以使事情變得更容易
- Mineral Stats Toolkit -> 到 MT 特徵分析的距離
- 岩石圈導體紙
- mtwaffle -> MT 資料分析範例
- 吡啶甲基轉移酶
- 抵抗力
- MECMUS -> 讀取美國電導率模型的工具
- 模型
網格化
- 格林威治標準時間
- 維德角
- Grid_aeromag -> 巴西網格範例
- pyinterp -> 透過 Boost 進行多維網格化
- 偽重力 -> 來自布萊克利，95
反轉
- 類比PEG
- Mira Geoscience Fork -> 用於地理應用程序
- SimPEG叉子
- 轉變 2020 SimPEG
- 轉型 2021 SimPEG
- SimPEG 腳本
- 腹肌關節反轉範例
- 吉姆利
- Tomofast-x
- USGS 匿名 FTP
- USGS Software -> 更長的舊有用東西清單：dosbox，有人嗎？
- 地球物理子程式 -> Fortran 程式碼
- 2020亞琛反演問題 -> 重力反演理論概述
地球化學
- 黃鐵礦
- 調平
- Pygeochem 工具
- 吉奧奎米卡
- 地球化學
鑽孔
- dh2loop -> 鑽井間隔輔助
- 鑽取 -> 透過 geoh5py 在筆記本中鑽取視覺化 -> 註釋去測量
- PyGSLib -> 井下測量和區間標準化
- pyborehole -> 處理和視覺化鑽孔數據
- dhcomp -> 將地球物理資料合成到一組間隔
遙感
- 很棒的光譜指數 -> 光譜指數創建指南
- 開啟數據立方體
- DEA Notebooks -> 用於 ODC 風格工作流程的程式碼
- Datacube-stats -> ODC的統計分析庫
- 地理筆記本 -> Element 84 中的程式碼範例
- Raster4ML -> 大量植被指數
- Lefa -> 斷裂分析、輪廓
無伺服器
- Kerchunk -> 透過 Zarr 對基於雲端的資料進行無伺服器訪問
- Kerchunk geoh5 -> 透過 kerchunk 無伺服器存取 Geoscient Analyst/geoh5 項目
- Icehunk -> 用於張量/ND 陣列資料的交易儲存引擎，設計用於雲端物件儲存。
斯塔克目錄
- DEA Stackstac -> 使用澳洲數位地球資料的範例
- 進氣斯塔克
- ML AOI 擴充
- ML 模型擴充規格 -> 用於編目時空模型的機器學習模型規格
- ODC-Stac -> 無資料庫開放資料立方體
- 派斯塔克
- 衛星搜尋
- Stackstac -> 元資料加速了 dask 和 xarray 時間序列
統計數據
- 橙色 -> 資料探勘 GUI
- Hdstats -> 幾何中位數的演算法基礎
- 高畫質中位數
視覺化
- 電視 -> 在終端機中查看衛星影像
- 標題
- 坐
- 赫斯達爾
- 星星
- 秘魯金礦開採特區
礦產潛力
- 鎳礦產潛力測繪 -> 基於 ESRI 的分析
- 前景線上工具
礦業經濟學
- Bluecap -> 莫納什大學評估礦山可行性的框架
- Zipfs 定律 -> 擬合礦物沉積分佈的曲線
- PyASX -> ASX 資料來源擷取
- 金屬價格 API -> 容器化微服務
視覺化
- Napari -> 多維影像檢視器
- Holoviews -> 大規模資料視覺化
- Graphviz -> 圖形繪製/查看幫助 Windows 安裝信息
- 空間kde
色彩圖
- CET 感知均勻色彩圖
- PU 顏色圖 -> 為 Geoscience Analyst 中的使用者格式化
- 色彩圖扭曲 -> 一個面板應用程序，用於演示地球物理數據上的非感知色彩圖造成的扭曲
- 從 Colormpas 提取數據
- 開放地球科學程式碼項目
地理空間
- Geospatial >-安裝多個常用的python包
- 地理空間 python -> 精選列表
技術堆疊
C
- GDAL -> 絕對關鍵的資料轉換與分析框架
- Tools -> Note 有很多命令列工具也非常有用
茱莉亞
- Julia Earth -> 促進地球科學中的地理空間資料科學和地統計建模
- Julia Geodynamics -> 計算地球動力學程式碼
- 茱莉亞地球科學簡介
Python-PyData
- Anaconda -> 使用這個套件管理器安裝很多東西。
- GDAL 等 -> 在這裡消除 GDAL 和 Tensorflow 安裝的痛苦
- Git Bash -> 讓 conda 在 Git Bash 中工作
- Numpy 多維數組
- Pandas 表格資料分析
- Matplotlib 視覺化
- Zarr -> 壓縮、分塊分散式數組
- Dask -> 平行、分散式計算
- Dask Cloud Provider -> 自動啟動雲端dask集群
- Dask Median -> 給出 Dask 中值函數原型的筆記本
- Python 地理空間生態系統 -> 精選訊息
Rust - GeoRust
- GeoRust -> Rust 中的地理空間實用程式集合
資料庫
- DuckDB -> 快速處理 OLAP DB - 具有一些地理空間和陣列功能
- 宜必思 + Duckdb 地理空間 -> scipy2024 談話
數據科學
- Python 資料科學範本 -> 專案包設定
- 很棒的 python 數據科學 -> 策劃指南
可能性
科學
- 用於地球科學的 Python 資源
- 令人敬畏的科學計算
碼頭工人
- AWS 深度學習容器
- 空間泊塢窗
- DL Docker 地理空間
- 搖桿
- Docker Lambda
- 地理資料庫
- DL Docker 地理空間
本體論
- 昆士蘭地質學會詞彙
- 西澳大利亞地質學會
- 地層學
- 地球科學知識經理
- GeoSciML 詞彙
圖書
Python
- Python 地理空間分析手冊
- 使用 Python 進行地理處理 -> Manning livebook
其他
- 教科書
- 石油和天然氣產業的機器學習
- 使用 R 進行地理計算
- Earthdata Cloud Cookbook -> 如何存取 NASA 資源
- Data Cleaner's Cookbook -> 充分利用 UNIX 工具進行資料整理與清理
- 數學地球科學百科全書
- 數學地球科學手冊
其他
- GXPy -> Geosoft Python API
- EarthArxiv -> 從預印本檔案下載論文
- Essoar -> 預印本論文檔案
數據集
世界
地質學
- 基岩 -> 世界廣義地質學
- GLIM -> 全球岩性圖
- 古地質學顯生宙古地理圖集
- 沉積層 -> 土壤、風化層和沈積層的全球 1 公里網格厚度
- 世界應力圖 -> 全球地殼現今應力場資訊彙編
- GMBA -> 全球登山盤點
地球物理學
重力
- 曲率 -> 根據重力梯度資料進行全域曲率分析
- 2012年全球大會
磁學
- EAMG2V3 _> 地磁異常網格
- WDMAM -> 世界數位磁異常地圖
大地電磁學
地震
- 實驗室 SLNAAFSA
- 實驗室CAM2016
- 莫霍面 -> GEMMA 數據
- 莫霍面 -> Szwillus 數據
- 地震速度 - > Debayle 等
- LithoRef18 -> 來自多個資料集聯合反演和分析的岩石圈和上地函全球參考模型
- CRUST1.0 -> 全球地殼模型 netcdf
- 概覽首頁
熱的
一般的
- 深時數位地球 -> 各種資料來源和模型的資料和視覺化
- EarthChem -> 由社區驅動的地球化學、地質年代學和岩石學數據的保存、發現、訪問和可視化
- GEOROC -> 岩石的地球化學成分
- 全球地質學 -> 以 GIS 格式（例如 shapefile）製作全球地質圖的簡短秘訣，其中年齡範圍映射到 GTS2020 時間尺度
- 大原產省委員會
- 地函羽
- 沉積物厚度 -> 地圖
- Spatialreference.org -> 網站儲存庫
澳洲
- 共同地球模型
- 重礦物地圖
- 澳洲重礦物試辦地圖
- 閃亮的應用程式
地球化學
- 澳洲大陸表面岩石和風化層主要氧化物濃度的預測網格 -> 各種氧化物
地質學
- 鹼性岩石圖集
- 新生代
- 中生代
- 古生代
- 太古代
- 搜尋
- 元古代鹼性岩 -> 澳洲 GIS 元古代鹼性及相關火成岩
- 新生代
- 中生代
- 古生代
- 太古代
- 論文 https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/147963
- 水文地質 -> 澳洲水文地質地圖
- 水文地質-> 5M
- 層狀地質 -> 1M
- 地表地質 -> 1M 規模
- 澳洲鎂鐵質-超鎂鐵質岩漿事件 GIS 資料集
地球物理學
磁學
- TMI -> 澳洲磁異常地圖，第七版，2019 年 TMI
- 40m -> 40m 版本
- VRTP -> 澳洲總磁強度 (TMI) 電網，可變極點 (VRTP) 2019
- 1VD -> 2019 年澳洲總磁強度網格 - 一階垂直導數 (1VD)
輻射測量
- 輻射測量 -> 澳洲完整輻射網格 (Radmap) v4 2019，帶建模填充
- K -> 澳洲輻射網格 (Radmap) v4 2019 過濾 pct 鉀網格
- U -> 澳洲輻射網格 (Radmap) v4 2019 過濾 ppm 鈾
- Th -> 澳洲輻射網格 (Radmap) v4 2019 過濾 ppm 釷
- Th/K -> 澳洲輻射網格 (Radmap) v4 2019 釷與鉀的比率
- 英國/英國 -> 澳洲輻射網格 (Radmap) v4 2019 鈾與鉀的比率
- U/Th -> 澳洲輻射網格 (Radmap) v4 2019 鈾與釷比率
- U 平方/Th -> 澳洲輻射網格 (Radmap) v4 2019 鈾與釷的平方比率
- 劑量率 -> 澳洲輻射網格 (Radmap) v4 2019 過濾地面劑量率
- 三元圖片 -> 澳洲輻射網格 (Radmap) v4 2019 - 三元影像（K、Th、U）
澳洲AEM
- AusAEM 1 -> AusAEM 第 1 年 NT/QLD 機載電磁測量； GA 層狀地球反演產品
- AusAEM 1 -> AusAEM 第 1 年 NT/QLD：TEMPEST® 機載電磁資料與 Em Flow® 電導率估算
- AusAEM 1 -> AusAEM1 解釋資料包
- AusAEM 2 -> AusAEM 02 WA/NT 2019-20 機載電磁測量
- AusAEM–WA -> AusAEM–WA，默奇森機載電磁測量區塊
- AusAEM–WA -> AusAEM-WA，西南奧爾巴尼機載電磁測量區塊
- AusAEM–WA -> AusAEM WA 2020-21，東部金礦區和東伊爾加恩空降
- AusAEM–WA -> AusAEM (WA) 2020-21，Earaheedy 和 Desert Strip
- AusAEM ERC -> AusAEM 東部資源走廊
- AusAEM WRC -> AusAEM 西部資源走廊
- 插值概述
- 國家表面和近地表電導率網格 -> AusEM 的國家 ML 插值，與北澳大利亞類似
澳洲燈
- AusLAMP SEA -> 來自 AusLAMP 大地電磁資料的澳洲東南部大陸電阻率模型
- 維多利亞數據
- 新南威爾斯數據
- AusLAMP TISA -> 來自大地電磁學的電阻率模型：AusLAMP-TISA 項目
- AusLAMP Delamerian -> 來自 AusLAMP 大地電磁資料的 Delamerian 造山帶岩石圈電阻模型
- 澳洲燈塔公司
- 澳洲燈高勒
- AusLAMP 站 -> 2017 年左右
- 塔斯馬尼德紙業
莫霍面
礦藏
- 澳洲主要礦藏的地質環境、年齡和禀賦
- 1799 年至 2021 年澳洲礦山生產綜合資料集
礦產潛力
- 概述 - 澳洲地球科學 -> 出版品和資料集概述
- 沉積物託管鋅
- 報告
- 沉積物託管銅
- 報告
- 抽象的
- 碳酸岩稀土元素
礦山廢料
原住民產權
遙感
- Landsat Bare Earth - 來自 Landsat 的裸地中位數
- 用於土壤和岩性建模的增強型裸地 Landsat 圖像：資料集 -> 增強的詳細信息
- 根據高解析度衛星圖像繪製的全球採礦足跡 ** 論文
- DEM -> 澳洲1秒SRTM各品種DEM
結構
速度
- AU Tomo -> 來自同步和非同步環境雜訊成像的澳洲地殼的下一代速度模型
地形
- 多尺度地形位置 - RGB
- 資訊
- 地形濕度指數 - 1 角秒和 3 角秒
- 資訊
- 地形位置指數 - 1 和 3 角秒
- 資訊
- 風化強度模型
- 資訊
- {資訊](https://researchdata.edu.au/weathering-intensity-model-australia/1361069)
北方
- 覆蓋層厚度 TISA -> 使用內插網格的 Tennant Creek Mt Isa 覆蓋層厚度點
- 使用區域 AEM 調查和機器學習進行高解析度電導率繪圖 -> AusAEM 的 ML 電導率插值
- 擴展摘要
- 固體地質學 -> 北澳大利亞克拉通的固體地質學
- 反演模型 -> 北澳大利亞克拉通 3D 重力與磁力反演模型
- Ni-Cu-PGE -> 澳洲侵入型 Ni-Cu-PGE 硫化物礦床的潛力：礦物系統前景的大陸尺度分析
- TISA IOCG -> Tennant Creek – Mt Isa 地區氧化鐵銅金 (IOCG) 礦產潛力評估：地理空間數據
- TISA 蝕變 -> 使用 3D 重力和磁力反演產生磁鐵礦和赤鐵礦蝕變代理
南澳大利亞
地質學
- 基岩地質
- 水晶地下室 -> 水晶地下室相交鑽孔
- 礦山和礦藏
- 礦物鑽孔
- 固體地質學 3D
- 100K 故障
- 太古代
- 太古宙斷層
- 中元古代 -> 中
- 中元古代 -> 中斷層
- 中元古代 -> 晚期
- 中元古代斷層 -> 晚期斷層
- 新元古代
- 新元古代斷層
- 斯圖爾特陸棚沉積銅 3D 模型
- 地表地質
地球物理學
- AusLAMP 3D -> 大地電磁反演
- GCAS -> 高勒克拉通機載勘測
- 重力 -> 重力網格
- 站 -> 重力站
- 磁學 -> 磁學
- 地震線 -> 地震線
高勒
昆士蘭州
- 概述
- 昆士蘭深部採礦-> 昆士蘭深部採礦
- 礦床圖集 -> 西北礦省礦床圖集
- 地質學 -> 地質學系列概述
- 礦產與能源報告 -> 2011 年西北昆士蘭礦產與能源省報告 - NWQMEP
- 向量 -> 礦物地球化學向量
- 石油井
- 煤層氣井
- 鑽孔
克隆咖哩
北領地
- 阿倫塔 IOCG -> 阿倫塔南部地區的氧化鐵-銅-金潛力
- 南鈾 -> 北領地南部鈾和地熱系統評估數位資料包
- Tennant Creek -> 根據北領地東 Tennant 地區大地電磁資料所得的電導模型
新南威爾斯州
地質學
- 無縫地質學 -> 新南威爾斯州無縫地質學資料包（舊版本也在此頁面上）
礦產潛力資料包
西澳大利亞州
地球化學
地質學
- 100K 基岩
- 100K 表面地圖，您必須單獨下載並組合 - 它們不一致
- 250K 表面地圖，您必須單獨下載並組合 - 它們不一致
- 500K 基岩
- 廢棄礦井
- 礦物產狀
礦產潛力
前景
- 摩羯座-> 使用礦物系統方法進行前景分析 - 摩羯座案例研究項目
- 利奧波德國王 -> 利奧波德國王造山帶和倫納德陸架的礦產前景：西金伯利地區勢場資料分析
- 伊爾加恩金
- Yilgarn 2 -> Yilgarn 克拉通東部的預測礦物發現：造山金礦系統區域規模目標的範例
- [商店說明] -> WA 有一些可透過 USB 隨身碟購買的前景套餐，價格為 50-60AU 型 - 請參閱地理空間地圖部分
塔斯馬尼亞
地質學
維多利亞
紐西蘭
北美洲
- 國家規模的地球物理、地質和礦產資源數據和網格 -> 還有一些澳洲數據
- 地下水井 -> 資料庫
- 整個北美的最大水平應力方向和相對應力大小（斷層區）數據
加拿大
地質學
- 地圖
- 地質 -> 更新基岩地質圖
- 地質 -> 南雷伊和部分赫恩地區、邱吉爾省、西北地區、薩斯喀徹溫省、努勒維特、馬尼托巴省和阿爾伯塔省的基岩地質彙編和區域綜合
- 莫霍面 -> 國家莫霍面深度估計資料庫 透過地震折射和遠震測量進行估計
地球物理學
- Dap 搜尋 -> Geoportal 搜尋 - 請注意，這些位於 Geosoft 網格中 - 請參閱其他內容以了解轉換可能性
- [重力、磁力、輻射] -> 主要是國家範圍
歐洲
芬蘭
愛爾蘭
帶有程式碼的論文
自然語言處理
- https://www.sciencedirect.com/science/article/pii/S2590197422000064?via%3Dihub#bib20- -> 地球科學語言模型及其內在評估 -> 上面的 NRCan 代碼 [包括模型]
- https://www.researchgate.net/publication/334507958_Word_embeddings_for_application_in_geosciences_development_evaluation_and_examples_of_soil-lated_concepts -> GeoVec [包含模型]
- https://www.researchgate.net/publication/347902344_Portuguese_word_embeddings_for_the_oil_and_gas_industry_Development_and_evaluation -> PetroVec [包含模型]
- 用於自動搜尋和整理期刊增刊中的地球化學資料集的資源
地球化學
- https://www.researchgate.net/publication/365758387_A_resource_for_automated_search_and_collation_of_geochemical_datasets_from_journal_supplements
- https://github.com/erinlmartin/figshare_geoscrape?s=09
地質學
- https://github.com/sydney-machine-learning/autoencoders_remotesensing -> 用於岩性測繪的堆疊式自動編碼器
礦物
- https://www.researchgate.net/publication/318839364_Network_analysis_of_mineraological_systems
具有特徵資料的論文
礦產前景
- https://www.sciencedirect.com/science/article/pii/S016913682100010X#s0135 -> 加拿大岩漿鎳（±Cu±Co±PGE）硫化物礦物系統的遠景模擬[非常值得一讀]
- https://www.sciencedirect.com/science/article/pii/S0169136821006612#b0510 -> 沉積物中的鋅鉛礦物系統及其關鍵原料的資料驅動前景建模[非常值得一讀]
- https://www.researchgate.net/publication/358956673_Towards_a_complete_data-driven_prospectivity_mapping_methodology_A_case_study_of_the_Southeastern_Churchill_Province_Quebec_and_Labrador
英格蘭
- https://www.researchgate.net/publication/358083076_Machine_learning_for_geochemical_exploration_classifying_metalogenic_fertility_in_arc_magmas_and_insights_into_porphyry_copper_deposit_formation
地球化學
- https://www.researchgate.net/publication/361076789_Automated_machine_learning_pipeline_for_geochemical_analysis
地質學
- https://eprints.utas.edu.au/32368/ -> 岩性和交代作用的機器輔助建模
地球物理學
- https://github.com/TomasNaprstek/AeroMagnetic_CNN - 航空磁力CNN
- 論文 https://www.researchgate.net/publication/354772176_Convolution_Neural_Networks_Applied_to_the_Interpretation_of_Lineaments_in_Aeromagnet_Data
- 博士 -> 航磁數據線形內插與解釋的新方法
- 論文 https://www.researchgate.net/publication/354772176_Convolution_Neural_Networks_Applied_to_the_Interpretation_of_Lineaments_in_AeroMagnetic_Data -> 卷積神經網路應用於航磁資料中線形的解釋
地理空間輸出 - 無程式碼
- https://geoscience.data.qld.gov.au/report/cr113697 -> NWMP 資料驅動的礦產勘探和地質測繪 [CSIRO 也是]
期刊
- https://www.sciencedirect.com/journal/artificial-intelligence-in-geosciences -> 地球科學中的人工智慧
文件
- 通常不是機器學習，或沒有代碼/數據，有時根本不可用
- 最終將分為有資料包或沒有資料包的事物，如新南威爾斯州區域研究。
- 但是，如果對某個區域感興趣，您通常可以對圖片進行地理配準（如果沒有其他粗略指南）。
- 一般來說，這些都是不可重複的——新南威爾斯州前景區研究和 NWQMP 等一些研究正在進行一些工作。
- 本節偶爾出現的論文可能會在上面列出
新文件
一般的
- https://www.researchgate.net/publication/337650865_A_combinative_knowledge-driven_integration_method_for_integrating_geophysical_layers_with_geological_and_geochemical_datasets
- https://link.springer.com/article/10.1007/s11053-023-10237-w - 新一代礦物前景圖繪製人工智慧演算法
- https://www.researchgate.net/publication/235443297_Addressing_challenges_with_exploration_datasets_to_generate_usable_mineral_pottial_maps
- https://www.researchgate.net/publication/330077321_An_Improved_Data-Driven_Multiple_Criteria_Decision-Making_Procedure_for_Spatial_Modeling_of_Mineral_Prospectivity_Adaption_of_Prediction-AterPlot_FunctionPlot_Functionand.
- 用於礦產勘探的人工智慧：數據科學未來方向的回顧和展望 -> https://www.sciencedirect.com/science/article/pii/S0012825224002691
- https://www.researchgate.net/project/Bayesian-Machine-Learning-for-Geological-Modeling-and-Geophysical-Segmentation
- https://www.researchgate.net/publication/229714681_Classifiers_for_Modeling_of_Mineral_Potential
- https://www.researchgate.net/publication/352251078_Data_Analysis_Methods_for_Prospectivity_Modelling_as_applied_to_Mineral_Exploration_Targeting_State-of-the-Art_and_Outlook
- https://www.researchgate.net/publication/267927728_Data-Driven_Evidential_Belief_Modeling_of_Mineral_Potential_Using_Few_Prospects_and_Evidence_with_Missing_Values
- https://www.linkedin.com/pulse/deep-learning-meets-downward-continuation-caldera-analytics/?trackingId=Ybkv3ukNI7ygH3irCHZdGw%3D%3D
- https://www.researchgate.net/publication/382560010_DINOv2_Rocks_Geological_Image_Analysis_Classification_Segmentation_and_Interpretability
- https://www.researchgate.net/publication/368489689_Discrimination_of_Pb-Zn_deposit_types_using_sphalerite_geochemistry_New_insights_from_machine_learning_algorithm
- https://link.springer.com/article/10.1007/s11430-024-1309-9 -> 用於礦產前景測繪的可解釋人工智慧模型
- https://www.researchgate.net/publication/229792860_From_Predictive_Mapping_of_Mineral_Prospectivity_to_Quantitative_Estimation_of_Number_of_Undiscovered_Prospects
- https://www.researchgate.net/publication/339997675_Fully_reversible_neural_networks_for_large-scale_surface_and_sub-surface_charging_via_remote_sensing
- https://www.researchgate.net/publication/220164488_Geocomputation_of_mineral_exploration_targets
- https://www.researchgate.net/publication/272494576_Geological_knowledge_discovery_and_minerals_targeting_from_regolith_using_a_machine_learning_approach
- https://www.researchgate.net/publication/280013864_Geometric_average_of_spatial_evidence_data_layers_A_GIS-based_multi-criteria_decision-making_approach_to_mineral_prospectivity_mapping
- https://www.researchgate.net/publication/355467413_Harnessing_the_Power_of_Artificial_Intelligence_and_Machine_Learning_in_Mineral_Exploration-Opportunities_and_Cautionary_Notes
- https://www.researchgate.net/publication/335819474_Importance_of_spatial_predictor_variable_selection_in_machine_learning_applications_-Moving_from_data_reproduct_to_spatial_prediction
- https://www.researchgate.net/publication/337003268_Improved_supervised_classification_of_bedrock_in_areas_of_transported_overburden_Applying_domain_expertise_at_Kerkasha_Eritrea - Gazley/Hood
- https://www.researchgate.net/publication/360660467_Lithospheric_conductors_reveal_source_regions_of_convergent_margin_mineral_systems
- https://api.research-repository.uwa.edu.au/portalfiles/portal/5263287/Lysytsyn_Volodymyr_2015.pdf（博士論文）基於 GIS 的澳洲 Mt Isa Inlier 淺成熱液銅遠景圖：對勘探目標的影響
- https://www.researchgate.net/publication/374972769_Knowledge_and_technology_transfer_in_and_beyond_mineral_exploration -> 礦產勘探內外的知識和技術轉讓
- https://www.researchgate.net/publication/331946100_Machine_learning_for_data-driven_discovery_in_solid_Earth_geoscience
- https://theses.hal.science/tel-04107211/document - 地下地質非均質源的機器學習方法
- https://www.researchgate.net/publication/309715081_Magmato-Hydrotherm_space_A_new_metric_for_geochemical_characterization_of_metalic_ore_deposits - 岩漿熱液空間：金屬礦床地球化學特性的新指標
- https://www.researchgate.net/publication/220164234_Mapping_complexity_of_spatial_distribution_of_faults_using_fractal_and_multifractal_models_Vectoring_towards_exploration_targets
- https://www.researchgate.net/publication/220163838_Objective_selection_of_suitable_unit_cell_size_in_data-driven_modeling_of_mineral_prospectivity
- https://www.researchgate.net/publication/273500012_Prediction-area_P-A_plot_and_C-A_fractal_analysis_to_classify_and_evaluate_evidential_maps_for_mineral_prospectivity_modeling
- https://www.researchgate.net/publication/354925136_Soil-sample_geochemistry_normalized_by_class_membership_from_machine-learnt_clusters_of_satellite_and_geophysicals_data [Gazley/Hood]
- https://link.springer.com/article/10.1007/s12665-024-11870-1 -> 依賴人類感官參與的地球科學地圖的不確定性量化
- https://www.researchgate.net/publication/235443294_The_effect_of_map-scale_on_geological_complexity
- https://www.researchgate.net/publication/235443305_The_effect_of_map_scale_on_geological_complexity_for_computer-aided_exploration_targeting
- https://link.springer.com/article/10.1007/s11053-024-10322-8 -> 資料驅動的礦物前景測繪中工作流程引起的不確定性
礦產前景
澳洲
- https://www.mdpi.com/2072-4292/15/16/4074 -> 空間資料驅動的礦物前景圖繪製方法
- https://www.researchgate.net/publication/353253570_A_Truly_Spatial_Random_Forests_Algorithm_for_Geoscience_Data_Analysis_and_Modelling
- https://www.researchgate.net/publication/253217016_Advanced_methodologies_for_the_analysis_of_databases_of_mineral_deposits_and_major_faults
- https://www.researchgate.net/publication/362260616_Assessing_the_impact_of_conceptual_mineral_systems_uncertainty_on_prospectivity_predictions
- https://www.researchgate.net/publication/352310314_Central_Lachlan_Mineral_Potential_Study
- https://meg.resourcesregulator.nsw.gov.au/sites/default/files/2024-05/EITH%202024%20Muller_Exploration_in_the_House_keynote.pdf -> 關鍵礦物 – 使用生成式 AI 繪製前景圖
- https://www.tandfonline.com/doi/pdf/10.1080/22020586.2019.12073159?needAccess=true - > 將礦物系統方法與機器學習結合：Mt Woods Inlier 北部的「現代礦物探勘」案例研究南澳高勒克拉通
- https://www.researchgate.net/publication/365697240_Mineral_pottial_modelling_of_orogenic_gold_systems_in_the_Granites-Tanami_Orogen_Northern_Territory_Australia_A_multi-technique_approach
- https://publications.csiro.au/publications/publication/PIcsiro:EP2022-0483 -> 昆士蘭州伊薩山省東部主要礦物系統的特徵：資料分析的新視角
- https://link.springer.com/article/10.1007/s11004-021-09989-z -> 礦產勘探目標的隨機建模
- https://www.researchgate.net/publication/276171631_Supervised_Neural_Network_Targeting_and_Classification_Analysis_of_Airborne_EM_Magnetic_and_Gamma-ray_Spectrometry_Data_for_Mineral_Exploration
- https://www.researchgate.net/publication/353058758_Using_Machine_Learning_to_Map_Western_Australian_Landscapes_for_Mineral_Exploration
- https://www.researchgate.net/publication/264535019_Weights-of-evidence_and_logistic_regression_modeling_of_magmatic_nickel_sulfide_prospectivity_in_the_Yilgarn_Craton_Western_Australia
阿根廷
- https://www.researchgate.net/publication/263542691_ANALYSIS_OF_SPATIAL_DISTRIBUTION_OF_EPITHERMAL_GOLD_DEPOSITS_IN_THE_DESEADO_MASSIF_SANTA_CRUZ_PROVINCE
- https://www.researchgate.net/publication/263542560_EVIDENTIAL_BELIEF_MAPPING_OF_EPITHERMAL_GOLD_POTENTIAL_IN_THE_DESEADO_MASSIF_SANTA_CRUZ_PROVINCE_ARGENTINA
- https://www.researchgate.net/publication/277940917_Porphyry_epi Thermal_and_orogenic_gold_prospectivity_of_Argentina
- https://www.researchgate.net/publication/269518805_Prospectivity_for_epi Thermal_gold-silver_deposits_in_the_Deseado_Massif_Argentina
- https://www.researchgate.net/publication/235443303_Prospectivity_mapping_for_multi-stage_epi Thermal_gold_mineralization_in_Argentina
巴西
- https://www.researchgate.net/publication/367245252_Geochemical_multifractal_modeling_of_soil_and_stream_sediment_data_applied_to_gold_prospectivity_mapping_of_the_Pitangui_Greenstone_Belt_northwest_Belt
- https://www.researchgate.net/publication/381880769_How_do_non-deposit_sites_influence_the_performance_of_machine_learning-based_gold_prospectivity_mapping_A_study_case_in_the_Pitangui_Greenstone_Belt_Brazil
- https://www.researchsquare.com/article/rs-5066453/v1->在巴西東北部博爾伯雷瑪省增強鋰探索：整合機載地球物理學，低密度地球化學和機器學習演算法
- https://www.researchgate.net/publication/362263694_Machine_Learning_Methods_for_Quantifying_Uncertainty_in_Prospectivity_Mapping_of_Magmatic-Hydrothermal_Gold_Deposits_A_Case_Study_from_Juruena_Mineral_Province_Northern_Mato_Grosso_Brazil
- https://www.researchgate.net/publication/360055592_Predicting_mineralization_and_targeting_exploration_criteria_based_on_machine-learning_in_the_Serra_de_Jacobina_quartz-pebble-metaconglomerate_Au-U_deposits_Sao_Francisco_Craton_Brazil
模糊
- https://www.researchgate.net/publication/272170968_A_Comparative_Analysis_of_Weights_of_Evidence_Evidential_Belief_Functions_and_Fuzzy_Logic_for_Mineral_Potential_Mapping_Using_Incomplete_Data_at_the_Scale_of_Investigation A Comparative Analysis of Weights of Evidence, Evidential Belief Functions, and Fuzzy Logic for Mineral Potential Mapping Using Incomplete Data at the Scale of Investigation
- https://www.researchgate.net/publication/360386350_Application_of_Fuzzy_Gamma_Operator_to_Generate_Mineral_Prospectivity_Mapping_for_Cu-Mo_Porphyry_Deposits_Case_Study_Kighal-Bourmolk_Area_Northwestern_Iran
- https://www.researchgate.net/publication/348823482_Combining_fuzzy_analytic_hierarchy_process_with_concentration-area_fractal_for_mineral_prospectivity_mapping_A_case_study_involving_Qinling_orogenic_belt_in_central_China
- https://tupa.gtk.fi/raportti/arkisto/m60_2003_1.pdf-> kuusamo區域的概念模糊邏輯前景分析
- https://www.researchgate.net/publication/356508827_geophysical-spatial_data_modeling_modeling_fuzzy_fuzzy_logic_papplied_to_to_nova_to_nova_aurora_aurora_aurora_astate_to_to_to_nova_to_nova_aurora_aurora_aurora_astate_astate_wuri_uri; _state_state_state_state_state_state_state_state_state_stateserterneastertate_tatate_state_outhersertertern
- https://www.researchgate.net/publication/356937528_Mineral_prospectivity_mapping_a_potential_technique_for_sustainable_mineral_exploration_and_mining_activities_-_a_case_study_using_the_copper_deposits_of_the_Tagmout_basin_Morocco
加拿大
- http://www.geosciencebc.com/i/pdf/summaryofactivities2015/soa2015_granek.pdf->高階的地球科學透過專注的機器學習應用於Quest Project Project Project Project Project Dataset，British Columbia，
- https://open.library.ubc.ca/soa/circle/collections/collections/ubctheses/24/items/1.0340340->機器學習演算法應用於礦物的潛在目標映射
- https://www.researchgate.net/publication/369599705_A_study_of_faults_in_the_Superior_province_of_Ontario_and_Quebec_using_the_random_forest_machine_learning_algorithm_spatial_relationship_to_gold_mines
- https://www.researchgate.net/publication/273176257_data-_and_knowledge_drived_mineral_mineral_prospectivity_maps_maps_for_for_for_canada's_north
- https://www.researchgate.net/publication/300153215_data_mining_for_for_real_mining_a_robust_algorith_algorithm_for_for_prospectivity_mappeitivity_mapping_mapping_mapgor_with_with_unctainties
- https://www.sciendirect.com/science/article/pii/s1674987123002268->>開發和應用特徵工程地質層用於排名岩漿，火山基因和造型系統組件在Archean Greenstone greenstone greenstone greenstone system中
- https://qspace.library.queensu.ca/bitstream/handle/1974/28138/cevik_ilkay_s_s_202009_masc.pdf?sequence = 3＆isallow = y->機器學習在礦物勘探和改善礦產礦物資源中的知識發現的增強功能
- https://www.researchgate.net/publication/343511849_Indentification_of_intriverive_lithologies_in_volcanic_terrains_in_in_in_in_british_british_columbia_by_machine_machine_man_in_british_british_columbia_by_machine_machine_machine_machine_achine_sforsm; _value_clue_usissofficefcl.sclassoffier
- https://www.researchgate.net/publication/365782501_improving_mineral_prospectivity_model_generalization_aneralization_an_example_from_orogen_gold_gold_gold_gold_mineralization_the_ston_stecton_urgey_gold_gold_gold_mine_mine_transect_transect_Pect_s_transston_stect; transect_transect_ontarario_carada
- https://www.researchgate.net/publication/348983384_mineral_prospectivity_mappedivity_mapping_usis_a_a_vnet_convolutional_neural_neural_neur_network
- 公司連結
- https://www.researchgate.net/publication/369048379_mineral_prospectivity_mapping_mapping_mapping_machine_machine_learniques_techniques_for_gold_gold_gold_explolo_in_the_the_larcan_larwario_lake_gold_gold_explo_area_ariodario_kund_lardario_a_yarea_yri_m碎_yooloolo_ool Arire_學生_hao_hawel碎_hao_碎_hael_hayaolo_問題_不是一天制_hazalhahayaal_hayaal_不是alyaal_hayaal_alyaalyaal_hala很多_
- https://www.researchgate.net/publication/337167506_orogenic_gold_prospectivity_mappedivitivitivity_mapping_usis_machine_learning
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- https://link.springer.com/article/10.1007/s11053-024-10369-7->加拿大碳酸鹽岩宿主的REE +/- NB沉積物的預測建模
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- https://www.researchgate.net/publication/220164155_SUPPORT_VECTOR_MACHINE_MACHINE_A_TOOL_TOOL_FOR_MAPPED_MINERAL_PROSPECTIVY
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- https://data.geology.gov.yk.ca/reference/95936#infotab-> Yukon地質調查局的礦物潛在映射方法更新
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中非
- https://www.researchgate.net/publication/323452014_The_Utility_of_Machine_Learning_in_Identification_of_Key_Geophysical_and_Geochemical_Datasets_A_Case_Study_in_Lithological_Mapping_in_the_Central_African_Copper_Belt
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智利
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中國
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- https://www.researchgate.net/publication/336771580_3d_mineral_prospectivitivitivitivitivitivitivitivitivitivitivitivity_mapping_mapping_with_random_forests_a_case_study_study_of_ton_random_forests_a_case_study_study_of_
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- https://www.researchgate.net/publication/329600793_A_combined_approach_using_spatially-weighted_principal_components_analysis_and_wavelet_transformation_for_geochemical_anomaly_mapping_in_the_Dashui_ore-concentration_district_Central_China
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- https://www.researchgate.net/publication/369865076_a_deep-learning.net/publication/369865076_a_deep-learning.net/publication/369865076_a_deep-learning aste_mineral_prospectivity_modeling_modeling_framework_framework_and_and_and_workflow_in_prediction_of_pordiction_of_porphyry-epy-mal-jionals在內_nec_ibne_ibion_ibion_inizyation_incne_iha_ihac_ibion_inca_incneta_ihac_ibion_inbation' _in_duolong_duololong_ore_orectict
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- https://www.researchgate.net/publication/339821823_A_MONTE_CARLO_CARLO基礎_framework_for_risk-return_analysis_in_mineral_mineral_mineral_prospectivition_mapping
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- https://link.springer.com/article/10.1007/s11004-023-10076-8-可解釋的用於礦物潛在映射的可解釋的圖表網絡
- https://www.researchgate.net/publication/332751556_Application_of_hierarchical_clustering_singularity_mapping_and_Kohonen_neural_network_to_identify_Ag-Au-Pb-Zn_polymetallic_mineralization_associated_geochemical_anomaly_in_Pangxidong_district
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- https://www.researchgate.net/publication/325702993_assessment_of_geochemical_anomaly_anomaly_unclentyty_unclentyty_through_geostatistition_simulation_and_singularity_analisy_analisy
- https://www.researchgate.net/publication/368586826_Bagging-based_Positive-Unlabeled_Data_Learning_Algorithm_with_Base_Learners_Random_Forest_and_XGBoost_for_3D_Exploration_Targeting_in_the_Kalatongke_District_Xinjiang_China
- https://link.springer.com/article/10.1007/s11004-024-10153-6->因果發現和深度學習演算法，用於檢測與金色質量礦化相關的地球化學模式：Edongnan地區的案例研究
- https://www.sciencedirect.com/science/article/pii/s0169136824001409->> CNN-Transformers在Maodeng -Baiyinchagan地區，南部大型Xing'an Range
- https://www.researchgate.net/publication/347079505_Convolutional_neural_network_and_transfer_learning_based_mineral_prospectivity_modeling_for_geochemical_exploration_of_Au_mineralization_within_the_Guandian-Zhangbaling_area_Anhui_Province_China
- https://www.researchgate.net/publication/352703015_Data-driven_based_logistic_function_and_prediction-area_plot_for_mineral_prospectivity_mapping_a_case_study_from_the_eastern_margin_of_Qinling_orogenic_belt_central_China
- https://www.sciendirect.com/science/article/abs/pii/s0012825218306123->深度學習及其在地球化學映射中的應用
- https://www.frontiersin.org/articles/10.3389/feart.2024.1308426/full-->深金腰帶，jiaojia gold Belt，Jiaodong Peninsula，Eastern China
- https://www.researchgate.net/publication/352893038_detection_of_geochemical_anomalies_related_to_mineralization_mineralization_usistor_the_ganomaly_network
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- https://www.sciendirect.com/science/article/arbs/pii/s0375674221001370->>透過基於磁石組成的隨機森林演算法區分IOCG和IOA沉積物
- https://www.researchgate.net/publication/340401748_effects_of_random_negation_negation_training_samples_on_mineral_prospectivity_mapping
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- https://www.mdpi.com/2075-163x/14/5/492-->探索向量和指標和指標在lintan carlin-type金礦中提取的因素分析和關聯規則演算法，中國YouJiang Basin，YouJiang
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- https://www.researchgate.net/publication/359714254_Geochemical_characterization_of_the_Central_Mineral_Belt_U_Cu_Mo_V_mineralization_Labrador_Canada_Application_of_unsupervised_machine-learning_for_evaluation_of_IOCG_and_affiliated_mineral_potential
- https://www.researchgate.net/publication/3507888828_GEOCHEMALICE_CONCENTAIN_PROSPECTIVETIVITION_MAPPEITION_MAPPEITIVE_MAPPED_BY_BY_UNSUPERVISED_CLUSTER_CLUSTER_ANALYSIS
- https://www.researchgate.net/publication/267927506_GIS基礎基礎_mineral_potential_modeling_by_advanced_spatial_spatial_spatial_spatial_methods_in_the_south_southe_southeaster_southeastern_methodannan_in_the_south_southe_southeaster_southeastern_ym_min_min_a_minal_minal_strical_ookal_
- https://www.researchgate.net/publication/380190183_geologicaly_constration_concontain_convolutional_neural_neural_network_for_mineral_prospectivity_mapping
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- https://www.researchgate.net/publication/307011381_INDENCIADIC_ANDICALICY_AND_MAPPED_GEOCHEMICAL_PATTERNS_AND_AND_THEIR_SENIRECICANCE_FOR_REGIANION_FOR_REGIANTION_MEIR_SENIRECICANCE_FOR_REGIANION_FOR_REGIANTION_MEALYIANIANNLY_ JIANG_CHINA
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- https://www.researchgate.net/publication/282621670_Identifying_geochemical_anomalies_associated_with_Au-Cu_mineralization_using_multifractal_and_artificial_neural_network_models_in_the_Ningqiang_district_Shaanxi_China
- https://www.sciendirect.com/science/article/arbs/pii/s0375674224000943->>>整合物理驅動的動力學模擬與數據驅動的機器學習，以預測在成熟的eRefield中預測潛在的目標：在Tongguangshan Orefield offields中進行案例研究： ， 中國
- https://www.researchgate.net/publication/329299202_Integrating_sequential_indicator_simulation_and_singularity_analysis_to_analyze_uncertainty_of_geochemical_anomaly_for_exploration_targeting_of_tungsten_polymetallic_mineralization_Nanling_belt_South_
- https://www.sciendirect.com/science/article/article/abs/pii/s0883292724001987->>整合土壤地球化學和機器學習，以增強礦物質勘探，以增強礦物質勘探
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- https://www.nature.com/articles/s41598-024-73357-0->基於卷積神經網路和合奏學習
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- https://link.springer.com/article/10.1007/s11053-024-10335-3->基於自我監督的對比度學習和地球化學數據的礦物前景預測中國省[使用]
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- https://www.sciencectirect.com/science/article/pii/s0169136824003172->>新見解是對小天AU沉積物的金屬生成生成的新見解
- https://www.researchgate.net/publication/332547136_prospectivity_mappitivity_mappitivity_for_porphyry_cu-mo_mineralization_in_the_eastern_eastern_tianshan_xinshan_xinjiang_northwestern_china_china
- https://www.sciendirect.com/science/article/pii/s0169136824001823->數位預測方法和數位礦石存款模型的應用
- https://www.researchgate.net/publication/344303914_random-drop_data_augmentation_of_deep_deep_convolutional_neural_neural_neural_neur_network_for_mineral_for_mineral_prospection_prospection_mapping
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- https://www.researchgate.net/publication/361520562_recognizing_multivariate_geochemical_anomalies_reled_to_to_mineralization_mineralization_mineralization_by_usis_usis_deep_deep_deep_unsupervisedise_graphaph_learning
- https://www.sciendirect.com/science/article/pii/s0169136824003937->>半舒適的圖形卷積網絡，用於整合連續和二進制證據層用於礦物勘探目標
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- https://link.springer.com/article/10.1007/s11053-024-10387-5-使用深森林預測模型從遙感資料繪製資料驅動資料驅動的礦物的前瞻性映射
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埃及
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英格蘭
- https://www.researchgate.net/publication/342339753_A_MACHINE_LEARNING_EAPPRACE_TO_TO_TUNGSTEN_PROSPECTIVETIVITIOD_MODELITIVE_MODELITIVE_MODELITION_MODELDED_KNO_WEEAT_TRANNO-EDWUU_UU_UU_UU_UU_SUU_UU_UU_SUU_UU_UU_UU3_範範範範文文_3_UU_3_UUS_UUS_UUS_U律_律律AND_MODEL_MODEL_MODEL_CONFIDED
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厄利垂亞
- https://www.researchgate.net/publication/349158008_mapping_gold_mineral_prospectivitivitivitivitivitivitivitivitivitivitivitivitivitivitivitivitivitivitivitivitivin_itivin_itivinivinivinconivinivin; southeast_asteast_asmara_asmara_asmara_ertrea
芬蘭
- https://www.researchgate.net/publication/360661926_Target-scale_prospectivity_modeling_for_gold_mineralization_within_the_Rajapalot_Au-Co_project_area_in_northern_Fennoscandian_Shield_Finland_Part_2_Application_of_self-organizing_maps_and_artificial_n
- https://www.sciendirect.com/science/article/pii/s0169136824004037->解決基於機器學習的礦物質潛在映射的不平衡數據
芬蘭
- https://publications.csiro.au/publications/#publication/PIcsiro:EP146125/SQmineral%20prospectivity/RP1/RS50/RORECENT/STsearch-by-keyword/LRTA/RI12/reg26 -> A novel spatial analysis approach for assession - 芬蘭北部的礦物礦產潛在性
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- https://www.researchgate.net/publication/283451958_data-driven_logistic astem_weighting_ef_geochemical_geemical_and_geologal_evidese_evidese_layers_in_in_in_mineral_mineral_mineral_evidese_evidese_layers_in_in_in_mineral_mineral_mineral_prospectivity_map
- https://www.researchgate.net/publication/320280611_evaluation_of_boosting_algorithms_for_for_prospectivity_mapping
- https://www.researchgate.net/publication/298297988_fuzzy_logic_data_integration_technique_used_as_a_a_a_nickel_exploration_tool
- https://www.researchgate.net/publication/259372191_Gravity_data_in_regional_scale_3D_and_gold_prospectivity_modelling_-_example_from_the_Central_Lapland_greenstone_belt_northern_Finland
- https://www.researchgate.net/publication/315381587_introduction_to_the_the_special_issue_gis ased_mineral_potential_targeting
- https://www.researchgate.net/publication/320709733_knowledge-drive_prospectivity_model_model_for_iron_iron_oxide-cu-au-au_iocg_iocg_deposits_in_in_northern_finland
- https://tupa.gtk.fi/raportti/arkisto/57_2021.pdf->礦物的潛在和勘探目標和探索目標Minproxt 2021 Webinar-紙質彙編
- https://tupa.gtk.fi/raportti/arkisto/29_2023.pdf->礦物的潛在和勘探目標和探索目標Minproxt 2022 Webinar-紙質彙編
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迦納
- https://www.researchgate.net/publication/227256267_application_of_data-data-driven_evidential_belief_belief_functions_to_prospectivity_mmapped_for_aquamarine-bearion_pegamarine-beartivity_mmapped_for_aquamarine-bearion_pundamine-bearmyion_pegites_ppazimdmamah_matu;
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格陵蘭
- https://www.researchgate.net/publication/360970965_Inderification_of_radioactive_mineralized_mineralized_lithology_and_and_mineral_mineral_mineral_mappedivitivity_mapped_baseped_on_on_on_mineral_mineral_mappedivitivity_mapped_baseped_on_on_on_remote_reeg;
印度
- https://www.researchgate.net/publication/372636338_unsupervise_machine_machine_learning_based_based_prospectivity_of_nw_nw_nw_and_ne_ne_india_india_india_forcarbonatite-carbonat-aratolkalwal
印尼
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伊朗
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- https://www.researchgate.net/publication/358507255_a_comparation_study_study_of_convolutional_neural_neural_neural_neurs_networks_and_conventional_conventional_machine_machine_machine_networks_and_conventional_conventional_machine_machine_machine_networks_and_conventional_conventional_machine_machine_machine_networks_and_conventional_conventional_machine_machine_machine_melsboca_isidis_isis_is_is_isis 好的 好的 好的 好的 好的執行_remote_remote_sensing_sensing_sensing_sensing_sensing_datata
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- https://www.researchgate.net/publication/353982380_Porphyry_Cu-Au_prospectivity_modelling_using_semi-supervised_learning_algorithm_in_Dehsalm_district_eastern_Iran_In_Farsi_with_extended_English_abstract
- https://www.researchgate.net/publication/320886789_Prospectivity_analysis_of_orogenic_gold_deposits_in_Saqez-Sardasht_Goldfield_Zagros_Orogen_Iran
- https://www.researchgate.net/publication/361529867_Prospectivity_mapping_of_orogenic_lode_gold_deposits_using_fuzzy_models_a_case_study_of_Saqqez_area_NW_of_Iran
- https://www.researchgate.net/publication/361717490_Quantifying_Uncertainties_Linked_to_the_Diversity_of_Mathematical_Frameworks_in_Knowledge-Driven_Mineral_Prospectivity_Mapping
- https://www.researchgate.net/publication/374730424_Recognition_of_mineralization-related_anomaly_patterns_through_an_autoencoder_neural_network_for_mineral_exploration_targeting
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- https://www.researchgate.net/publication/339153591_Sensitivity_analysis_of_prospectivity_modeling_to_evidence_maps_Enhancing_success_of_targeting_for_epithermal_gold_Takab_district_NW_Iran
- https://www.researchgate.net/publication/321076980_Spatial_analyses_of_exploration_evidence_data_to_model_skarn-type_copper_prospectivity_in_the_Varzaghan_district_NW_Iran
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- https://www.sciencedirect.com/science/article/pii/S0009281924000801 -> Targeting porphyry Cu deposits in the Chahargonbad region of Iran: A joint application of deep belief networks and random forest techniques
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- https://www.researchsquare.com/article/rs-4760956/v1 -> Uncertainty reduction with Hyperparameter Optimization in mineral prospectivity mapping: A Regularized Artificial Neural Network approach [UNSEEN]
愛爾蘭
- https://www.gsi.ie/en-ie/programmes-and-projects/tellus/activities/tellus-product-development/mineral-prospectivity/Pages/default.aspx - > NW Midlands Mineral Prospectivity Mapping
印度
- https://www.researchgate.net/publication/226092981_A_Hybrid_Neuro-Fuzzy_Model_for_Mineral_Potential_Mapping
- https://www.researchgate.net/publication/225328359_A_Hybrid_Fuzzy_Weights-of-Evidence_Model_for_Mineral_Potential_Mapping
- https://www.researchgate.net/publication/227221497_Artificial_Neural_Networks_for_Mineral-Potential_Mapping_A_Case_Study_from_Aravalli_Province_Western_India
- https://www.researchgate.net/publication/222050039_Bayesian_network_classifiers_for_mineral_potential_mapping
- https://www.researchgate.net/publication/355397149_Gold_Prospectivity_Mapping_in_the_Sonakhan_Greenstone_Belt_Central_India_A_Knowledge-Driven_Guide_for_Target_Delineation_in_a_Region_of_Low_Exploration_Maturity
- https://www.researchgate.net/publication/272092276_Extended_Weights-of-Evidence_Modelling_for_Predictive_Mapping_of_Base_Metal_Deposit_Potential_in_Aravalli_Province_Western_India
- https://www.researchgate.net/publication/226193283_Knowledge-Driven_and_Data-Driven_Fuzzy_Models_for_Predictive_Mineral_Potential_Mapping
- https://www.researchgate.net/publication/238027981_SVM-based_base-metal_prospectivity_modeling_of_the_Aravalli_Orogen_Northwestern_India
韓國
- https://www.researchgate.net/publication/382131746_Domain_Adaptation_from_Drilling_to_Geophysical_Data_for_Mineral_Exploration
挪威
- https://www.mdpi.com/2075-163X/9/2/131/htm - Prospectivity Mapping of Mineral Deposits in Northern Norway Using Radial Basis Function Neural Networks
韓國
- https://www.researchgate.net/publication/221911782_Application_of_Artificial_Neural_Network_for_Mineral_Potential_Mapping
- https://www.researchgate.net/publication/359861043_Rock_Classification_in_a_Vanadiferous_Titanomagnetite_Deposit_Based_on_Supervised_Machine_Learning#fullTextFileContent Rock Classification in a Vanadiferous Titanomagnetite Deposit Based on Supervised Machine Learning
菲律賓
- https://www.researchgate.net/publication/359632307_A_Geologically_Constrained_Variational_Autoencoder_for_Mineral_Prospectivity_Mapping
- https://www.researchgate.net/publication/263174923_Application_of_Mineral_Exploration_Models_and_GIS_to_Generate_Mineral_Potential_Maps_as_Input_for_Optimum_Land-Use_Planning_in_the_Philippines
- https://www.researchgate.net/publication/267927677_Data-driven_predictive_mapping_of_gold_prospectivity_Baguio_district_Philippines_Application_of_Random_Forests_algorithm
- https://www.researchgate.net/publication/276271833_Data-Driven_Predictive_Modeling_of_Mineral_Prospectivity_Using_Random_Forests_A_Case_Study_in_Catanduanes_Island_Philippines
- https://www.researchgate.net/publication/209803275_Evidential_belief_functions_for_data-driven_geologically_constrained_mapping_of_gold_potential_Baguio_district_Philippines
- https://www.researchgate.net/publication/241001432_Geologically_Constrained_Probabilistic_Mapping_of_Gold_Potential_Baguio_District_Philippines
- https://www.researchgate.net/publication/263724277_Geologically_Constrained_Fuzzy_Mapping_of_Gold_Mineralization_Potential_Baguio_District_Philippines
- https://www.researchgate.net/publication/229641286_Improved_Wildcat_Modelling_of_Mineral_Prospectivity
- https://www.researchgate.net/publication/238447208_Logistic_Regression_for_Geologically_Constrained_Mapping_of_Gold_Potential_Baguio_District_Philippines
- https://www.researchgate.net/publication/248977334_Mineral_imaging_with_Landsat_TM_data_for_hydrothermal_alteration_mapping_in_heavily-vegetated_terrane
- https://www.researchgate.net/publication/356546133_Mineral_Prospectivity_Mapping_via_Gated_Recurrent_Unit_Model
- https://www.researchgate.net/publication/267640864_Random_forest_predictive_modeling_of_mineral_prospectivity_with_small_number_of_prospects_and_data_with_missing_values_in_Abra_Philippines
- https://www.researchgate.net/publication/3931975_Remote_detection_of_vegetation_stress_for_mineral_exploration
- https://www.researchgate.net/publication/263422015_Where_Are_Porphyry_Copper_Deposits_Spatially_Localized_A_Case_Study_in_Benguet_Province_Philippines
- https://www.researchgate.net/publication/233488614_Wildcat_mapping_of_gold_potential_Baguio_District_Philippines
- https://www.researchgate.net/publication/226982180_Weights_of_Evidence_Modeling_of_Mineral_Potential_A_Case_Study_Using_Small_Number_of_Prospects_Abra_Philippines
俄羅斯
- https://www.researchgate.net/publication/358431343_Application_of_Maximum_Entropy_for_Mineral_Prospectivity_Mapping_in_Heavily_Vegetated_Areas_of_Greater_Kurile_Chain_with_Landsat_8_Data
- https://www.researchgate.net/publication/354000754_Mineral_Prospectivity_Mapping_for_Forecasting_Gold_Deposits_in_the_Central_Kolyma_Region_North-East_Russia
南非
- https://www.researchgate.net/publication/359294267_Data-driven_multi-index_overlay_gold_prospectivity_mapping_using_geophysical_and_remote_sensing_datasets
- https://link.springer.com/article/10.1007/s11053-024-10390-w -> Mineral Reconnaissance Through Scientific Consensus: First National Prospectivity Maps for PGE–Ni–Cu–Cr and Witwatersrand-type Au Deposits in South Africa
- https://www.researchgate.net/publication/361526053_Mineral_prospectivity_mapping_of_gold-base_metal_mineralisation_in_the_Sabie-Pilgrim%27s_Rest_area_Mpumalanga_Province_South_Africa
- https://www.researchgate.net/publication/264296137_PREDICTIVE_BEDROCK_AND_MINERAL_PROSPECTIVITY_MAPPING_IN_THE_GIYANI_GREENSTONE_BELT_SOUTH_AFRICA
- https://www.researchgate.net/publication/268196204_Predictive_mapping_of_prospectivity_for_orogenic_gold_Giyani_greenstone_belt_South_Africa
西班牙
- https://www.researchgate.net/publication/225656353_Deriving_Optimal_Exploration_Target_Zones_on_Mineral_Prospectivity_Maps
- https://www.researchgate.net/publication/222198648_Knowledge-guided_data-driven_evidential_belief_modeling_of_mineral_prospectivity_in_Cabo_de_Gata_SE_Spain
- https://www.researchgate.net/publication/356639977_Machine_learning_models_for_Hg_prospecting_in_the_Almaden_mining_district
- https://www.researchgate.net/publication/43165602_Methodology_for_deriving_optimal_exploration_target_zones
- https://www.researchgate.net/publication/263542579_Optimal_Exploration_Target_Zones
- https://www.researchgate.net/publication/222892103_Optimal_field_sampling_for_targeting_minerals_using_hyperspectral_data
- https://www.researchgate.net/publication/271671416_Predictive_modelling_of_gold_potential_with_the_integration_of_multisource_information_based_on_random_forest_a_case_study_on_the_Rodalquilar_area_Southern_Spain
蘇丹
- https://link.springer.com/article/10.1007/s11053-024-10387-5 -> Toward Data-Driven Mineral Prospectivity Mapping from Remote Sensing Data Using Deep Forest Predictive Model [UNSEEN]
瑞典
- https://www.researchgate.net/publication/259128115_Biogeochemical_expression_of_rare_earth_element_and_zirconium_mineralization_at_Norra_Karr_Southern_Sweden
- https://www.researchgate.net/publication/260086862_COMPARISION_OF_VMS_PROSPECTIVITY_MAPPING_BY_EBF_AND_WOFE_MODELING_THE_SKELLEFTE_DISTRICT_SWEDEN
- https://www.researchgate.net/publication/336086368_GIS-based_mineral_system_approach_for_prospectivity_mapping_of_iron-oxide_apatite-bearing_mineralisation_in_Bergslagen_Sweden
- https://www.researchgate.net/publication/229347041_Predictive_mapping_of_prospectivity_and_quantitative_estimation_of_undiscovered_VMS_deposits_in_Skellefte_district_Sweden
- https://www.researchgate.net/publication/260086947_PRELIMINARY_GIS-BASED_ANALYSIS_OF_REGIONAL-SCALE_VMS_PROSPECTIVITY_IN_THE_SKELLEFTE_REGION_SWEDEN
坦尚尼亞
- https://www.sciencedirect.com/science/article/pii/S2666261224000270 -> Machine learning based prospect targeting: A case of gold occurrence in central parts of Tanzania, East Africa
烏幹達
- https://www.researchgate.net/publication/242339962_Predictive_mapping_for_orogenic_gold_prospectivity_in_Uganda
- https://www.researchgate.net/publication/262566098_Predictive_Mapping_of_Prospectivity_for_Orogenic_Gold_in_Uganda
- https://www.researchgate.net/publication/381219015_Machine_Learning_Application_in_Predictive_Mineral_Mapping_of_Southwestern_Uganda_Leveraging_Airborne_Magnetic_Radiometric_and_Electromagnetic_Data
英國
- https://www.researchgate.net/publication/383580839_Improved_mineral_prospectivity_mapping_using_graph_neural_networks
美國
- https://www.researchgate.net/publication/338663292_A_Predictive_Geospatial_Exploration_Model_for_Mississippi_Valley_Type_Pb-Zn_Mineralization_in_the_Southeast_Missouri_Lead_District
- https://www.sciencedirect.com/science/article/abs/pii/S0375674218300396?via%3Dihub -> Machine learning strategies for classification and prediction of alteration facies: 執行長亞利桑那
- [presentation of the above!] https://www.slideshare.net/JuanCarlosOrdezCalde/geology-chemostratigraphy-and-alteration-geochemistry-of-the-rosemont-cumoag-skarn-deposit-southern-arizona
- https://github.com/rohitash-chandra/research/blob/master/presentations/CSIRO%20Minerals-Seminar-September2022.pdf -> Machine Learning for Mineral Exploration: A Data Odyssey
- Video https://www.youtube.com/watch?v=zhXuPQy7mk8&t=561s -> Talks about using plate subduction and associated statistics via GPlates
尚比亞
- https://www.researchgate.net/publication/263542565_APPLICATION_OF_REMOTE_SENSING_AND_SPATIAL_DATA_INTEGRATION_TO_PREDICT_POTENTIAL_ZONES_FOR_AQUAMARINE-BEARING_PEGMATITES_LUNDAZI_AREA_NORTHEAST_ZAMBIA
- https://www.researchgate.net/publication/264041472_Geological_and_Mineral_Potential_Mapping_by_Geoscience_Data_Integration
辛巴威
- https://www.researchgate.net/publication/260792212_Nickel_Sulphide_Deposits_in_Archaean_Greenstone_Belts_in_Zimbabwe_Review_and_Prospectivity_Analysis
GENERAL PAPERS
概述
- https://www.sciencedirect.com/science/article/pii/S2772883824000347 -> A review on the applications of airborne geophysical and remote sensing datasets in epithermal gold mineralisation mapping
- https://www.researchgate.net/publication/353530416_A_Systematic_Review_on_the_Application_of_Machine_Learning_in_Exploiting_Mineralogical_Data_in_Mining_and_Mineral_Industry
- https://www.researchgate.net/publication/365777421_Computer_Vision_and_Pattern_Recognition_for_the_Analysis_of_2D3D_Remote_Sensing_Data_in_Geoscience_A_Survey - Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey
- https://www.researchgate.net/publication/352104303_Deep_Learning_for_Geophysics_Current_and_Future_Trends
- https://www.proquest.com/openview/e7bec6c8ee50183b5049516b000d4f5c/1?pq-origsite=gscholar&cbl=18750&diss=y -> Probabilistic Knowledge-Guided Machine Learning in Engineering and Geoscience Systems
- KGMLPrescribedFires repository for one paper / part of above dissertation
存款
- https://pubs.er.usgs.gov/publication/ofr20211049 -> Deposit Classification Scheme for the Critical Minerals Mapping Initiative Global Geochemical Database
環境、社會及治理
- https://www.escubed.org/journals/earth-science-systems-and-society/articles/10.3389/esss.2024.10109/full -> Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The The Case鋰
地球化學
Causal Discovery and Deep Learning Algorithms for Detecting Geochemical Patterns Associated with Gold-Polymetallic Mineralization: A Case Study of the Edongnan Region
- https://link.springer.com/article/10.1007/s11053-024-10408-3 -> A New Sphalerite Thermometer Based on Machine Learning with Trace Element Geochemistry
- https://www.researchgate.net/publication/378150628_A_SMOTified_extreme_learning_machine_for_identifying_mineralization_anomalies_from_geochemical_exploration_data_a_case_study_from_the_Yeniugou_area_Xinjiang_China A SMOTified extreme learning machine for identifying mineralization anomalies from geochemical exploration data
- https://ui.adsabs.harvard.edu/abs/2018EGUGA..20.4169R/abstract -> Accelerating minerals exploration with in-field characterisation, sample tracking and active machine learning
- https://www.researchgate.net/publication/375509344_Alteration_assemblage_characterization_using_machine_learning_applied_to_high_resolution_drill-core_images_hyperspectral_data_and_geochemistry
- https://qspace.library.queensu.ca/items/38f52d19-609d-4916-bcd0-3ce20675dee3/full - > Application of Computational Methods to Data Integration and Geoscientific Problems in Mineral Exploration and Mining
- https://www.sciencedirect.com/science/article/pii/S0169136822005509?dgcid=rss_sd_all -> Applying neural networks-based modelling to the prediction of mineralization: A case-study using the Western Australian Geochemistry (WACHEM) database
- https://www.sciencedirect.com/science/article/pii/S0169136824002099 -> Development of a machine learning model to classify mineral deposits using sphalerite chemistry and mineral assemblages
- https://www.sciencedirect.com/science/article/pii/S0169136824002403 -> Discrimination of deposit types using magnetite geochemistry based on machine learning
- https://www.researchgate.net/publication/302595237_A_machine_learning_approach_to_geochemical_mapping
- https://www.researchgate.net/publication/369300132_DEEP-LEARNING_IDENTIFICATION_OF_ANOMALOUS_DATA_IN_GEOCHEMICAL_DATASETS_DEEP-LEARNING_IDENTIFICATION_OF_ANOMALOUS_DATA_IN_GEOCHEMICAL_DATASETS
- https://www.researchgate.net/publication/378549920_Denoising_of_geochemical_data_using_deep_learning-Implications_for_regional_surveys -> Denoising of Geochemical Data using Deep Learning–Implications for Regional Surveys]
- https://www.researchgate.net/publication/368489689_discrimination_of_pb-zn_deposit_types_using_sphalerite_geochemistion_geochemistion_new_inew_insights_from_machine_machine_geochemistion_new_inew_insights_from_machine_machine_leargorithmachine_leargorithm
- https://www.researchgate.net/publication/381369176_Effectiveness_of_LOF_iForest_and_OCSVM_in_detecting_anomalies_in_stream_sediment_geochemical_data#:~:text=LOF%20outperformed%20iForest%20and%20OCSVM,patterns%20in%20the%20iForest%20map
- https://dzkjqb.cug.edu.cn/en/article/doi/10.19509/j.cnki.dzkq.tb20220423 -> Gaussian mixture model in geochemical anomaly delineation of stream sedi。 ]
- https://www.sciencedirect.com/science/article/pii/S0883292724002427 -> Geologically constrained unsupervised dual-branch deep learning algorithm for geochemical anomalies identification
- https://www.researchgate.net/publication/365953549_Incorporating_the_genetic_and_firefly_optimization_algorithms_into_K-means_clustering_method_for_detection_of_porphyry_and_skarn_Cu-related_geochemical_footprints_in_Baft_district_Kerman_Iran
- https://www.researchgate.net/publication/369768936_Infomax-based_deep_autoencoder_network_for_recognition_of_multi-element_geochemical_anomalies_linked_to_mineralization -> Paywalled
- https://www.sciencedirect.com/science/article/abs/pii/S0098300424001626 -> Local phase-constrained convolutional autoencoder network for identifying multivariate geochemical anomalies
- https://www.researchgate.net/publication/354564681_Machine_Learning_for_Identification_of_Primary_Water_Concentrations_in_Mantle_Pyroxene
- https://www.researchgate.net/publication/366210211_Machine_Learning_Prediction_of_Ore_Deposit_Genetic_Type_Using_Magnetite_Geochemistry
- https://link.springer.com/article/10.1007/s42461-024-01013-2 -> NIR-Spectroscopy and Machine Learning Models to Pre-concentrate Copper Hosted Within Sedimentary Rocks[UNSEEN]
- https://www.researchsquare.com/article/rs-4106957/v1 -> Multi-element geochemical anomaly recognition applying geologically-constrained convolutional deep learning algorithm with Butterworth filtering
- https://www.researchgate.net/publication/369241349_Quantifying_continental_crust_thickness_using_the_machine_learning_method
- https://link.springer.com/article/10.1007/s11004-024-10158-1 -> Spatial-Spectrum Two-Branch Model Based on a Superpixel Graph Convolutional Network and 1DCNN for Geochemical Anomaly Identification
- https://www.researchgate.net/publication/334651800_Using_machine_learning_to_estimate_a_key_missing_geochemical_variable_in_mining_exploration_Application_of_the_Random_Forest_algorithm_to_multi-sensor_core_logging_data
磷灰石
- https://www.researchgate.net/publication/377892369_Apatite_trace_element_composition_as_an_indicator_of_ore_deposit_types_A_machine_learning_approachApatite trace element composition as an indicator of ore deposit types: A machine learning approach
- https://www.researchgate.net/publication/369729999_Visual_Interpretation_of_Machine_Learning_Genetical_Classification_of_Apatite_from_Various_Ore_Sources
地質學
改造
- https://ieeexplore.ieee.org/abstract/document/10544529 -> Remote sensing data processing using convolutional neural networks for mapping alteration zones [UNSEEN]
深度
- https://www.researchgate.net/publication/332263305_A_speedy_update_on_machine_learning_applied_to_bedrock_mapping_using_geochemistry_or_geophysics_examples_from_the_Pacific_Rim_and_nearby
- https://eprints.utas.edu.au/32368/ - thesis paper update
- https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2024.1407173/full -> Deep learning for geological mapping in the overburden area
- https://www.researchgate.net/publication/280038632_Estimating_the_fill_thickness_and_bedrock_topography_in_intermontane_valleys_using_artificial_neural_networks_-_Supporting_Information
- https://www.researchgate.net/publication/311783770_Mapping_the_global_depth_to_bedrock_for_land_surface_modeling
- https://www.researchgate.net/publication/379813337_Contribution_to_advancing_aquifer_geometric_mapping_using_machine_learning_and_deep_learning_techniques_a_case_study_of_the_AL_Haouz-Mejjate_aquifer_Marrakech_Morocco
- https://www.linkedin.com/pulse/depth-basement-modelling-machine-learning-perspective-n5gyc/?trackingId=qFSktvVPUiSa2V2nlmXVoQ%3D%3D
鑽孔
- https://pubmed.ncbi.nlm.nih.gov/35776744/ - Deep learning based lithology classification of drill core images
- https://www.researchgate.net/publication/381445417_Machine_Learning_for_Lithology_Analysis_using_a_Multi-Modal_Approach_of_Integrating_XRF_and_XCT_data
- https://www.researchgate.net/publication/379760986_A_machine_vision_approach_for_detecting_changes_in_drill_core_textures_using_optical_images
- https://www.sciencedirect.com/science/article/pii/S2949891024002112 -> Sensitivity analysis of similarity learning models for well-intervals based on logging data
- https://www.sciencedirect.com/science/article/pii/S2949891024003828 -> CoreViT: a new vision transformer model for lithology identification in cores
一般的
- https://www.sciencedirect.com/science/article/pii/S0034425724002323 -> Deep learning-based geological map generation using geological routes
- https://www.researchgate.net/publication/354781583_Deep_learning_framework_for_geological_symbol_detection_on_geological_maps
- https://www.researchgate.net/publication/335104674_Does_shallow_geological_knowledge_help_neural-networks_to_predict_deep_units
- https://www.researchgate.net/publication/379939974_Graph_convolutional_network_for_lithological_classification_and_mapping_using_stream_sediment_geochemical_data_and_geophysical_data
- https://www.sciencedirect.com/science/article/abs/pii/S0098300424001493-> FlexLogNet: A flexible deep learning-based well-log completion method of adaptively using what you have to predict what you are missing
- https://ieeexplore.ieee.org/abstract/document/10493129 -> Geological Background Prototype Learning Enhanced Network for Remote Sensing-Based Engineering Geological Lithology Interpretation in Highly Vegetated Areas [Unseen]
- https://www.sciencedirect.com/science/article/pii/S2096249524000619 -> Generating extremely low-dimensional representation of subsurface earth models using vector quantization and deep Autoencoder
- https://www.researchgate.net/publication/370175012_GeoPDNN_A_Semisupervised_Deep_Learning_Neural_Network_Using_Pseudolabels_for_Three-dimensional_Urban_Geological_Modelling_and_Uncertainty_Analysis_from_Borehole_Data
- https://www.researchsquare.com/article/rs-4805227/v1 -> Synergizing AI with Geology: Exploring VisionTransformers for Rock Classification
- https://www.researchgate.net/publication/343511849_Identification_of_intrusive_lithologies_in_volcanic_terrains_in_British_Columbia_by_machine_learning_using_Random_Forests_the_value_of_using_a_soft_classifier
- https://www.sciencedirect.com/science/article/pii/S0169136824000921 -> Machine learning-based field geological mapping: A new exploration of geological survey data acquisition strategy https://www.researchgate.net/publication/324411647_Predicting_rock_type_and_detecting_hydrothermal_alteration_using_machine_learning_and_petrophysical_properties_of_the_Canadian_Malartic_ore_and_host_rocks_Pontiac_Subprovince_Quebec_Canada
- https://www.sciencedirect.com/science/article/abs/pii/S0895981124001743 -> Utilizing Random Forest algorithm for identifying mafic and ultramafic rocks in the Gameleira Suite, Archean-Paleoproterozoic basement of the Brasília Belt, Brazil
- https://arxiv.org/pdf/2407.18100 -> DINOv2 Rocks Geological Image Analysis: Classification,
地質年代學
- https://www.researchgate.net/publication/379077847_Tracing_Andean_Origins_A_Machine_Learning_Framework_for_Lead_Isotopes
地形學
- https://agu.confex.com/agu/fm18/mediafile/Handout/Paper427843/Landforms%20Poster.pdf -> Using machine learning to classify landforms for minerals exploration
- https://www.tandfonline.com/doi/abs/10.1080/13658816.2024.2414409 -> GeomorPM: a geomorphic pretrained model integrating convolution and Transformer architectures based on DEM data
岩性
- https://link.springer.com/article/10.1007/s11053-024-10396-4 -> Interpretable SHAP Model Combining Meta-learning and Vision Transformer for Lithology Classification Using Limited and Unbalanced Drilling Data in Well Logging [UNSEN]
- https://www.nature.com/articles/s41598-024-66199-3 -> Machine learning and remote sensing-based lithological mapping of the Duwi Shear-Belt area, Central Eastern Desert, Egypt
- https://link.springer.com/article/10.1007/s11053-024-10375-9 - SsL-VGMM: A Semisupervised Machine Learning Model of Multisource Data Fusion for Lithology Prediction [UNSEEN]
- https://www.researchgate.net/publication/380719080_An_integrated_machine_learning_framework_with_uncertainty_quantification_for_three-dimensional_lithological_modeling_from_multi-source_geophysical_data_and_drilling_data
- https://www.bio-conferences.org/articles/bioconf/pdf/2024/34/bioconf_rena23_01005.pdf -> Lithological Mapping using Artificial Intelligence and Remote Sensing data: A Case Study of Bab Boudir region Morocco
礦物學
- https://pubs.geoscienceworld.org/msa/ammin/article-abstract/doi/10.2138/am-2023-9092/636861/The-application-of-transfer-learning-in-optical -> The application of “transfer learning” in optical microscopy: the petrographic classification of metallic minerals
- https://www.researchgate.net/publication/385074584_Deep_Learning-Based_Mineral_Classification_Using_Pre-Trained_VGG16_Model_with_Data_Augmentation_Challenges_and_Future_Directions
地層學
- https://www.researchgate.net/publication/335486001_A_Stratigraphic_Prediction_Method_Based_on_Machine_Learning
- https://www.researchgate.net/publication/346641320_Classifying_basin-scale_stratigraphic_geometries_from_subsurface_formation_tops_with_machine_learning
結構
- https://www.sciencedirect.com/science/article/pii/S0098300421000285 -> A machine learning model for structural trend fields
- https://onlinelibrary.wiley.com/doi/full/10.1111/1365-2478.13589 -> Inferring fault structures and overburden depth in 3D from geophysical data using machine learning algorithms – A case study on the Fenelon gold deposit, Quebec, Canada
- https://www.sciencedirect.com/science/article/pii/S019181412400138X -> Mapping paleostress trajectories by means of the clustering of reduced stress tensors determined from homogeneous and heterogeneous data sets
- https://www.researchgate.net/publication/332267249_Seismic_fault_detection_using_an_encoder-decoder_convolutional_neural_network_with_a_small_training_set
- https://www.researchgate.net/publication/377168034_Unsupervised_machine_learning_and_depth_clusters_of_Euler_deconvolution_of_magnetic_data_a_new_approach_to_imaging_geological_structures
- https://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggae226/7701418 -> Use of Decision Tree Ensembles for Crustal Structure Imaging from Receiver Functions
構造學
- https://www.researchgate.net/publication/371594975_Assessing_plate_reconstruction_models_using_plate_driving_force_consistency_tests
- https://www.researchgate.net/publication/333182666_Decoding_Earth's_plate_tectonic_history_using_sparse_geochemical_data
- https://www.researchgate.net/publication/376519740_Machine_learning_and_tectonic_setting_determination_Bridging_the_gap_between_Earth_scientists_and_data_scientists
- https://pubs.geoscienceworld.org/gsa/geology/article-abstract/doi/10.1130/G52466.1/648458/Prediction-of-CO2-content-in-mid-ocean-ridge -> Prediction of CO2 content in mid-ocean ridge basalts via a machine learning approach
地球物理學
基礎
- https://www.researchgate.net/publication/373714604_Seismic_Foundation_Model_SFM_a_new_generation_deep_learning_model_in_geophysics
一般的
- https://essopenarchive.org/users/841077/articles/1231187-bayesian-inference-in-geophysics-with-ai-enhanced-markov-chain-monte-carlo -> Bayesian Inference in Geophysics with AI-enhanced Markovain卡洛
- https://www.researchgate.net/publication/353789276_Geology_differentiation_by_applying_unsupervised_machine_learning_to_multiple_independent_geophysical_inversions
- https://www.sciencedirect.com/science/article/pii/S001379522100137X - Joint interpretation of geophysical data: Applying machine learning to the modeling of an evaporitic sequence in Villar de Cañas (Spain)
- https://www.sciencedirect.com/science/article/pii/S2666544121000253 - Microleveling aerogeophysical data using deep convolutional network and MoG-RPCA
- https://www.researchgate.net/publication/368550674_Objective_classification_of_high-resolution_geophysical_data_Empowering_the_next_generation_of_mineral_exploration_in_Sierra_Leone
- https://datarock.com.au/blog/transfer-learning-with-seismic-attributes -> Transfer Learning with Seismic Attributes
Potential Fields
- https://api.research-repository.uwa.edu.au/ws/portalfiles/portal/390212334/THESIS_-_DOCTOR_OF_PHILOSOPHY_-_SMITH_Luke_Thomas_-_2023_.pdf -> Potential Field Geophysics Enhancement Using Conteporary Deep Learning
EM
- https://d197for5662m48.cloudfront.net/documents/publicationstatus/206704/preprint_pdf/59681a0a2c571bc2a9006f37517bc6ef.pdf -> A Fast Three-dimensional Imaging Scheme of Airborne Time Domain Electromagnetic Data using Deep Learning
- https://www.researchgate.net/publication/351507441_A_Neural_Network-Based_Hybrid_Framework_for_Least-Squares_Inversion_of_Transient_Electromagnetic_Data
- https://www.researchgate.net/profile/Yunhe-Liu/publication/382196526_An_Efficient_Bayesian_Inference_for_Geo-electromagnetic_Data_Inversion_based_on_Surrogate_Modeling_with_Adaptive_Sampling_DNN
- https://www.researchgate.net/publication/325980016_Agglomerative_hierarchical_clustering_of_airborne_electromagnetic_data_for_multi-scale_geological_studies
- https://www.earthdoc.org/content/papers/10.3997/2214-4609.202410980 -> Deep Learning Assisted 2-D Current Density Modelling of Very Low Frequency Electromagnetic Data
- https://npg.copernicus.org/articles/26/13/2019/ -> Denoising stacked autoencoders for transient electromagnetic signal denoising
- https://www.researchgate.net/publication/373836226_An_information_theoretic_Bayesian_uncertainty_analysis_of_AEM_systems_over_Menindee_Lake_Australia -> An information theoretic Bayesian uncertainty analysis of AEM systems over Menindee Lake, Australia
- https://www.researchgate.net/publication/348850484_Effect_of_Data_Normalization_on_Neural_Networks_for_the_Forward_Modelling_of_Transient_Electromagnetic_Data
- https://www.researchgate.net/publication/342153377_Fast_imaging_of_time-domain_airborne_EM_data_using_deep_learning_technology
- https://library.seg.org/doi/10.4133/JEEG4.2.93 -> Neural Network Interpretation of High Frequency Electromagnetic Ellipticity Data Part I: Understanding the Half-Space and Layered Earth Response
- https://arxiv.org/abs/2207.12607 -> Physics Embedded Machine Learning for Electromagnetic Data Imaging
- https://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggae244/7713480 -> Physics-guided deep learning-based inversion for airborne electromagnetic data
- https://library.seg.org/doi/abs/10.1190/geo2024-0282.1 -> Comparative Analysis of Deep Learning and Traditional Airborne Electromagnetic Data Processing: A Case Study [UNSEEN]
- https://www.researchgate.net/publication/359441000_Surface_parameters_and_bedrock_properties_covary_across_a_mountainous_watershed_Insights_from_machine_learning_and_geophysics
- https://www.researchgate.net/publication/337166479_Using_machine_learning_to_interpret_3D_airborne_electromagnetic_inversions
- https://www.researchgate.net/publication/344397798_TEMDnet_A_Novel_Deep_Denoising_Network_for_Transient_Electromagnetic_Signal_With_Signal-to-Image_Transformation
- https://www.researchgate.net/publication/366391168_Two-dimensional_fast_imaging_of_airborne_EM_data_based_on_U-net
緊急RT
- https://www.sciencedirect.com/science/article/pii/S0013795224001893 -> Geo-constrained clustering of resistivity data revealing the heterogeneous lithological architectures and the distinctive geoelectrical signature of shallow deposits
重力
- https://ieeexplore.ieee.org/abstract/document/10597585 -> 3D Basement Relief and Density Inversion Based on EfficientNetV2 Deep Learning Network [UNSEEN]
- https://link.springer.com/article/10.1007/s11770-024-1096-5 -> 3D gravity inversion using cycle-consistent generative adversarial network [UNSEEN]
- https://www.researchgate.net/publication/365142017_3D_gravity_inversion_based_on_deep_learning
- https://www.researchgate.net/publication/378930477_A_Deep_Learning_Gravity_Inversion_Method_Based_on_a_Self-Constrained_Network_and_Its_Application
- https://www.researchgate.net/publication/362276214_DecNet_Decomposition_network_for_3D_gravity_inversion -> Olympic Dam example here
- https://www.researchgate.net/publication/368448190_Deep_Learning_to_estimate_the_basement_depth_by_gravity_data_using_Feedforward_neural_network
- https://www.researchgate.net/publication/326231731_Depth_and_Lineament_Maps_Derived_from_North_Cameroon_Gravity_Data_Computed_by_Artificial_Neural_Network_International_Journal_of_Geophysics_vol_2018_Article_ID_1298087_13_pages_2018
- https://www.researchgate.net/publication/366922016_Fast_imaging_for_the_3D_density_structures_by_machine_learning_approach
- https://www.researchgate.net/publication/370230217_Inversion_of_the_Gravity_Gradiometry_Data_by_ResUet_Network_An_Application_in_Nordkapp_Basin_Barents_Sea
- https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2022.897055/full -> High-precision downward continuation of the potential field based on the D-Unet network
- https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10672527 -> RTM Gravity Forward Modeling Using Improved Fully Connected Deep Neural Networks
高光譜
- https://www.researchgate.net/publication/380391736_A_review_on_hyperspectral_imagery_application_for_lithological_mapping_and_mineral_prospecting_Machine_learning_techniques_and_future_prospects
- https://www.researchgate.net/publication/372876863_Ore-Grade_Estimation_from_Hyperspectral_Data_Using_Convolutional_Neural_Networks_A_Case_Study_at_the_Olympic_Dam_Iron_Oxide_Copper-Gold_Deposit_Australia [UNSEEN]
聯合反演
- https://www.researchgate.net/publication/383454185_Deep_joint_inversion_of_electromagnetic_seismic_and_gravity_data
- https://ieeexplore.ieee.org/abstract/document/10677418 -> Joint Inversion of Seismic and Resistivity Data Powered by Deep-learning [UNSEEN]
磁學
- https://www.researchgate.net/publication/348697645_3D_geological_structure_inversion_from_Noddy-generated_magnetic_data_using_deep_learning_methods
- https://www.researchgate.net/publication/360288249_3D_Inversion_of_Magnetic_Gradient_Tensor_Data_Based_on_Convolutional_Neural_Networks
- https://www.researchgate.net/publication/295902270_Artificial_neural_network_inversion_of_magnetic_anomalies_caused_by_2D_fault_structures
- https://www.researchgate.net/publication/354002966_Convolutional_neural_networks_for_the_characterization_of_magnetic_anomalies
- https://www.researchgate.net/publication/354772176_Convolution_Neural_Networks_Applied_to_the_Interpretation_of_Lineaments_in_Aeromagnetic_Data
- https://www.researchgate.net/publication/363550362_High-precision_downward_continuation_of_the_potential_field_based_on_the_D-Unet_network
- https://www.sciencedirect.com/science/article/pii/S0169136822004279?via%3Dihub -> Magnetic grid resolution enhancement using machine learning: A case study from the Eastern Goldfields Superterrane
- https://www.researchgate.net/publication/347173621_Predicting_Magnetization_Directions_Using_Convolutional_Neural_Networks -> Paywalled
- https://www.researchgate.net/publication/361114986_Reseaux_de_Neurones_Convolutifs_pour_la_Caracterisation_d'Anomalies_Magnetiques -> French original of the above
大地電磁學
- https://advancesincontinuousanddiscretemodels.springeropen.com/articles/10.1186/s13662-024-03842-3 -> 2D magnetotelluric imaging method based on visionary self-attention mechanism and data science
- https://ieeexplore.ieee.org/abstract/document/10530937 -> A Magnetotelluric Data Denoising Method Based on Lightweight Ensemble Learning [UNSEEN]
- https://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggae166/7674890 -> Deep basin conductor characterization using machine learning-assisted magnetotelluric Bayesian inversion in the SW Barents Sea
- http://en.dzkx.org/article/doi/10.6038/cjg2024R0580 -> Fast inversion method of apparent resistivity based on deep learning
- https://www.researchgate.net/publication/367504269_Flexible_and_accurate_prior_model_construction_based_on_deep_learning_for_2D_magnetotelluric_data_inversion
- https://www.sciencedirect.com/science/article/pii/S2214579624000510 -> Intelligent Geological Interpretation of AMT Data Based on Machine Learning
- https://ieeexplore.ieee.org/abstract/document/10551853 -> Magnetotelluric Data Inversion Based on Deep Learning with the Self-attention Mechanism
- https://www.researchgate.net/publication/361741409_Physics-Driven_Deep_Learning_Inversion_with_Application_to_Magnetotelluric
- https://www.researchgate.net/publication/355568465_Stochastic_inversion_of_magnetotelluric_data_using_deep_reinforcement_learning
- https://www.researchgate.net/publication/354360079_Two-dimensional_deep_learning_inversion_of_magnetotelluric_sounding_data
- https://ieeexplore.ieee.org/abstract/document/10530923 -> Three Dimensional Magnetotelluric Forward Modeling Through Deep Learning
Passive Seismic
- https://nature.com/articles/s41467-020-17841-x -> Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
- https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022GL099053 -> Eikonal Tomography With Physics-Informed Neural Networks: Rayleigh Wave Phase Velocity in the Northeastern Margin of the Tibetan Plateau
- https://arxiv.org/abs/2403.15095 -> End-to-End Mineral Exploration with Artificial Intelligence and Ambient Noise Tomography
- https://www.nature.com/articles/s41598-019-50381-z -> High-resolution seismic tomography of Long Beach, CA using machine learning
地震
- https://www.sciencedirect.com/science/article/pii/S0040195124002166 -> Reprocessing and interpretation of legacy seismic data using machine learning from the Granada Basin, Spain
- https://ojs.uni-miskolc.hu/index.php/geosciences/article/view/3313 -> EDGE DETECTION OF TOMOGRAPHIC IMAGES USING TRADITIONAL AND DEEP LEARNING TOOLS
表面電阻率
- https://www.researchgate.net/publication/367606119_Deriving_Surface_Resistivity_from_Polarimetric_SAR_Data_Using_Dual-Input_UNet
不確定
- https://library.seg.org/doi/abs/10.1190/GEM2024-084.1 -> Quantifying uncertainty in 3D geophysical inverse problems: Advancing from deterministic to Bayesian and deep generative models [UNSEEN]
地熱
- https://www.osti.gov/biblio/2335471 - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada [adjacent but interesting]
- https://gdr.openei.org/submissions/1402 - Associated code
- https://catalog.data.gov/dataset/python-codebase-and-jupyter-notebooks-applications-of-machine-learning-techniques-to-geoth
- https://www.researchgate.net/publication/341418586_Preliminary_Report_on_Applications_of_Machine_Learning_Techniques_to_the_Nevada_Geothermal_Play_Fairway_Analysis
地圖
- https://www.researchgate.net/publication/347786302_Semantic_Segmentation_Deep_Learning_for_Extracting_Surface_Mine_Extents_from_Historic_Topographic_Maps
礦物
- https://www.researchgate.net/publication/357942198_Mineral_classification_of_lithium-bearing_pegmatites_based_on_laser-induced_breakdown_spectroscopy_Application_of_semi-supervised_learning_to_detect_known_minerals_and_unknown_material
- https://iopscience.iop.org/article/10.1088/1755-1315/1032/1/012046 -> Classifying Minerals using Deep Learning Algorithms
- https://www.researchgate.net/publication/370835450_Predicting_new_mineral_occurrences_and_planetary_analog_environments_via_mineral_association_analysis
- https://www.researchgate.net/publication/361230503_What_is_Mineral_Informatics
自然語言處理
- https://www.researchgate.net/publication/358616133_Chinese_Named_Entity_Recognition_in_the_Geoscience_Domain_Based_on_BERT
- https://www.researchgate.net/publication/339394395_Dictionary-Based_Automated_Information_Extraction_From_Geological_Documents_Using_a_Deep_Learning_Algorithm
- https://www.aclweb.org/anthology/2020.lrec-1.568/ -> Embeddings for Named Entity Recognition in Geoscience Portuguese Literature
- https://www.researchgate.net/publication/359186219_Few-shot_learning_for_name_entity_recognition_in_geological_text_based_on_GeoBERT
- https://www.researchgate.net/publication/333464862_GeoDocA_-_Fast_Analysis_of_Geological_Content_in_Mineral_Exploration_Reports_A_Text_Mining_Approach
- https://www.researchgate.net/publication/366710921_Geological_profile-text_information_association_model_of_mineral_exploration_reports_for_fast_analysis_of_geological_content
- https://www.researchgate.net/publication/330835955_Geoscience_Keyphrase_Extraction_Algorithm_Using_Enhanced_Word_Embedding [UNSEEN]
- https://www.researchgate.net/publication/332997161_GNER_A_Generative_Model_for_Geological_Named_Entity_Recognition_Without_Labeled_Data_Using_Deep_Learning
- https://www.researchgate.net/publication/321850315_Information_extraction_and_knowledge_graph_construction_from_geoscience_literature
- https://www.researchgate.net/publication/365929623_Named_Entity_Annotation_Schema_for_Geological_Literature_Mining_in_the_Domain_of_Porphyry_Copper_Deposits
- https://www.researchgate.net/publication/329621358_Ontology-Based_Enhanced_Word_Embedding_for_Automated_Information_Extraction_from_Geoscience_Reports
- https://www.researchgate.net/publication/379808469_Ontology-driven_relational_data_mapping_for_constructing_a_knowledge_graph_of_porphyry_copper_deposits -> Ontology-driven relational data mapping for constructing a knowledge graph of porphyry copper deposits
- https://www.researchgate.net/publication/327709479_Prospecting_Information_Extraction_by_Text_Mining_Based_on_Convolutional_Neural_Networks-A_Case_Study_of_the_Lala_Copper_Deposit_China
- https://ieeexplore.ieee.org/document/8711400 -> Research and Application on Geoscience Literature Knowledge Discovery Technology
- https://www.researchgate.net/publication/332328315_Text_Mining_to_Facilitate_Domain_Knowledge_Discovery
- https://www.researchgate.net/publication/351238658_Understanding_Ore-Forming_Conditions_using_Machine_Reading_of_Text
- https://www.researchgate.net/publication/359089763_Visual_analytics_and_information_extraction_of_geological_content_for_text-based_mineral_exploration_reports
- https://www.researchgate.net/publication/354754114_What_is_this_article_about_Generative_summarization_with_the_BERT_model_in_the_geosciences_domain
- https://www.slideshare.net/phcleverley/where-text-analytics-meets-geoscience -> Where text analytics meets geoscience
Petrography
- https://www.researchgate.net/publication/335226326_Digital_petrography_Mineralogy_and_porosity_identification_using_machine_learning_algorithms_in_petrographic_thin_section_images
Last edited: 29/09/2020 The below are a collection of works from when I was doing a review
Public Mineral Prospectivity Mapping
概述
- https://www.researchgate.net/publication/331852267_Applying_Spatial_Prospectivity_Mapping_to_Exploration_Targeting_Fundamental_Practical_issues_and_Suggested_Solutions_for_the_Future
- https://www.researchgate.net/publication/284890591_Geochemical_Anomaly_and_Mineral_Prospectivity_Mapping_in_GIS
- https://www.researchgate.net/publication/341472154_Geodata_Science-Based_Mineral_Prospectivity_Mapping_A_Review
- https://www.researchgate.net/publication/275338029_Introduction_to_the_Special_Issue_GIS-based_mineral_potential_modelling_and_geological_data_analyses_for_mineral_exploration
- https://www.researchgate.net/publication/339074334_Introduction_to_the_special_issue_on_spatial_modelling_and_analysis_of_ore-forming_processes_in_mineral_exploration_targeting
- https://www.researchgate.net/publication/317319129_Natural_Resources_Research_Publications_on_Geochemical_Anomaly_and_Mineral_Potential_Mapping_and_Introduction_to_the_Special_Issue_of_Papers_in_These_Fields
- https://www.researchgate.net/publication/46696293_Selection_of_coherent_deposit-type_locations_and_their_application_in_data-driven_mineral_prospectivity_mapping
地球化學
https://www.researchgate.net/publication/375926319_A_paradigm_shift_in_Precambrian_research_driven_by_big_data
https://www.researchgate.net/publication/359447201_A_review_of_machine_learning_in_geochemistry_and_cosmochemistry_Method_improvements_and_applications
- https://jaywen.com/files/He_2022_Applied_Geochemistry.pdf
https://www.researchgate.net/publication/220164381_Application_of_geochemical_zonality_coefficients_in_mineral_prospectivity_mapping
https://www.researchgate.net/publication/238505045_Analysis_and_mapping_of_geochemical_anomalies_using_logratio-transformed_stream_sediment_data_with_censored_values
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022EA002626 -> Comparative Study on Three Autoencoder-Based Deep Learning Algorithms for Geochemical Anomaly Identification
https://www.researchgate.net/publication/373758047_Decision-making_within_geochemical_exploration_data_based_on_spatial_uncertainty_-A_new_insight_and_a_futuristic_review
https://www.researchgate.net/publication/331505001_Deep_learning_and_its_application_in_geochemical_mapping
https://www.researchgate.net/publication/380262759_Factor_analysis_in_residual_soils_of_the_Iberian_Pyrite_Belt_Spain_Comparison_between_raw_data_log_transformation_data_and_compositional_data [UNSEEN]
https://www.researchgate.net/publication/272091723_Geochemical_characteristics_of_mineral_deposits_Implications_for_ore_genesis
https://www.researchgate.net/publication/257189047_Geochemical_mineralization_probability_index_GMPI_A_new_approach_to_generate_enhanced_stream_sediment_geochemical_evidential_map_for_increasing_probability_of_success_in_mineral_potential_mapping
https://www.researchgate.net/publication/333497470_Integration_of_auto-encoder_network_with_density-based_spatial_clustering_for_geochemical_anomaly_detection_for_mineral_exploration
https://www.researchgate.net/publication/319303831_Introduction_to_the_thematic_issue_Analysis_of_exploration_geochemical_data_for_mapping_of_anomalies
https://www.researchgate.net/publication/356722687_Machine_learning-based_prediction_of_trace_element_concentrations_using_data_from_the_Karoo_large_igneous_province_and_its_application_in_prospectivity_mapping#fullTextFileContent
https://www.degruyter.com/document/doi/10.2138/am-2023-9115/html -> Machine learning applied to apatite compositions for determining mineralization potential [UNSEEN]
https://www.researchgate.net/publication/257026525_Primary_geochemical_characteristics_of_mineral_deposits_-_Implications_for_exploration
https://www.researchgate.net/publication/283554338_Recognition_of_geochemical_anomalies_using_a_deep_autoencoder_network
- https://zarmesh.com/wp-content/uploads/2017/04/Recognition-of-geochemical-anomalies-using-a-deep-autoencoder-network.pdf
https://www.researchgate.net/publication/349606557_Robust_Feature_Extraction_for_Geochemical_Anomaly_Recognition_Using_a_Stacked_Convolutional_Denoising_Autoencoder [UNSEEN]
https://www.researchgate.net/publication/375911531_Spatial_Interpolation_Using_Machine_Learning_From_Patterns_and_Regularities_to_Block_Models#fullTextFileContent
https://www.researchgate.net/publication/259716832_Supervised_and_unsupervised_classification_of_near-mine_soil_Geochemistry_and_Geophysics_data
https://www.researchgate.net/publication/277813662_Supervised_Geochemical_Anomaly_Detection_by_Pattern_Recognition
https://www.researchgate.net/publication/249544991_Usefulness_of_stream_order_to_detect_stream_sediment_geochemical_anomalies
https://www.researchgate.net/publication/321275541_Weighting_stream_sediment_geochemical_samples_as_exploration_indicator_of_deposit_-_type
模糊
- https://www.researchgate.net/publication/272170968_A_Comparative_Analysis_of_Weights_of_Evidence_Evidential_Belief_Functions_and_Fuzzy_Logic_for_Mineral_Potential_Mapping_Using_Incomplete_Data_at_the_Scale_of_Investigation
- https://www.researchgate.net/publication/267816279_Fuzzification_of_continuous-value_spatial_evidence_for_mineral_prospectivity_mapping
- https://www.researchgate.net/publication/301635716_Union_score_and_fuzzy_logic_mineral_prospectivity_mapping_using_discretized_and_continuous_spatial_evidence_values
不確定
- https://deliverypdf.ssrn.com/delivery.php?ID=555064031119110002088087068121000096050036019060022069010050000053011056029076002067121000064004002088113115000107115017083105004026015092089005123065040099024112018026013043065104094012124120126039100033055018066074125089104115090100009064122122019003015085069021024027072126106082092110&EXT=pdf&INDEX=TRUE -> Estimating uncertainties in 3-D models of complex fold-and-thrust 2 belts: a case study of the Eastern Alps triangle zone
- https://www.researchgate.net/publication/333339659_Incorporating_conceptual_and_interpretation_uncertainty_to_mineral_prospectivity_modelling
- https://www.researchgate.net/publication/235443307_Managing_uncertainty_in_exploration_targeting
- https://www.researchgate.net/publication/255909185_The_upside_of_uncertainty_Identification_of_lithology_contact_zones_from_airborne_geophysics_and_satellite_data_using_random_forests_and_support_vector_machines
Geospatial Maps
澳洲
- https://www.researchgate.net/publication/334440382_Mapping_iron_oxide_Cu-Au_IOCG_mineral_potential_in_Australia_using_a_knowledge-driven_mineral_systems-based_approach
南澳大利亞
- https://www.researchgate.net/publication/335313790_Prospectivity_modelling_of_the_Olympic_Cu-Au_Province - https://services.sarig.sa.gov.au/raster/ProspectivityModelling/wms?service=wms&version=1.1.1&REQUEST=GetCapabilities
- An assessment of the uranium and geothermal prospectivity of east-central South Australia - https://d28rz98at9flks.cloudfront.net/72666/Rec2011_034.pdf
新台幣
- https://www.researchgate.net/publication/285235798_An_assessment_of_the_uranium_and_geothermal_prospectivity_of_the_southern_Northern_Territory
西澳
- https://www.researchgate.net/publication/273073675_Building_a_machine_learning_classifier_for_iron_ore_prospectivity_in_the_Yilgarn_Craton
- http://dmpbookshop.eruditetechnologies.com.au/product/district-scale-targeting-for-gold-in-the-yilgarn-craton-part-2-of-the-yilgarn-gold-exploration-targeting-atlas.do$55 purchase
- http://dmpbookshop.eruditetechnologies.com.au/product/mineral-prospectivity-of-the-king-leopold-orogen-and-lennard-shelf-analysis-of-potential-field-data-in-the-west-kimberley-region-geographical-product-n14bnzp.do
- http://dmpbookshop.eruditetechnologies.com.au/product/mineral-systems-analysis-of-the-west-musgrave-province-regional-structure-and-prospectivity-modelling-geographical-product-n12dzp.do
- http://dmpbookshop.eruditetechnologies.com.au/product/mineral-systems-analysis-of-the-west-musgrave-province-regional-structure-and-prospectivity-modelling.do $22 purchase
- https://researchdata.edu.au/predictive-mineral-discovery-gold-mineral/1209568?source=suggested_datasets - Predictive mineral discovery in the eastern Yilgarn Craton: an example of district-scale targeting of an orogenic gold mineral system - https://d28rz98at9flks.cloudfront.net/82617/Y4_Gold_Targeting.zip
- http://dmpbookshop.eruditetechnologies.com.au/product/prospectivity-analysis-of-the-halls-creek-orogen-western-australia-using-a-mineral-systems-approach-geographical-product-n15af3zp.do
- https://researchdata.edu.au/prospectivity-analysis-using-063-m436/1424743 - Prospectivity analysis using a mineral systems approach - Capricorn case study project CSIROspectivity systems approach - Capricorn case study project CSIROspectivity systems Capusing aappionion fulions 3.下載）
- http://dmpbookshop.eruditetechnologies.com.au/product/regional-scale-targeting-for-gold-in-the-yilgarn-craton-part-1-of-the-yilgarn-gold-exploration-targeting-atlas.do $55 purchase
- https://www.researchgate.net/publication/263928515_Towards_Australian_metallogenic_maps_through_space_and_time
- https://www.sciencedirect.com/science/article/abs/pii/S0301926810002111 - Yilgarn
巴西
- https://www.researchgate.net/publication/340633563_CATALOG_OF_PROSPECTIVITY_MAPS_OF_SELECTED_AREAS_FROM_BRAZIL
- https://www.researchgate.net/publication/341936771_Modeling_of_Cu-Au_Prospectivity_in_the_Carajas_mineral_province_Brazil_through_Machine_Learning_Dealing_with_Imbalanced_Training_Data
- https://www.researchgate.net/publication/287270273_Nickel_prospective_modelling_using_fuzzy_logic_on_nova_Brasilandia_metasedimentary_belt_Rondonia_Brazil
- https://www.scielo.br/scielo.php?script=sci_arttext&pid=S2317-48892016000200261 - Sao Francisco Craton Nickel
澳洲
- https://www.researchgate.net/publication/248211737_A_continent-wide_study_of_Australia's_uranium_potential
- https://www.researchgate.net/publication/334440382_Mapping_iron_oxide_Cu-Au_IOCG_mineral_potential_in_Australia_using_a_knowledge-driven_mineral_systems-based_approach
- https://researchdata.edu.au/predictive-model-opal-mining-approach/673159/?refer_q=rows=15/sort=score%20desc/class=collection/p=2/q=mineral%20prospectivity%20map/ - Opal
SA
- https://data.gov.au/dataset/ds-ga-a8619169-1c2a-6697-e044-00144fdd4fa6/details?q= -> An assessment of the uranium and geothermal prospectivity of east central South Australia
- https://d28rz98at9flks.cloudfront.net/72666/Rec2011_034.pdf -> An assessment of the uranium and geothermal prospectivity of east-central South Australia
- https://www.pir.sa.gov.au/__data/assets/pdf_file/0011/239636/204581-001_wise_high.pdf - Eastern Gawler - WPA
- http://www.energymining.sa.gov.au/minerals/knowledge_centre/mesa_journal/previous_feature_articles/new_prospectivity_map
- https://catalog.sarig.sa.gov.au/geonetwork/srv/eng/catalog.search#/metadata/e59cd4ba-1a0a-4911-9e6a-58d80576678d - Olympic Domain IOCG Prospectivity model
- https://www.researchgate.net/publication/335313790_Prospectivity_modelling_of_the_Olympic_Cu-Au_Province - https://services.sarig.sa.gov.au/raster/ProspectivityModelling/wms?service=wms&version=1.1.1&REQUEST=GetCapabilities
西澳
- https://www.sciencedirect.com/science/article/abs/pii/S0301926810002111 - Yilgarn Karol Czarnota
- https://www.researchgate.net/publication/229333177_Prospectivity_analysis_of_the_Plutonic_Marymia_Greenstone_Belt_Western_Australia
- https://www.researchgate.net/publication/280039091_Mineral_systems_approach_applied_to_GIS-based_2D-prospectivity_modelling_of_geological_regions_Insights_from_Western_Australia
- https://www.researchgate.net/publication/351238658_Understanding_Ore-Forming_Conditions_using_Machine_Reading_of_Text
新台幣
- https://www.researchgate.net/publication/285235798_An_assessment_of_the_uranium_and_geothermal_prospectivity_of_the_southern_Northern_Territory
- https://www.researchgate.net/publication/342352173_Modelling_gold_potential_in_the_Granites-Tanami_Orogen_NT_Australia_A_comparative_study_using_continuous_and_data-driven_techniques
新南威爾斯州
- https://www.resourcesandgeoscience.nsw.gov.au/miners-and-explorers/geoscience-information/projects/mineral-potential-mapping#_southern-_new-_england-_orogen-mineral-potential
- https://www.smedg.org.au/GSNSW_2019_Blevin.pdf - Eastern Lachlan Orogen
- https://www.researchgate.net/publication/265915602_Comparing_prospectivity_modelling_results_and_past_exploration_data_A_case_study_of_porphyry_Cu-Au_mineral_systems_in_the_Macquarie_Arc_Lachlan_Fold_Belt_New_South_Wales
巴西
- https://www.researchgate.net/publication/340633563_CATALOG_OF_PROSPECTIVITY_MAPS_OF_SELECTED_AREAS_FROM_BRAZIL
- https://www.researchgate.net/publication/340633739_MINERAL_POTENTIAL_AND_OPORTUNITIES_FOR_THE_EXPLORATION_OF_NEW_GEOLOGICAL_GROUNDS_IN_BRAZIL
- https://www.semanticscholar.org/paper/Mineral-Potential-Mapping-for-Orogenic-Gold-in-the-Silva-Silva/a23a9ce4da48863da876758afa9e1d2723088853
- https://www.scielo.br/scielo.php?script=sci_arttext&pid=S2317-48892016000200261 - Supergene nickel deposits in outhwestern Sao Francisco Carton, Brazil
卡拉哈斯
- https://www.researchgate.net/publication/258466504_Self-Organizing_Maps_A_Data_Mining_Tool_for_the_Analysis_of_Airborne_Geophysical_Data_Collected_over_the_Brazilian_Amazon
- https://www.researchgate.net/publication/258647519_Semiautomated_geologic_mapping_using_self-organizing_maps_and_airborne_geophysics_in_the_Brazilian_Amazon
- https://www.researchgate.net/publication/235443304_GIS-Based_prospectivity_mapping_for_orogenic_gold_A_case_study_from_the_Andorinhas_region_Brasil
- https://www.researchgate.net/publication/341936771_Modeling_of_Cu-Au_Prospectivity_in_the_Carajas_mineral_province_Brazil_through_Machine_Learning_Dealing_with_Imbalanced_Training_Data
- https://www.researchgate.net/publication/332031621_Predictive_lithological_mapping_through_machine_learning_methods_a_case_study_in_the_Cinzento_Lineament_Carajas_Province_Brazil
- https://www.researchgate.net/publication/340633659_Copper-gold_favorability_in_the_Cinzento_Shear_Zone_Carajas_Mineral_Province
- https://www.researchgate.net/publication/329477409_Favorability_potential_for_IOCG_type_deposits_in_the_Riacho_do_Pontal_Belt_New_insights_for_identifying_prospects_of_IOCG-type_deposits_in_NE_Brazil
- https://www.researchgate.net/publication/339453836_Uranium_anomalies_detection_through_Random_Forest_regression
- https://d1wqtxts1xzle7.cloudfront.net/48145419/Artificial_neural_networks_applied_to_mi20160818-5365-odv4na.pdf?1471522188=&response-content-disposition=inline%3B+filename%3DArtificial_neural_networks_applied_to_mi.pdf&Expires=1593477539&Signature=DNmSxKogrD54dE4LX~8DT4K7vV0ZGcf8Q2RRfXEPsCc8PGiBrbeBpy4NVQdCiENLz-YfSzVGk6LI8k5MEGxR~qwnUn9ISLHDuIau6VqBFSEA29jMixCbvQM6hbkUJKQlli-AuSPUV23TsSk76kB6amDYtwNHmBnUPzTQGZLj2XkzJza9PA-7W2-VrPQKHNPxJp3z8J0mPq4rhmHZLaFMMSL6QMpK5qpvSqi6Znx-kIhCprlyYfODisq0unOIwnEQstiMf2RnB6gPmGOodhNlLsSr01e7TvtvFDBOQvhhooeDeQrvkINN4DJjAIIrbrcQ8B2b-ATQS0a3QQe93h-VFA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA - Leite, EPL; de Souza Filho, CR Artificial neural networks applied to mineral potential mapping for copper-gold mineralizations in the Carajás Mineral Province, Brazil. Geoph. Prosp. 2009, 57, 1049–1065.
- https://link-springer-com.access.library.unisa.edu.au/content/pdf/10.1007/s11053-015-9263-2.pdf - A Comparative Analysis of Weights of Evidence, Evidential Belief Functions, and Fuzzy Logic for Mineral Potential Mapping Using Incomplete Data at the Scale of Investigation
- https://library.seg.org/doi/abs/10.1190/sbgf2011-245 - Gold Prospectivity Mapping of Andorinhas Greenstone Belt, Para
Gurupi
- https://www.researchgate.net/publication/312220651_Predictive_Mapping_of_Prospectivity_in_the_Gurupi_Orogenic_Gold_Belt_North-Northeast_Brazil_An_Example_of_District-Scale_Mineral_System_Approach_to_Exploration_Targeting
澳洲
- https://www.researchgate.net/publication/260107484_Unsupervised_clustering_of_continental-scale_geophysical_and_geochemical_data_using_Self-Organising_Maps
- https://www.researchgate.net/publication/332263305_A_speedy_update_on_machine_learning_applied_to_bedrock_mapping_using_geochemistry_or_geophysics_examples_from_the_Pacific_Rim_and_nearby
- https://www.researchgate.net/publication/317312520_Catchment-based_gold_prospectivity_analysis_combining_geochemical_geophysical_and_geological_data_across_northern_Australia
- https://www.researchgate.net/publication/326571155_Continental-scale_mineral_prospectivity_assessment_using_the_National_Geochemical_Survey_of_Australia_NGSA_dataset
- https://www.researchgate.net/publication/334440382_Mapping_iron_oxide_Cu-Au_IOCG_mineral_potential_in_Australia_using_a_knowledge-driven_mineral_systems-based_approach
- https://www.researchgate.net/publication/282189370_Uranium_Prospectivity_Mapping_Across_the_Australian_Continent_via_Unsupervised_Cluster_Analysis_of_Integrated_Remote_Sensing_Data
南澳大利亞
- https://www.researchgate.net/publication/335313790_Prospectivity_modelling_of_the_Olympic_Cu-Au_Province - https://services.sarig.sa.gov.au/raster/ProspectivityModelling/wms?service=wms&version=1.1.1&REQUEST=GetCapabilities
昆士蘭州
- https://www.researchgate.net/publication/317312520_Catchment-based_gold_prospectivity_analysis_combining_geochemical_geophysical_and_geological_data_across_northern_Australia
- https://www.researchgate.net/publication/252707107_GIS-based_epithermal_copper_prospectivity_mapping_of_the_Mt_Isa_Inlier_Australia_Implications_for_exploration_targeting
- https://www.researchgate.net/publication/222211452_Predictive_modelling_of_prospectivity_for_Pb-Zn_deposits_in_the_Lawn_Hill_Region_Queensland_Australia
新南威爾斯州
- https://www.researchgate.net/publication/336349643_MINERAL_POTENTIAL_MAPPING_AS_A_STRATEGIC_PLANNING_TOOL_IN_THE_EASTERN_LACHLAN_OROGEN_NSW
- https://www.publish.csiro.au/ex/pdf/ASEG2013ab236 - Mineral prospectivity analysis of the Wagga–Omeo belt in NSW
- https://www.researchgate.net/publication/329761040_NSW_Zone_54_Mineral_Systems_Mineral_Potential_Report
- https://www.researchgate.net/publication/337569823_Practical_Implementation_of_Random_Forest-Based_Mineral_Potential_Mapping_for_Porphyry_Cu-Au_Mineralization_in_the_Eastern_Lachlan_Orogen_NSW_Australia
- https://www.researchgate.net/publication/333551776_Translating_expressions_of_intrusion-related_mineral_systems_into_mappable_spatial_proxies_for_mineral_potential_mapping_Case_studies_from_the_Southern_New_England_Orogen_Australia
塔斯馬尼亞
- https://www.researchgate.net/publication/262380025_Mapping_geology_and_volcanic-hosted_massive_sulfide_alteration_in_the_Hellyer-Mt_Charter_region_Tasmania_using_Random_Forests_TM_and_Self-Organising_Maps
維多利亞
- https://www.researchgate.net/publication/323856713_Lithological_mapping_using_Random_Forests_applied_to_geophysical_and_remote_sensing_data_a_demonstration_study_from_the_Eastern_Goldfields_of_Australia
- https://publications.csiro.au/publications/#publication/PIcsiro:EP123339/SQmineral%20prospectivity/RP1/RS50/RORECENT/STsearch-by-keyword/LISEA/RI16/RT26 [nickel]
- https://www.researchgate.net/publication/257026553_Regional_prospectivity_analysis_for_hydrothermal-remobilised_nickel_mineral_systems_in_western_Victoria_Australia
西澳大利亞州
- https://www.researchgate.net/publication/274714146_Reducing_subjectivity_in_multi-commodity_mineral_prospectivity_analyses_Modelling_the_west_Kimberley_Australia
- https://www.researchgate.net/publication/319013132_Identifying_mineral_prospectivity_using_3D_magnetotelluric_potential_field_and_geological_data_in_the_east_Kimberley_Australia
- https://www.researchgate.net/publication/280930127_Regional-scale_targeting_for_gold_in_the_Yilgarn_Craton_Part_1_of_the_Yilgarn_Gold_Exploration_Targeting_Atlas
- https://www.researchgate.net/publication/279533541_District-scale_targeting_for_gold_in_the_Yilgarn_Craton_Part_2_of_the_Yilgarn_Gold_Exploration_Targeting_Atlas
- https://www.researchgate.net/publication/257026568_Exploration_targeting_for_orogenic_gold_deposits_in_the_Granites-Tanami_Orogen_Mineral_system_analysis_targeting_model_and_prospectivity_analysis
- https://www.researchgate.net/publication/280039091_Mineral_systems_approach_applied_to_GIS-based_2D-prospectivity_modelling_of_geological_regions_Insights_from_Western_Australia (the West Arunta Orogen, West Musgrave Orogen and Gascoyne Province - http://dmpbookshop.eruditetechnologies.com.au/product/mineral-systems-analysis-of-the-west-musgrave-province-regional-structure-and-prospectivity-modelling.do
- https://reader.elsevier.com/reader/sd/pii/S0169136810000417? - token=9FD1C06A25E7ECC0C384C0ECF976E4BC9C36047C53CEED08066811979A640E89DD94C49510D1B500C6FF5E69982E018E Prospectivity analysis of the Plutonic Marymia Greenstone Belt, Western Australia
- https://research-repository.uwa.edu.au/en/publications/exploration-targeting-for-orogenic-gold-deposits-in-the-granites- - Tanami orogen
- https://www.researchgate.net/publication/332631130_Fuzzy_inference_systems_for_prospectivity_modeling_of_mineral_systems_and_a_case-study_for_prospectivity_mapping_of_surficial_Uranium_in_Yeelirrie_Area_Western_Australia_Ore_Geology_Reviews_71_839-852Tasmania
- https://publications.csiro.au/rpr/download?pid=csiro:EP102133&dsid=DS3 [nickel]
Endowment Modelling
- https://www.researchgate.net/publication/248211962_A_new_method_for_spatial_centrographic_analysis_of_mineral_deposit_clusters
- https://www.researchgate.net/publication/275620329_A_Time-Series_Audit_of_Zipf's_Law_as_a_Measure_of_Terrane_Endowment_and_Maturity_in_Mineral_Exploration
- https://www.researchgate.net/publication/341087909_Assessing_the_variability_of_expert_estimates_in_the_USGS_Three-part_Mineral_Resource_Assessment_Methodology_A_call_for_increased_skill_diversity_and_scenario-based_training
- https://github.com/iagoslc/ZipfsLaw_Quadrilatero_Ferrifero
- https://www.researchgate.net/publication/222834436_Controls_on_mineral_deposit_occurrence_inferred_from_analysis_of_their_spatial_pattern_and_spatial_association_with_geological_features
- https://www.researchgate.net/publication/229792860_from_predictive_mapping_mapping_mineral_prospectivity_to_quantitative_quantitative_estimative_of_number_of_of_of_undiscovered_prospects
- https://www.researchgate.net/publication/330994502_Global_Grade-and-Tonnage_Modeling_of_Uranium_deposits
- https://pubs.geoscienceworld.org/segweb/economicgeology/article-abstract/103/4/829/127993/Linking-Mineral-Deposit-Models-to-Quantitative?redirectedFrom=fulltext
- https://www.researchgate.net/publication/238365283_Metal_endowment_of_cratons_terranes_and_districts_Insights_from_a_quantitative_analysis_of_regions_with_giant_and_super-giant_deposits
- https://www.researchgate.net/publication/308778798_Spatial_analysis_of_mineral_deposit_distribution_A_review_of_methods_and_implications_for_structural_controls_on_iron_oxide-copper-gold_mineralization_in_Carajas_Brazil
- https://www.researchgate.net/publication/229347041_Predictive_mapping_of_prospectivity_and_quantitative_estimation_of_undiscovered_VMS_deposits_in_Skellefte_district_Sweden
- https://www.researchgate.net/publication/342405763_Predicting_grade-tonnage_characteristics_of_undiscovered_mineralisation_application_of_the_USGS_Three-part_Undiscovered_Mineral_Resource_Assessment_to_the_Sandstone_Greenstone_Belt_of_the_Yilgarn_Bloc
- https://www.sciencedirect.com/science/article/pii/S0169136810000685
- https://www.researchgate.net/publication/240301743_Spatial_statistical_analysis_of_the_distribution_of_komatiite-hosted_nickel_sulfide_deposits_in_the_Kalgoorlie_terrane_Western_Australia_Clustered_or_Not
World Models
- https://www.researchgate.net/publication/331283650_Archean_crust_and_metallogenic_zones_in_the_Amazonian_Craton_sensed_by_satellite_gravity_data
- https://eartharxiv.org/2kjvc/ -> Global distribution of sediment-hosted metals controlled by craton edge stability
- https://www.researchgate.net/post/Is_it_possible_to_derive_free_air_anomaly_or_bouguer_anomaly_from_gravity_disturbance_data
- https://www.researchgate.net/publication/325344128_The_role_of_basement_control_in_Iron_Oxide-Copper-Gold_mineral_systems_revealed_by_satellite_gravity_models
- https://www.researchgate.net/publication/331428028_Supplementary_Material_for_the_paper_Archean_crust_and_metallogenic_zones_in_the_Amazonian_Craton_sensed_by_satellite_gravity_data
- https://www.leouieda.com/pdf/use-the-disturbance.pdf
- https://www.leouieda.com/papers/use-the-disturbance.html
Financial Forecasting
- https://www.researchgate.net/publication/317137060_Forecasting_copper_prices_by_decision_tree_learning
- https://www.researchgate.net/publication/4874824_Mine_Size_and_the_Structure_of_Costs
Agent based Modelling
- https://mpra.ub.uni-muenchen.de/62159/ -> Mineral exploration as a game of chance [Agent Based Modelling]
光譜分解
- Overviews and examples, with some focus on neural network approaches.
神經網路
- https://www.researchgate.net/publication/224180646_A_neural_network_approach_for_pixel_unmixing_in_hyperspectral_data
- https://www.researchgate.net/publication/340690859_A_Supervised_Nonlinear_Spectral_Unmixing_Method_by_Means_of_Neural_Networks
- https://www.researchgate.net/publication/326205017_Classification_of_Hyperspectral_Data_Using_a_Multi-Channel_Convolutional_Neural_Network
- https://www.researchgate.net/publication/339062151_Classification_of_small-scale_hyperspectral_images_with_multi-source_deep_transfer_learning
- https://www.researchgate.net/publication/331824337_Comparative_Analysis_of_Unmixing_Algorithms_Using_Synthetic_Hyperspectral_Data
- https://www.researchgate.net/publication/335501086_Convolutional_Autoencoder_For_Spatial-Spectral_Hyperspectral_Unmixing
- https://www.researchgate.net/publication/341501560_Convolutional_Autoencoder_for_Spectral-Spatial_Hyperspectral_Unmixing
- https://www.researchgate.net/publication/333906204_Deep_convolutional_neural_networks_for_land-cover_classification_with_Sentinel-2_images
- https://www.researchgate.net/publication/356711693_Deep-learning-based_latent_space_encoding_for_spectral_unmixing_of_geological_materials
- https://www.researchgate.net/publication/331505001_Deep_learning_and_its_application_in_geochemical_mapping
- https://www.researchgate.net/publication/332696102_Deep_Learning_for_Classification_of_Hyperspectral_Data_A_Comparative_Review
- https://www.researchgate.net/publication/336889271_Deep_Learning_for_Hyperspectral_Image_Classification_An_Overview
- https://www.researchgate.net/publication/327995228_Deep_Spectral_Convolution_Network_for_Hyperspectral_Unmixing
- https://ieeexplore.ieee.org/abstract/document/10580951 -> Exploring Hybrid Contrastive Learning and Scene-to-Label Information for Multilabel Remote Sensing Image Classification [UNSEEN]
- https://www.researchgate.net/publication/356393038_Generalized_Unsupervised_Clustering_of_Hyperspectral_Images_of_Geological_Targets_in_the_Near_Infrared
- https://ieeexplore.ieee.org/abstract/document/10588073 -> Hyperspectral Image Classification Using Spatial and Spectral Features Based on Deep Learning [UNSEEN]
- https://www.researchgate.net/publication/333301728_Hyperspectral_Image_Classification_Method_Based_on_CNN_Architecture_Embedding_With_Hashing_Semantic_Feature
- https://www.researchgate.net/publication/323950012_Hyperspectral_Unmixing_Using_A_Neural_Network_Autoencoder
- https://www.researchgate.net/publication/339657313_Hyperspectral_unmixing_using_deep_convolutional_autoencoder
- https://www.researchgate.net/publication/339066136_Hyperspectral_Unmixing_Using_Deep_Convolutional_Autoencoders_in_a_Supervised_Scenario
- https://www.researchgate.net/publication/335878933_LITHOLOGICAL_CLASSIFICATION_USING_MULTI-SENSOR_DATA_AND_CONVOLUTIONAL_NEURAL_NETWORKS
- https://ieeexplore.ieee.org/abstract/document/10551851 -> MSNet: Self-Supervised Multiscale Network With Enhanced Separation Training for Hyperspectral Anomaly Detection
- https://www.researchgate.net/publication/331794887_Nonlinear_Unmixing_of_Hyperspectral_Data_via_Deep_Autoencoder_Networks
- https://ieeexplore.ieee.org/abstract/document/10534107 -> ReSC-net: Hyperspectral Image Classification Based on Attention-Enhanced Residual Module and Spatial-Channel Attention
- https://www.researchgate.net/publication/340961027_Recent_Advances_in_Hyperspectral_Unmixing_Using_Sparse_Techniques_and_Deep_Learning
- https://www.researchgate.net/publication/330272600_Semisupervised_Stacked_Autoencoder_With_Cotraining_for_Hyperspectral_Image_Classification
- https://www.researchgate.net/publication/336097421_Spatial-Spectral_Hyperspectral_Unmixing_Using_Multitask_Learning
- https://www.researchgate.net/publication/312355586_Spectral-Spatial_Classification_of_Hyperspectral_Imagery_with_3D_Convolutional_Neural_Network
- https://meetingorganizer.copernicus.org/EGU2020/EGU2020-10719.html -> Sentinel-2 as a tool for mapping iron-bearing alteration minerals: a case study from the Iberian Pyrite Belt (Southern Spain)
- https://www.researchgate.net/publication/334058881_SSDC-DenseNet_A_Cost-Effective_End-to-End_Spectral-Spatial_Dual-Channel_Dense_Network_for_Hyperspectral_Image_Classification
- https://www.researchgate.net/publication/333497470_Integration_of_auto-encoder_network_with_density-based_spatial_clustering_for_geochemical_anomaly_detection_for_mineral_exploration
- https://www.sciencedirect.com/science/article/pii/S0009281924000473 -> Geochemical characteristics and mapping of Reşadiye (Tokat-Türkiye) bentonite deposits using machine learning and sub-pixel mixture algorithms
一般的
- https://www.sciencedirect.com/science/article/pii/S0273117724004861?dgcid=rss_sd_all -> Optimization of machine learning algorithms for remote alteration mapping
- https://www.researchgate.net/publication/337841253_A_solar_optical_hyperspectral_library_of_rare_earth-bearing_minerals_rare_earth_oxides_copper-bearing_minerals_and_Apliki_mine_surface_samples
- https://ieeexplore.ieee.org/document/10536904 -> A Reversible Generative Network for Hyperspectral Unmixing With Spectral Variability
- https://www.researchgate.net/publication/3204295_Abundance_Estimation_of_Spectrally_Similar_Minerals_by_Using_Derivative_Spectra_in_Simulated_Annealing
- https://www.researchgate.net/publication/338371376_Accuracy_assessment_of_hydrothermal_mineral_maps_derived_from_ASTER_images
- https://www.researchgate.net/publication/337790490_Analysis_of_Most_Significant_Bands_and_Band_Ratios_for_Discrimination_of_Hydrothermal_Alteration_Minerals
- https://www.researchgate.net/project/Deep-Learning-for-Remote-Sensing-2
- https://ieeexplore.ieee.org/abstract/document/10589462 -> Deep Spectral Spatial Feature Enhancement through Transformer for Hyperspectral Image Classification
- https://www.researchgate.net/publication/331876006_Fusion_of_Landsat_and_Worldview_Images
- https://www.researchgate.net/publication/259096595_Geological_mapping_using_remote_sensing_data_A_comparison_of_five_machine_learning_algorithms_their_response_to_variations_in_the_spatial_distribution_of_training_data_and_the_use_of_explicit_spatial_
- https://www.researchgate.net/publication/341802637_Improved_k-means_and_spectral_matching_for_hyperspectral_mineral_mapping
- https://www.researchgate.net/publication/272565561_Integration_and_Analysis_of_ASTER_and_IKONOS_Images_for_the_Identification_of_Hydrothermally-_Altered_Mineral_Exploration_Sites
- https://www.researchgate.net/publication/236271149_Multi-_and_hyperspectral_geologic_remote_sensing_A_review_GRSG_Member_News
- https://www.researchgate.net/publication/220492175_Multi-and_Hyperspectral_geologic_remote_sensing_A_review
- https://www.sciencedirect.com/science/article/pii/S1574954124001572 -> Rapid estimation of soil Mn content by machine learning and soil spectra in large-scale
- https://www.researchgate.net/publication/342184377_remotesensing-12-01239-v2_1
- https://www.researchgate.net/project/Remote-sensing-exploration-of-critical-mineral-deposits
- https://www.researchgate.net/project/Sentinel-2-MSI-for-geological-remote-sensing
- https://www.researchgate.net/publication/323808118_Thermal_infrared_multispectral_remote_sensing_of_lithology_and_mineralogy_based_on_spectral_properties_of_materials
- https://www.researchgate.net/publication/340505978_Unsupervised_and_Supervised_Feature_Extraction_Methods_for_Hyperspectral_Images_Based_on_Mixtures_of_Factor_Analyzers
非洲
- https://www.researchgate.net/publication/235443308_Application_of_remote_sensing_and_GIS_mapping_to_Quaternary_to_recent_surficial_sediments_of_the_Central_Uranium_district_Namibia
- https://www.researchgate.net/publication/342373512_Geological_mapping_using_Random_Forests_applied_to_Remote_Sensing_data_a_demonstration_study_from_Msaidira-Souk_Al_Had_Sidi_Ifni_inlier_Western_Anti-Atlas_Morocco
- https://www.researchgate.net/publication/340534611_Identifying_high_potential_zones_of_gold_mineralization_in_a_sub-tropical_region_using_Landsat-8_and_ASTER_remote_sensing_data_a_case_study_of_the_Ngoura-Colomines_goldfield_Eastern_Cameroon
- https://www.researchgate.net/publication/342162988_Lithological_and_alteration_mineral_mapping_for_alluvial_gold_exploration_in_the_south_east_of_Birao_area_Central_African_Republic_using_Landsat-8_Operational_Land_Imager_OLI_data
- https://www.researchgate.net/publication/329193841_Mapping_Copper_Mineralisation_using_EO-1_Hyperion_Data_Fusion_with_Landsat_8_OLI_and_Sentinel-2A_in_Moroccan_Anti_Atlas
- https://www.researchgate.net/publication/230918249_SPECTRAL_REMOTE_SENSING_OF_HYDROTHERMAL_ALTERATION_ASSOCIATED_WITH_VOLCANOGENIC_MASSIVE_SULPHIDE_DEPOSITS_GOROB-HOPE_AREA_NAMIBIA
- https://www.researchgate.net/publication/337304180_The_application_of_day_and_night_time_ASTER_satellite_imagery_for_geothermal_and_mineral_mapping_in_East_Africa
- https://www.researchgate.net/publication/336823002_Towards_Multiscale_and_Multisource_Remote_Sensing_Mineral_Exploration_Using_RPAS_A_Case_Study_in_the_Lofdal_Carbonatite-Hosted_REE_Deposit_Namibia
- https://www.researchgate.net/publication/338296843_Use_of_the_Sentinel-2A_Multispectral_Image_for_Litho-Structural_and_Alteration_Mapping_in_Al_Glo'a_Map_Sheet_150000_Bou_Azzer-El_Graara_Inlier_Central_Anti-Atlas_Morocco
巴西
- https://www.researchgate.net/publication/287950835_Altimetric_and_aeromagnetometric_data_fusion_as_a_tool_of_geological_interpretation_the_example_of_the_Carajas_Mineral_Province_PA
- https://www.researchgate.net/publication/237222985_Analise_e_integracao_de_dados_do_SAR-R99B_com_dados_de_sensoriamento_remoto_optico_e_dados_aerogeofisicos_na_regiao_dos_depositos_de_oxido_de_Fe-Cu-Au_tipo_Sossego_e_118_na_Provincia_Mineral_de_Caraja
- https://www.researchgate.net/publication/327503453_Comparison_of_Altered_Mineral_Information_Extracted_from_ETM_ASTER_and_Hyperion_data_in_Aguas_Claras_Iron_Ore_Brazil
- https://www.researchgate.net/publication/251743903_Enhancement_Of_Landsat_Thematic_Mapper_Imagery_For_Mineral_Prospecting_In_Weathered_And_Vegetated_Terrain_In_SE_Brazil
- https://www.researchgate.net/publication/228854234_Hyperspectral_Data_Processing_For_Mineral_Mapping_Using_AVIRIS_1995_Data_in_Alto_Paraiso_de_Goias_Central_Brazil
- https://www.researchgate.net/publication/326612136_Mapping_Mining_Areas_in_the_Brazilian_Amazon_Using_MSISentinel-2_Imagery_2017
- https://www.researchgate.net/publication/242188704_MINERALOGICAL_CHARACTERIZATION_AND_MAPPING_USING_REFLECTANCE_SPECTROSCOPY_AN_EXPERIMENT_AT_ALTO_DO_GIZ_PEGMATITE_IN_THE_SOUTH_PORTION_OF_BORBOREMA_PEGMATITE_PROVINCE_BPP_NORTHEASTERN_BRAZIL
中國
- https://www.researchgate.net/publication/338355143_A_comprehensive_scheme_for_lithological_mapping_using_Sentinel-2A_and_ASTER_GDEM_in_weathered_and_vegetated_coastal_zone_Southern_China
- https://www.researchgate.net/publication/332957713_Data_mining_of_the_best_spectral_indices_for_geochemical_anomalies_of_copper_A_study_in_the_northwestern_Junggar_region_Xinjiang
- https://www.researchgate.net/publication/380287318_Machine_learning_model_for_deep_exploration_Utilizing_short_wavelength_infrared_SWIR_of_hydrothermal_alteration_minerals_in_the_Qianchen_gold_deposit_Jiaodong_Peninsula_Eastern_China
- https://www.researchgate.net/publication/304906898_Remote_sensing_and_GIS_prospectivity_mapping_for_magmatic-hydrothermal_base-_and_precious-metal_deposits_in_the_Honghai_district_China
格陵蘭
- https://www.researchgate.net/publication/326655551_Application_of_Multi-Sensor_Satellite_Data_for_Exploration_of_Zn-Pb_Sulfide_Mineralization_in_the_Franklinian_Basin_North_Greenland
- https://www.researchgate.net/publication/337512735_Fusion_of_DPCA_and_ICA_algorithms_for_mineral_detection_using_Landsat-8_spectral_bands
- https://www.researchgate.net/publication/336684298_Landsat-8_Advanced_Spaceborne_Thermal_Emission_and_Reflection_Radiometer_and_WorldView-3_Multispectral_Satellite_Imagery_for_Prospecting_Copper-Gold_Mineralization_in_the_Northeastern_Inglefield_Mobil
印度
- https://www.researchgate.net/publication/337649256_Automated_lithological_mapping_by_integrating_spectral_enhancement_techniques_and_machine_learning_algorithms_using_AVIRIS-NG_hyperspectral_data_in_Gold-bearing_granite-greenstone_rocks_in_Hutti_India
- https://www.researchgate.net/publication/333816841_Integrated_application_of_AVIRIS-NG_and_Sentinel-2A_dataset_in_altered_mineral_abundance_mapping_A_case_study_from_Jahazpur_area_Rajasthan
- https://www.researchgate.net/publication/339631389_Identification_and_characterization_of_hydrothermally_altered_minerals_using_surface_and_space-based_reflectance_spectroscopy_in_parts_of_south-eastern_Rajasthan_India
- https://www.researchgate.net/publication/338116272_Potential_Use_of_ASTER_Derived_Emissivity_Thermal_Inertia_and_Albedo_Image_for_Discriminating_Different_Rock_Types_of_Aravalli_Group_of_Rocks_Rajasthan
伊朗
- https://www.researchgate.net/publication/338336181_A_Remote_Sensing-Based_Application_of_Bayesian_Networks_for_Epithermal_Gold_Potential_Mapping_in_Ahar-Arasbaran_Area_NW_Iran
- https://www.researchgate.net/publication/338371376_Accuracy_assessment_of_hydrothermal_mineral_maps_derived_from_ASTER_images
- https://www.researchgate.net/publication/340606566_Application_of_Landsat-8_Sentinel-2_ASTER_and_WorldView-3_Spectral_Imagery_for_Exploration_of_Carbonate-Hosted_Pb-Zn_Deposits_in_the_Central_Iranian_Terrane_CIT
- https://www.researchgate.net/publication/331428927_Comparison_of_Different_Algorithms_to_Map_Hydrothermal_Alteration_Zones_Using_ASTER_Remote_Sensing_Data_for_Polymetallic_Vein-Type_Ore_Exploration_Toroud-Chahshirin_Magmatic_Belt_TCMB_North_Iran
- https://www.researchgate.net/publication/327832371_Band_Ratios_Matrix_Transformation_BRMT_A_Sedimentary_Lithology_Mapping_Approach_Using_ASTER_Satellite_Sensor
- https://www.researchgate.net/publication/331314687_Lithological_mapping_in_Sangan_region_in_Northeast_Iran_using_ASTER_satellite_data_and_image_processing_methods
- https://www.researchgate.net/publication/330774780_Mapping_hydrothermal_alteration_zones_and_lineaments_associated_with_orogenic_gold_mineralization_using_ASTER_data_A_case_study_from_the_Sanandaj-Sirjan_Zone_Iran
- https://www.researchgate.net/publication/380812370_Optimization_of_machine_learning_algorithms_for_remote_alteration_mapping
- https://www.researchgate.net/publication/362620968_Spatial_mapping_of_hydrothermal_alterations_and_structural_features_for_gold_and_cassiterite_exploration
秘魯
- https://www.researchgate.net/publication/271714561_Geology_and_Hydrothermal_Alteration_of_the_Chapi_Chiara_Prospect_and_Nearby_Targets_Southern_Peru_Using_ASTER_Data_and_Reflectance_Spectroscopy
- https://www.researchgate.net/publication/317141295_Hyperspectral_remote_sensing_applied_to_mineral_exploration_in_southern_Peru_A_multiple_data_integration_approach_in_the_Chapi_Chiara_gold_prospect
西班牙
- https://www.researchgate.net/publication/233039694_Geological_mapping_using_Landsat_Thematic_Mapper_imagery_in_Almeria_Province_south-east_Spain
- https://www.researchgate.net/publication/263542786_WEIGHTS_DERIVED_FROM_HYPERSPECTRAL_DATA_TO_FACILITATE_AN_OPTIMAL_FIELD_SAMPLING_SCHEME_FOR_POTENTIAL_MINERALS
其他
https://www.researchgate.net/publication/341611032_ASTER_spectral_band_ratios_for_lithological_mapping_A_case_study_for_measuring_geological_offset_along_the_Erkenek_Segment_of_the_East_Anatolian_Fault_Zone_Turkey
https://www.researchgate.net/publication/229383008_Hydrothermal_Alteration_Mapping_at_Bodie_California_using_AVIRIS_Hyperspectral_Data
https://www.researchgate.net/publication/332737573_Identification_of_alteration_zones_using_a_Landsat_8_image_of_densely_vegetated_areas_of_the_Wayang_Windu_Geothermal_field_West_Java_Indonesia
https://www.researchgate.net/publication/325137721_Interpretation_of_surface_geochemical_data_and_integration_with_geological_maps_and_Landsat-TM_images_for_mineral_exploration_from_a_portion_of_the_precambrian_of_Uruguay
https://www.researchgate.net/publication/336684298_Landsat-8_Advanced_Spaceborne_Thermal_Emission_and_Reflection_Radiometer_and_WorldView-3_Multispectral_Satellite_Imagery_for_Prospecting_Copper-Gold_Mineralization_in_the_Northeastern_Inglefield_Mobil
https://www.researchgate.net/publication/304036250_Mineral_Exploration_for_Epithermal_Gold_in_Northern_Patagonia_Argentina_From_Regional-_to_Deposit-Scale_Prospecting_Using_Landsat_TM_and_Terra_ASTER
https://www.researchgate.net/publication/340652300_New_logical_operator_algorithms_for_mapping_of_hydrothermally_altered_rocks_using_ASTER_data_A_case_study_from_central_Turkey
https://www.researchgate.net/publication/324938267_Regional_geology_mapping_using_satellite-based_remote_sensing_approach_in_Northern_Victoria_Land_Antarctica
https://www.researchgate.net/publication/379960654_From_sensor_fusion_to_knowledge_distillation_in_collaborative_LIBS_and_hyperspectral_imaging_for_mineral_identification
自然語言處理
- https://ieeexplore.ieee.org/abstract/document/10544642 -> Assessing named entity recognition efficacy using diverse geoscience datasets [UNSEEN]
- https://link.springer.com/article/10.1007/s12371-024-01011-2 -> Can AI Get a Degree in Geoscience?針對地球科學訓練的基於 GPT 的人工智慧系統的效能分析 (GeologyOracle)
- https://www.researchgate.net/publication/376671309_Enhancing_knowledge_discovery_from_unstructured_data_using_a_deep_learning_approach_to_support_subsurface_modeling_predictions
- https://www.mdpi.com/2220-9964/13/7/260 -> Extracting Geoscientific Dataset Names from the Literature Based on the Hierarchical Temporal Memory Model
- https://www.sciencedirect.com/science/article/pii/S0169136824002154 -> Three-dimensional mineral prospectivity mapping based on natural language processing and random forests: A case study of the Xiyu diamond deposit, China
法學碩士
- https://arxiv.org/pdf/2401.16822 - EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain
- https://link.springer.com/article/10.1007/s12371-024-01011-2 -> Can AI Get a Degree in Geoscience?針對地球科學訓練的基於 GPT 的人工智慧系統的效能分析 (GeologyOracle)
- Geology Oracle web prototype - https://geologyoracle.com/ask-the-geologyoracle/
General-Interest
- https://arxiv.org/abs/2404.05746v1 -> Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
- https://www.researchgate.net/publication/384137154_Guidelines_for_Sensitivity_Analyses_in_Process_Simulations_for_Solid_Earth_Geosciences
- https://www.mdpi.com/1660-4601/18/18/9752 -> Learning and Expertise in Mineral Exploration Decision-Making: An Ecological Dynamics Perspective
- https://www.sciencedirect.com/science/article/pii/S2214629624001476 -> Mapping critical minerals projects and their intersection with Indigenous peoples' land rights in Australia
- https://www.sciencedirect.com/science/article/pii/S0169136824003470 -> Overcoming survival bias in targeting mineral deposits of the future: Towards null and negative tests of the exploration search space, accounting for lack of visibility
- https://www.sciencedirect.com/science/article/pii/S088329272400115X - > Ranking Mineral Exploration Targets in Support of Commercial Decision Making: A Key Component for Inclusion in an Exploration Information System
深度學習
- https://arxiv.org/abs/2408.11804 -> Approaching Deep Learning through the Spectral Dynamics of Weights
- https://arxiv.org/pdf/2310.19909.pdf -> Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
- https://pure.mpg.de/rest/items/item_3029184_8/component/file_3282959/content -> Deep learning and process understanding for data-driven Earth system science
- https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2391952 -> Deep learning for spatiotemporal forecasting in Earth system science: a review
- https://wires.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/widm.1554 -> From 3D point-cloud data to explainable geometric deep learning: State-of-the-art and future challenges
- https://arxiv.org/pdf/2410.16602 -> Foundation Models for Remote Sensing and Earth Observation: A Survey
- https://www.nature.com/articles/s41467-021-24025-8 -> Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data
- https://arxiv.org/abs/2404.07738 ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
- https://ieeexplore.ieee.org/abstract/document/10605826 -> Swin-CDSA: The Semantic Segmentation of Remote Sensing Images Based on Cascaded Depthwise Convolution and Spatial Attention Mechanism
- https://www.sciencedirect.com/science/article/abs/pii/S0098300424000839#sec6 -> Leveraging automated deep learning (AutoDL) in geosciences
