矿产勘探机器学习
本页面列出了矿物勘探和机器学习的资源，通常包含有用的代码和示例。机器学习和数据科学是一个巨大的领域，这些是我在实践中发现有用和/或有趣的资源。当前指向存储库分支的链接是因为我更改了要使用的内容并放入列表中以供参考。还为数据分析、转换和可视化提供了资源，因为这是大部分工作。
欢迎提出建议：展开讨论、问题或拉取请求。
目录
- 前景
- 地质学
- 自然语言处理
- 遥感
- 数据质量
- 社区
- 云提供商
- 域名
- 概述
- 网络服务
- 数据门户
- 工具
- 本体论
- 图书
- 数据集
- 文件
- 其他
- 一般兴趣
地图
框架
- UNCOVER-ML框架
- EIS 工具包 -> 来自 EIS Horizon EU 项目的矿物前景图绘制的 Python 库
- PySpatialML -> 有助于自动预测和处理栅格机器学习到 geotiff 等的库。
- scikit 地图
- TorchGeo -> 用于遥感样式模型的 Pytorch 库
- terratorch -> 地理空间基础模型的灵活微调框架
- 火炬空间
- 地质学家
- Geo Deep Learning -> 基于RGB的简单深度学习框架
- AIDE：人工智能解决极端问题
- ExPloRA -> ExPLoRA：参数高效的扩展预训练以适应域转移下的视觉 Transformer
- （https://www.researchgate.net/profile/Miguel-Angel-Fernandez-Torres/publication/381917888_The_AIDE_Toolbox_Artificial_intelligence_for_d isentangling_extreme_events/links/66846648714e0b03153f38ae/The-AIDE-Toolbox-Artificial-intelligence-for-disentangling-extreme-events.pdf)
右
- CAST -> 时空模型的插入符号应用
- geodl -> 使用基于卷积神经网络的深度学习对地理空间数据进行语义分割
管道
前景
澳大利亚
- 氧化铁铜金矿潜力图
- 用于地质测绘的机器学习：算法和应用 -> 带有代码和数据的博士论文
- 镍钴红土远景图
- Transform 2022 教程 -> 随机森林示例
- 锡钨合金
- 斑岩铜时空勘探
- minpot-toolkit -> Hoggard 等实验室沉积铜边界分析示例
- MPM-WofE -> 矿物潜力测绘 - 证据权重
探险家挑战
- Explorer Challenge -> OZ Minerals 举办数据科学竞赛
南澳大利亚
- Gawler_MPM -> 钴、铬、镍
- 高勒克拉通的地球物理数据聚类
- [Zenodo Data]（利用地球物理数据和无监督机器学习自动检测与矿化相关的克拉通结构）
Explore SA - 南澳大利亚州能源和矿业部竞赛
- 获奖者 -> SARIG 数据信息
- Caldera -> Caldera Analytics 分析
- 因瑟托数据
- 巴特沃斯和巴尼特 -> 巴特沃斯和巴尼特条目
- 数据驱动的矿化图
北美
加拿大
- 转移预期学习
- 论文 -> 通过先验地质迁移学习利用不平衡数据进行斑岩型矿产远景测绘
南美洲
巴西
- Mapa Predivo -> 巴西学生项目
- Course_Predictive_Mapping_USP -> 课程项目
- 矿产前景图
- 3D 证据权重
- 地质复杂性 SMOTE -> 包括分形分析
- MPM Jurena -> Jurena 矿产省
中国
- MPM 集成学习 -> 中国青城子铅锌银金多金属区
- 矿物前景预测卷积神经网络 -> 具有一些架构的 CNN 示例 [作者的一篇论文使用 GoogleNet]
- CSAE 矿产前景预测
- CAE 矿产前景预测
苏丹
挪威
- 基于机器学习的方法，结合机载电磁学和岩土数据，对敏感的冰川粘土进行区域尺度测绘
地质学
- 巴西预测地质图 -> 巴西地质调查局的工作
- 基岩深度（评估基岩深度映射的空间机器学习方法）
- DL-RMD -> 用于深度学习应用的地球物理约束电磁电阻率模型数据库
- 地质图像分类器
- 人工智能时代的地质测绘 -> 人工智能时代的地质测绘
- GeolNR -> 用于三维结构地质建模应用的地质隐式神经表示
- 地图_litologico_preditiv
- 通过机器学习温压法绘制全球岩石圈地幔压力-温度条件
- 神经岩石分型
- West Musgraves 地质学不确定性 -> 使用熵分析进行不确定性图预测：非常有用
- 非平稳缓解变压器
- 基岩与沉积物
- 自动编码器_遥感
- 论文 -> 通过堆叠自动编码器和聚类进行地质测绘的遥感框架
训练数据
- 进入 Noddyverse -> 用于机器学习和反演应用的 3D 地质模型海量数据存储
岩性
- 深度学习岩性
- 岩石原岩预测器
- SA地质岩性预测
- 自动测井关联
- dawson-facies-2022 -> 地质图像的迁移学习
- 论文 - > 数据集大小和卷积神经网络架构对碳酸盐岩分类迁移学习的影响
- 岩相分类 -> 使用随机森林进行火山相分类
- 使用地质和地球物理数据进行 3D 建模的多视图集成机器学习方法
- SedNet
钻孔
- 异构钻孔 - Nicta/Data61 项目报告，用于查看使用不够远的钻孔进行建模
- corel -> 智能计算机视觉模型，可识别岩相并在岩心图像上执行岩石分类
古山谷
- Sub3DNet1.0：用于区域尺度 3D 地下结构测绘的深度学习模型
地层学
- Predicatops -> 为碳氢化合物设计的地层预测
- 地层几何 -> 根据地下测井预测地层几何
结构性
- APGS -> 构造地质包
- 使用板块驱动力一致性测试评估板块重建模型 -> Jupyter 笔记本和数据
- 板状
- [构造地质学食谱](https://github.com/gcmatos/structural-geology-cookbook]
- GEOMAPLEARN 1.0 -> 使用机器学习从地质图中检测地质结构
- 线形学习 -> 通过势场深度学习和聚类进行故障预测和绘图
- LitMod3D -> 岩石圈和底层上地幔的 3D 集成地球物理-岩石学交互建模
- 其他的
模拟
- GebPy -> 生成岩石和矿物的地质数据
- OpenGeoSys -> 开发用于模拟多孔和裂缝介质中的热-水-机械-化学 (THMC) 过程的数值方法
- Stratigraphics.jl -> 从 2D 地质统计过程创建 3D 地层学
地球动力学
- 荒地 -> 盆地和景观动力学
- CitcomS -> 有限元代码，旨在解决与地幔相关的可压缩热化学对流问题。
- LaMEM -> 模拟各种热力地球动力学过程，例如地幔-岩石圈相互作用
- PTatin3D -> 研究与地球动力学相关的长时间尺度过程[原始动机：能够研究岩石圈变形的高分辨率三维模型的工具包]
- 黑社会 -> 地球动力学有限元建模
地球物理学
基础模型
- 跨域基础模型适应：用于地球物理数据分析的开创性计算机视觉模型 -> 即将发布的一些代码
- 地震基础模型->“新一代地球物理深度学习模型”
澳大利亚
风化层深度
- 风化层深度 -> 模型
- 具有建模填充的完整澳大利亚辐射测量网格
AEM 插值
电磁学
- TEM-NLnet：具有噪声学习功能的瞬态电磁信号深度去噪网络
反转
- 机器学习和地球物理反演 -> 重建 Y. Kim 和 N. Nakata 的论文（Theleading Edge，第 37 卷，第 12 期，2018 年 12 月）
欧拉反卷积
- https://legacy.fatiando.org/gallery/gravmag/euler_moving_window.html
- 最终口琴版？ 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
- [通过卷积神经网络使用重力数据恢复 3D 地下室浮雕]
- 使用深度学习实现重力势场的稳定向下延续
- 通过机器学习方法对 3D 密度结构进行快速成像
磁学
- 通过 Adapted-SRGAN 绘制高分辨率航磁图
- MagImage2Geo3D
地震
- StorSeismic -> 一种预训练神经网络来存储地震数据特征的方法
- PINNtomo -> 使用物理信息神经网络进行地震层析成像
地震学
岩石物理学
构造学
- 使用机器学习识别古代俯冲带中俯冲板块的分离 -> 笔记本
- Colab 笔记本 -> 用于 ML-SEISMIC 出版物基准测试结果的 Google Colab 输入文件
- 释放地球动力学中机器学习的力量
- 用于基于速率和状态摩擦定律的断层滑动模拟的物理信息神经网络
- 慢滑事件的模拟和摩擦参数估计
- 论文 -> 用于估计慢滑移区域摩擦参数空间分布的物理信息深度学习
地球化学
- CODAinPractice -> 成分数据分析实践
- 地理科达
- DAN-GRF -> 连接到地理随机森林的深度自动编码器网络，用于空间感知地球化学异常检测
- Dash 地球化学勘探 -> 使用 K 均值对河流沉积物进行分类的 Web 应用程序
- 增强含单斜辉石岩浆的机器学习温压测量
- 论文 -> 增强单斜辉石轴承岩浆的 ML 热压测量
- 锆石肥沃度模型 -> 预测斑岩铜矿床中肥沃锆石的决策树
- 用于预测斑岩矿床类型和资源规模的机器学习锆石微量元素工具
- geology_class0 -> 通过锆石微量元素判别火成岩和矿床的机器学习方法
- 纸
- 演示应用程序
- https://colab.research.google.com/drive/1-bOZgG6Nxt2Rp1ueO1SYmzIqCRiyyYcT
- 地球化学印刷
- 全球地球化学
- ICBMS Jacobina -> 金矿床黄铁矿化学分析
- Bor 和 Cukaru Peki 对锆石微量元素化学的解释：传统方法和随机森林分类
- Indicator_minerals -> PCA 能否讲述电气石起源的故事？
- 地球化学勘探杂志 - 流形
- LewisML -> 刘易斯地层分析
- 云母 -> 化学成分，闪亮
- 用于稀土元素地球化学异常检测的多元统计分析和定制偏差网络建模
- 通过地球化学数据分析绘制稀土元素的远景图 -> 通过地球化学数据分析绘制稀土元素的远景图
- QMineral Modeller -> 来自巴西地质调查局的矿物化学虚拟助手
- 太古代期间俯冲发生的长期变化 -> Zenodo 代码存档
- [论文] https://www.researchgate.net/publication/380289934_Secular_Changes_in_the_Occurrence_of_Subduction_During_the_ArcheanA 通过锆石微量元素辨别火成岩和矿床的机器学习方法
克里金法
- DKNN：用于可解释地理空间插值的深度克里格神经网络
自然语言处理
- 文本提取 -> 从文档中提取文本：付费的 ML 作为服务，但效果很好，可以有效地提取表格
- NASA 概念标签 -> 关键词预测
- 岩相学报告数据提取器
- SA 探索主题建模 -> 来自探索报告的主题建模
- 地层仪
- 地质语料库
- 葡萄牙语 BERT
- 伯特 CWS
- 自动提取矿业公司钻孔结果
词嵌入
- 地球科学语言模型 -> 处理代码管道和模型 [Glove、BERT] 根据加拿大的地球科学文档进行重新训练
- 数据集 -> 支持模型的数据
- 论文 -> 地球科学语言模型及其内在评价
- 论文 -> 自然语言处理在地球科学文本数据和前景建模中的应用
- GeoVec -> 在 30 万篇地球科学论文上训练的词嵌入模型
- GeoVec 模型 -> GeoVec 模型的 OSF 存储
- 纸
- GeoVecto Litho -> 根据词嵌入进行 3D 模型插值
- GeoVEC Playground -> 使用 Padarian GeoVec 手套词嵌入模型
- GloVe -> 用于生成词嵌入的斯坦福库
- gloVE python glove, glove-python 在 Windows 上存在很大问题：这里安装 Windows 的二进制版本：
- 手套 -> 在内存中矢量化手套实现
- PetroVec -> 石油和天然气行业的葡萄牙语词嵌入：开发和评估
- wordembeddingsOG -> 葡萄牙语石油和天然气词嵌入
- 葡萄牙语词嵌入
- 西班牙语词嵌入
- 多语言对齐
命名实体识别
- Geo NER 模型 -> 命名实体识别
- GeoBERT - 模型的拥抱脸部存储库
- [论文]https://www.researchgate.net/publication/359186219_Few-shot_learning_for_name_entity_recognition_in_geological_text_based_on_GeoBERT
- INDUS -> NASA 科学定制的法学硕士套件
- 如何使用 Amazon Comprehend 在不掌握 NLP 的情况下查找文本中的关键地球科学术语
- OzRock - OzRock：地质（矿产勘探）领域实体识别的标记数据集
本体论
- GAKG -> 多模态地球科学学术知识图谱（中文）
- GeoERE-Net -> 使用深度学习方法理解基于知识图的地质报告
- 地质故障本体论
- geosim -> 语义触发的地质过程定性模拟
- [https://www.duo.uio.no/handle/10852/111467]（数字地质学知识建模）-> 博士论文，包含两篇论文
- SIRIUS GeoAnnotator -> 上面的网站示例
- 本体CWS
- 地层知识图（StraKG）
大型语言模型
- 地球科学大型语言模型
- GeoGalacica -> 地球科学中更大的基础语言模型
- GeoChat -> 用于遥感的接地大视觉语言模型
- LAGDAL -> LLM 将地质图信息与位置实验相匹配
聊天机器人
遥感
- CNN Sentinel -> 基于开放数据集的 CNN 卫星数据土地利用分类概述
- DEA 笔记本 -> 可扩展的机器学习示例，但这里有很多有用的东西
- EASI 食谱笔记本 -> 用于 ODC 风格分析的 CSIRO 地球分析平台介绍
- DS_UNet -> Unet 融合 Sentinel-1 合成孔径雷达 (SAR) 和 Sentinel-2 多光谱成像仪
- 多借口屏蔽自动编码器 (MP-MAE)
- 数据
- 分段地理空间 -> 对地理空间用途的任何内容进行分段
- SamGIS -> 对应用于 GIS 的任何内容进行分段
- SatMAE++ -> 重新思考多光谱卫星图像的 Transformers 预训练
- grid-mae -> 研究在 Vision Transformer Masked Autoencoder 中使用多尺度网格
- 尺度梅
- CIMAE -> CIMAE - 通道独立屏蔽自动编码器
- fork -> 为其指定名称以供参考
- [遥感自监督表示学习] -> 硕士论文包括以上内容以及几种模型的比较
- U谷仓
- 地球网
- GeoTorchAI -> GeoTorchAI：时空深度学习框架
- [pytorcheo](https://github.com/earthpulse/pytorchEO -> 深度学习对地观测应用和研究
- AiTLAS -> 一个开源基准套件，用于评估地球观测中图像分类的最先进深度学习方法
- Segmentation Gym -> Gym 被设计为“一站式商店”，用于“ND”上的图像分割 - 多光谱图像中任意数量的重合波段
- 深度学习改变区
- 很棒的采矿带比集合 -> 简单带比集合用于突出显示各种矿物
- 很棒的遥感基础模型
- Clay -> 地球的开源人工智能模型和界面
- IBM-NASA-地理空间 Prithvi
- 通过基础模型微调进行图像分割 -> For Prithvi
- AM-RADIO：凝聚视觉基础模型
- RemoteCLIP -> 遥感视觉语言基础模型
- 光谱GPT
- zenodo) -> 针对光谱数据定制的遥感基础模型
加工
- ASTER 转换 -> 从 ASTER hd5 到 geotiff NASA github 的转换
- HLS 数据资源 -> 协调 Landsat Sentinel 争论
- sarsen -> 基于xarray的SAR图像处理和校正
- openEO -> openEO 开发开放 API 将 R、Python、JavaScript 和其他客户端连接到 EO 云后端
光谱分解
- 2024 年高光谱图像分类从传统到变压器的调查
- 高光谱深度学习评论
- 高光谱自动编码器
- 深度学习恒指
- 3DCAE-高光谱-分类
- 脱氢IC
- 修订网
- 论文 -> 具有光谱变异性的高光谱分解的可逆生成网络
- Pysptools -> 也有有用的启发式算法
- 光谱蟒蛇
- 光谱数据集 RockSL -> 打开光谱数据集
- 分离
高光谱
- CasFormer：用于融合感知计算高光谱成像的级联变压器
- Keras 的光谱归一化
- S^2HM^2 -> S2HM2：用于大规模高光谱图像自监督特征学习和分类的谱空间分层掩模建模框架
可视化
- 从可视化中提取深层色彩图
- 用于从地形图中提取历史露天采矿扰动的语义分割 -> 以煤矿为例
- 国际年代地层颜色代码 -> RGB 代码以及电子表格和其他格式的其他代码
- LithClass -> USGS 版本的岩性颜色代码
- 彩色版
- SeisWiz -> 轻量级 python SEG-Y 查看器
质地
- 使用深度卷积神经网络进行矿物结构分类：斑岩铜矿床锆石的应用
模拟
几何学
- Deep Angle -> 使用深度学习快速计算断层扫描图像中的接触角
其他
- 矿物学系统的网络分析
- 地理分析和机器学习
- 机器学习地下
- 机器学习地球科学
- 成为一名地球科学侦探
- Earth ML -> PyData 方法的一些基本教程
- GeoMLA -> 用于空间和时空数据的机器学习算法
平台
指南
- 地理空间 CLI - 地理空间命令行工具列表
- 卫星图像深度学习
- 地球观测
- 地球人工智能
- 开源 GIS -> 生态系统全面概述
数据质量
- 机器学习的地球科学数据质量 -> 机器学习的地球科学数据质量
- 澳大利亚重力数据 -> 重力站数据概述与分析
- Geodiff -> 矢量数据比较
- Redflag -> 数据分析和检测问题的概述
机器学习
- Dask-ml -> 一些常见 ML 算法的分布式版本
- geospatial-rf -> 协助空间上下文中的随机森林应用的函数和包装器
- Geospatial-ml -> 一次安装多个通用包
潜在空间
- 嵌套融合
- 论文 -> 嵌套融合：M2020 PIXL RGBU 和 XRF 数据的多尺度嵌套数据的降维和潜在结构分析
指标
- 分数 -> 使用 xarray 验证和评估模型和预测
概率论
- NG Boost -> 概率回归
- 概率机器学习
- 使用 BO 进行 Bagging PU -> 使用贝叶斯优化进行正向无标记 Bagging
聚类
自组织映射
- GisSOM -> 来自芬兰地质调查局的以地理空间为中心的自组织地图
- SimpSOM -> 自组织映射
其他
贝叶斯
可解释性
- InterpretML -> 解释表格数据模型
- InterpretML -> 社区添加
深度学习
- 深度色彩图提取 -> 尝试从图片中提取数据比例
- 从地球科学文档中提取图像并对其进行分类
数据
- Xbatcher -> 基于 Xarray 的深度学习数据读取
- 使用 Zarr 和 Xarray 用于机器学习的云原生数据加载器
- zen3geo -> 使用 pytorch 的 Xbatcher 风格数据科学
可解释性
- 形状值
- 慧俪轻体 -> 分析网络的训练情况
- 体重观察者.ai
- Weightwatcher-ai.com -> 专业网页版
自我监督学习
- 自监督 -> 多种算法的 Pytorch 闪电实现
- 西姆克勒
- 很棒的自我监督学习 -> 精选列表
超参数
编码环境
社区
- Software Underground - 有兴趣探索地下和代码交集的人们的社区
- 聊天注册 - SWUNG 社区聊天注册
- Mattermost-社区聊天服务
- 旧的 Slack Channel（已弃用，请参阅上面的 Mattermost）
- 地球科学开源搭配
- 视频
- 很棒的开放地球科学[注意石油和天然气偏见]
- Transform 2021 黑客示例
- Segysak 2021 教程
- T21 地震笔记本
- 使用 Python 进行实用地震
- 2021 年辛佩格转型
- 潘吉奥
- 澳大利亚数字地球
- 开源地理空间基金会
- OSGeoLive -> 可启动 DVD/USB，带有大量开源地理空间软件
- ASEG -> 来自澳大利亚勘探地球科学家协会的视频
- 用于地质建模和绘图的人工智能 -> 会议当天的视频
- 会议
云提供商
AWS
- ec2 Spot 实验室 -> 使 Spot 实例的自动工作变得更容易
- Sagemaker 地理空间机器学习
- Sagemaker -> ML 托管服务
批
- Shepard -> AWS Batch Pipelines 的自动云形成设置：这很棒
套餐
一般的
- 深度学习容器
- Loguru -> 日志库
- AWS GDAL Robot -> Lambda 和 geotiff 的批处理
- 无服务器地震处理
- LIhops -> 多云分布式计算框架
概述
域名
网络服务
如果列出，则假定它们通常是数据，如果只是像 WMS 这样的图片，它会这样说。
世界
澳大利亚
- 澳大利亚GIN
- 澳大利亚地球科学
- 矿产潜力 -> WMS
- 澳大利亚地球科学目录服务
地质学
- AUSLAMP - > Tennant Creek - MtIsa
- 野外地质学
- 深层岩石圈 -> 深层岩石圈矿产潜力
- 地质年代学 -> 地质年代学
- 地质省份
- WMS -> WMS图片
- EGGS -> 地质和地球物理表面的估计
- 元古代碱性岩石 - 元古代碱性岩石数据集 WFS {还有 WMS}
- 新生代
- 中生代
- 古生代
- 太古代
- 地层学 -> 地层单位
地球物理学
- 地球物理调查
- 地震勘测 -> 陆上地震勘测
- 大地电磁 -> 澳大利亚北部 AUSLAMP 站
其他
- Ni-Cu-PEGE -> 侵入型镍铜 PGE 矿床
- EFTF 领域 -> 探索未来领域
- 温度 -> 解释温度
- DEA -> 澳大利亚数字地球
- 土地覆盖
- 水体
- BOM -> 气象局水文地球化学
新南威尔士州
- 新南威尔士州
- 世界CS
- WFS 矿物钻孔
- WFS 石油钻井
- WFS 煤炭钻孔
- 地震 -> 地震及其他
昆士兰州
- 昆士兰州
- 地球科学 -> 地球物理学和报告索引
- 地质学
- 区域性
- 状态
- 唐楼
- 道路
- 河道
南澳大利亚
- 萨里格
- 钻孔
- 地质学
- 地球物理学
- 前景
- 矿产和矿山
- 遥感
- 地震
- 唐楼
北领地
塔斯马尼亚
维多利亚
西澳大利亚州
新西兰
南美洲
巴西
秘鲁
墨西哥
阿根廷
哥伦比亚
乌拉圭
其他
欧洲
EGDI -> EGDI 矿物
瑞典
芬兰
- GTK -> 芬兰地质调查局
- 北极矿物 -> 北极 1M 矿点
丹麦
葡萄牙
西班牙
- 西班牙
- 地质 -> 200K
- 1M -> 1M
- 50K -> 50K
- IGME晶洞
- 地球物理学
- 铜 - 铜
- GeoFPI - > 地质和矿产 南葡萄牙区
- 水
乌克兰
爱尔兰
英国
- BGS -> 英国地质调查局
- 地理索引 -> 矿物产状示例
- 休息 -> BGS 休息服务和 Inspire 625
德国
捷克共和国
斯洛伐克
匈牙利
罗马尼亚
- IGR -> 仅 WMS
- IGR 分钟 -> 仅 WMS
波兰
北美
加拿大
美国
- 美国地质勘探局世界矿产
- 美国地质勘探局MRDS
- 明尼苏达州
亚洲
- 中国 -> WMS 矿床 wap
- 矿田 -> 矿物赋存点
- 印度矿产 -> WMS
- 印度地球物理
非洲
- 非洲地理门户 -> 休息服务
- 非洲 10M -> 非洲 10M 矿产情况 https://pubs.usgs.gov/of/2005/1294/e/OF05-1294-E.pdf
- IPIS 手工矿山 -> 也有 WMS 版本
- github
- 乌干达 -> GMIS WMS
一般的
- 矿产勘探 Web 服务 -> QGIS 插件，可访问许多相关的 Web 服务
其他
蜜蜂
- 开放数据 API -> GSQ 开放数据门户 API
- 核心 -> 开放研究文本
- 分享 -> 开放科学 API
- 美国地质调查局出版物
- 交叉引用
- xDD -> 前 GeoDeepDive
- ADEPT -> GUI 到 xDD 搜索 15M 收获的论文
- 开放亚历克斯
- 应用程序编程接口
- diophila Python 库
- Python库
- 宏观战略
- OpenMinData -> 方便从 Mindat API 查询和检索矿物和地质材料的数据
数据门户
世界
- 地球模型协作 -> 访问各种地球模型、用于模型预览的可视化工具、提取模型数据/元数据的设施以及访问贡献的处理软件和脚本。
- ISC通报 -> 地震震源机制检索
- [磁学信息联盟[(https://www2.earthref.org/MagIC/search) -> 古地磁、地磁、岩磁
澳大利亚
澳大利亚地球科学
- 澳大利亚地球科学数据目录
- 澳大利亚AEM
- 澳大利亚地球科学门户网站
- 探索未来门户 -> 澳大利亚地球科学门户网站提供下载信息
- 澳大利亚AEM
- 澳大利亚灯
- 地质年代学和同位素
- 水文地质 流域 -> 搜索流域层
- 关键矿物测绘计划
- 澳大利亚地层单位
- 澳大利亚钻孔地层单位 -> 沉积单位地下水的编制
- 澳大利亚地球科学地球物理学线程 -> OpendDAP 和 https 访问
- MORPH gdb -> 马斯格雷夫官员钻井数据
联邦科学与工业研究组织
- CSIRO 数据访问门户
- 风化层深度
- TWI -> 地形湿度指数
- ASTER 地球科学地图 -> 网站
- FTP -> CSIRO ftp 站点
- ASTER 地图注释 -> 上述注释
奥斯科普
燕鸥
气象局
基础空间数据
南澳大利亚
- SARIG -> 南澳大利亚地质调查局基于地理空间地图的搜索
- SARIG 目录 -> 数据目录
- 3D模型
- 数据包 - 年度更新
- s3 报告 -> s3 存储桶中的报告和文本版本（带有 Web 界面）
- 报告
- 地震
北领地
- 罢工 -> 北领地地质调查局
- 宝石管理信息系统
- 麦克阿瑟盆地 -> 3D 模型
- 地球物理调查
- 地球物理学 -> 参考
- 钻井和地球化学 -> 参考
昆士兰州
西澳大利亚州
- GEOVIEW -> 西澳大利亚地质调查局
- DMIRS -> DMIRS 数据和软件中心
- 下载 URLS -> 下载链接数据集
- 钻井和地球化学
- 下载包 - 改进？
- 地球化学
- 有深度的石油井
- 数据WA子集
新南威尔士州
- MINVIEW -> 新南威尔士州地质调查局
- DiGS -> 出版物和岩土工程收藏
塔斯马尼亚
维多利亚
- 地球资源
- GeoVIC -> 网络地图需要注册才能更有用
新西兰
- 探索数据库 -> 在线
- GERM -> 新西兰地质资源图
- 地质 -> 网络地图
- https://maps.gns.cri.nz/gns/wfs
南美洲
巴西
- CPRM -> 巴西地质调查局
- 下载 -> 巴西地质调查局下载
- Rigeo -> 地球科学机构知识库
秘鲁
- Ingemmet GeoPROMINE -> 秘鲁地质调查局
- 地理MAPE
墨西哥
阿根廷
哥伦比亚
乌拉圭
智利
欧洲
- EGDI -> 欧洲地球科学
- 世界金融峰会
- 普罗米因
- 启发 -> 启发地理门户
丹麦
芬兰
- 矿物质4EU
- GTK -> 芬兰地质调查局
- 地球化学图 -> 仅 pdf！
瑞典
西班牙
葡萄牙
爱尔兰
- GSI -> 爱尔兰地质调查局
- GSI - 地图查看器
- 金矿 -> 地图及文献检索
- data.gov.ie -> 国家门户视图
- isde -> 爱尔兰空间数据交换
挪威
- NGU -> 挪威地质调查局
- 数据库 -> 矿产资源和地层查找
- github
- 应用程序编程接口
- Geoporta -> 地球物理学
- GEONORGE -> 带下载的数据目录
英国
乌克兰
俄罗斯
- 俄罗斯地质研究所 -> 目前无法访问
- RGU -> 存款GIS项目
德国
- 地理门户
- 地理地图 -> M
- Atom -> Atom 数据源
- GDI -> 德国 3D 模型
法国
克罗地亚
捷克共和国
斯洛文尼亚
斯洛伐克
匈牙利
罗马尼亚
波兰
英国
- 英国陆上地球物理图书馆
- 操作系统数据中心英国地质学
- 地质学625
北美
加拿大
- 加拿大自然资源部
- github
- 地球科学数据存储库 -> DAP 服务器
- 矿业网络地图门户
- DEM -> COG 格式的加拿大 DEM
- CDEM -> 数字高程模型 (2011)
- 安大略省
- 魁北克
- SIGEOM数据库
- 不列颠哥伦比亚省
- 矿物赋存数据库
- 育空地区
- 新斯科舍省
- 省级
- 爱德华王子岛
- 萨斯喀彻温省
- 矿产赋存数据库
- 纽芬兰 -> 在 Chrome 中不起作用，在 Edge 中尝试过
- 艾伯塔省
- 交互式地图应用
- 西北地区
- 矿权
美国
- 美国地质勘探局 -> 地图数据库
- MRDS -> 矿产资源数据系统
- 地球探索者 -> USGS 遥感数据门户
- 国家地图数据库
- 国家地图数据库
- 阿拉斯加州
- ReSci -> 国家地质与地球物理资料保存计划科学馆藏登记
- 密歇根州
非洲
- 地籍
- 水文地质学 -> 地下水图集的水文地质学和地质学
- 西非 -> 矿藏
- 纳米比亚
- 矿物产状
- 矿工
- 南非 -> 南非地质调查局
- 矿物产状 -> 需要登录才能下载的示例
- 乌干达 -> GMIS 门户
- 金属矿物
- 坦桑尼亚
- 矿物产状
- 地雷
- SIGM -> 突尼斯地质和采矿
- 赞比亚 -> 赞比亚房产这里
亚洲
中国
印度
- 布科什 -> 印度地质调查局
- 注意拉贾斯坦邦的地质学除了零碎的工作之外不起作用，这是痛苦的 - 如果你想要它，请告诉我
沙特阿拉伯
其他
地质学
- 策略数据库
- GEM全球活跃故障
- RRuff 矿物特性
- 文章 -> 矿物学演化系统
- 地质学
- 目录
伊朗
地质学
一般的
- OSF -> 开放科学基金会
- 沉积物托管贱金属 -> 沉积物托管贱金属
- 岩石圈流圈边界 -> LAB Hoggard/Czarnota
- 地质调查清单
报告
澳大利亚
- 北领地 GEMIS
- 南澳大利亚州SARIG
- 西澳大利亚 WAMEX
- 昆士兰州
- 新南威尔士州挖掘
- PorterGEO -> 世界矿藏数据库及摘要概述
- 可持续矿物研究所 -> 昆士兰大学附属研究人员组织制作数据集和知识
加拿大
- 不列颠哥伦比亚省
- 安大略省 -> 矿产评估报告
- 艾伯塔省
- 育空地区
- 脚印
- 马尼托巴省
- 刊物
- 纽芬兰和拉布拉多
- 西北地区
- 新斯科舍省
- 魁北克
- 萨斯喀彻温省
美国
- 亚利桑那
- 蒙大拿
- 内华达州
- 新墨西哥州
- 明尼苏达州
- 密歇根州
- json
- 阿拉斯加州
- 华盛顿
其他
- 英国地质调查局NERC
- 矿产潜力
- 搜索
- API示例
- 刊物
- MEIGA -> MEIGA 600 BGS 矿产勘探项目报告
- GeoLagret -> 瑞典
- MinData -> 世界各地岩石位置的汇编
- 矿物数据库 -> 可导出的具有科学性质和年龄的矿物列表
- 美国宇航局
- ResearchGate -> 研究员和专业网络
工具
地理信息系统
- QGIS -> GIS 数据可视化和分析开源桌面应用程序，具有一些 ML 工具：对于快速轻松的查看来说是必不可少的
- QGIS 中的 2D 地质学 -> QGIS NA 2020 研讨会为学生和爱好者介绍地质图和横截面
- OpenLog -> 钻孔插件测试版
- Geo-SAM -> QGIS 插件，用于使用栅格分割任何内容
- 证据权重
- 插件
- 草
- 传奇 -> Sourceforge 的镜像
3D
地理空间综合
- 地球科学的 Python 资源
- geoutils -> 地理空间分析并促进其他 Python GIS 包之间的互操作性。
矢量数据
Python
- 大熊猫
- Dask-Geopandas
- GeofileOps->通过数据库功能和地理包装增加速度空间连接
- 卡丁车 - > daata的分布式版本控制
- 毕他丽 - >库从ESRI REST服务器大规模获取数据
右
- 顺丰
- Terra-> Terra提供了操纵“栅格”和“向量”形式的地理（空间）数据的方法。
栅格数据
C
- extactExtract-> C命令行Zonal Stats在C中
朱莉娅
Python
- rasterio->用于栅格数据处理的Python基础库
- Georeader->来自不同卫星任务的过程栅格数据
- rasterstats->基于矢量几何形状的地理空间栅格数据集
- Xarray->多维标记的阵列处理和分析
- ioxarray-> Xarray处理栅格数据的神话般的高级API
- Geocube->向量数据API的栅栏
- ODC -GEO->用于基于遥感的栅格处理的工具，使用许多非常方便的工具，例如色彩，网格工作流程
- COG验证器 - >检查云优化的Geotiffs的格式
- 无服务器datacube-demo-> xarray通过lithops /盘绕 /模态
- Xarray空间 - >统计分析栅格数据，例如自然断裂等分类
- XDGGS->其他类型的网格
- XGCM->带有标签的直方图
- XRFT->基于Xarray的傅立叶变换
- xvec-> xarray的矢量数据立方体
- Xarray -einstats->统计数据，线性代数和Xarray的Einops
右
- 栅格 - > r库
- Terra->提供了“栅格”和“向量”形式以地理（空间）数据操纵地理（空间）数据的方法。
- 星星 - >时空阵列：栅格和矢量数据库
- EctentExtracr-> r的栅格区域统计数据
基准测试
- 栅格基准 - >基准在Python和R中进行一些栅格性诽谤
桂
- Whitebox工具 - > Python API，GUI等用于地形湿度指数计算
数据收集
- piautostage->'一种开源3D打印工具，用于自动收集高分辨率显微镜图像；”为矿物样品设计。
数据转换
- aem到seg-y
- ASEG GDF2
- CGG Outfile读者
- Geosoft网格到栅格
- 循环Geosoft Grid
- 口琴Geosoft Grid->在转换为Xarray时正在进行的请求
- Auscope->来自二进制GOCAD模型的数据
- Gocad SG网格阅读器
- GeoModel-2-3dweb->在这里，它们有一种从二进制GOCAD SG网格中提取数据的方法
- Leapfrog网状阅读器
- OMF->开放采矿格式用于事物之间的转换
- PDF矿工
- VTK到DXF
地球化学
- pygeochemtools->库和命令行启用快速QC和地球化学数据的绘图
- SA地球化学图 - > SA地质调查局的南澳大利亚地球化学数据的数据分析和绘制
- 地球化学levenning
- 斯科特·哈雷（Scott Halley）的地球化学教程
- 元素周期表
地统计学
地质年代学
- 地质时间尺度 - >生产代码，但也有一个很好的常规CSV年龄
地质学
宝石 - >隐式建模
宝石 - >地理空间数据分析协助
循环 - >意义建模
手册Python Geologia->地质数据分析
map2loop-> 3D建模自动化
- loop3d-> gui for Map2loop
Pybedforms
SA地层 - >地层数据库编辑器WebApp
Striplog
Analise_De_Dados_Estruturais_altamira
全局构造学 - >开源数据集以建立在板上，保证金等。
Zenodo添加
岩性
pygplates
教程数据
地球物理
- 地球科学澳大利亚公用事业
- 地球物理学实践地球科学家
- 潜在的现场工具箱 - >一些基于Xarray的快速傅立叶变换过滤器 - 衍生物，Pseudogravity，RPG等。
- 笔记本 - >类带有一些示例[垂直导数，假性，向上延续等）
- 计算地球物理沙箱
- RIS地下沉积物 - >深度到南极洲的磁基底
- 信号图像处理
电磁
- 地球科学澳大利亚AEM
- UH电磁学 - >了解该领域的课程笔记本
- AEM解释
- emag py-> fdem
- Resipy-> DC / IP
重力和磁性
- 口琴
- 过滤示例 - >通过Xarray基于快速傅立叶变换的快速处理
- 澳大利亚重力数据
- 蠕虫
- Worms Update < - 潜在字段蠕虫创建带有一些次要更新来处理新的NetworkX API *github镜像
- 奥斯本磁性 - >调查数据处理示例
地震
- Segyio
- Segysak->基于Xarray的SEG -Y数据处理和分析
- 地球物理注释 - >地震数据处理
大地电磁学
- mtpy
- 教程
- mtpy->以上更新以使事情变得更容易
- 矿物统计工具包 - >距离MT特征分析的距离
- 岩石圈导体纸
- mtwaffle-> MT数据分析示例
- PYMT
- 抵抗
- mecmus->读取美国电导率模型的工具
- 模型
网格
- 格林威治标准时间
- 佛得角
- grid_aeromag->巴西格栅示例
- Pyinterp->通过Boost的多维网格
- 假性 - >来自Blakely，95
反转
- 模拟PEG
- mira地球科学叉 - >用于GeoApps
- Simpeg Fork
- 变换2020 Simpeg
- 变换2021 Simpeg
- Simpeg脚本
- 宇宙倒置示例
- 吉姆利
- tomofast-x
- USGS匿名FTP
- USGS软件 - >较长的旧有用内容列表：DOSBOX，有人吗？
- 地球物理子例程 - > fortran代码
- 2020 Aachen倒置问题 - >重力反演理论概述
地球化学
钻孔
- DH2LOR->钻孔间隔辅助
- 钻探 - >笔记本中的钻孔可视化通过geoh5py->注意降价
- pygslib->井下测量和间隔正常化
- pyborehole->处理和可视化钻孔数据
- DHCOMP->复合材料地球物理数据到一组间隔
遥感
- 很棒的光谱指数 - >频谱索引创建指南
- 开放数据立方体
- DEA笔记本 - >在ODC样式工作流中使用的代码
- Datacube -Stats-> ODC的统计分析库
- 地理笔记本 - >元素84的代码示例
- raster4ml->大量植被指数
- lefa->骨折分析，谱系
无服务器
- Kerchunk->通过Zarr无服务器访问基于云的数据
- kerchunk geoh5->通过kerchunk访问Geoscient Analyst/geoh5 5
- ICEHUNK->用于张量 / ND阵列数据的交易存储引擎，旨在在云对象存储中使用。
Stac目录
- DEA StackStac->使用数字地球澳大利亚数据的示例
- 进气stac
- ML AOI扩展
- ML模型扩展规范 - > CatalogingsPatio -Stmorqual模型的机器学习模型规范
- ODC -stac->数据库免费开放数据立方体
- Pystac
- SAT-Search
- stackstac->元数据加快了dask和xarray timeseries
统计数据
- 橙色 - >数据挖掘GUI
- HDSTATS->几何中位数的算法基础
- HDMedians
可视化
- 电视 - >在终端中查看卫星图像
- 巨人
- 坐
- HSDAR
- 星星
- 秘鲁黄金开采SAR
矿物潜力
- 镍矿物电位映射 - >基于ESRI的分析
- 潜在的在线工具
采矿经济学
- BlueCap->莫纳什大学（Monash University）评估矿山生存能力的框架
- ZIPFS法律 - >曲线拟合矿物沉积的分布
- PYASX-> ASX数据提要刮擦
- 金属价格API->容器化微服务
可视化
- Napari->多维图像查看器
- HOLOVIEWS->大规模数据可视化
- GraphViz->图形绘图/查看帮助Windows安装信息
- 空间kde
色彩图
- CET感知均匀的菌落
- pu colormaps->在地球科学分析师中为用户格式化
- colormap扭曲 - >一个面板应用程序，以证明由地球物理数据上的非知觉菌落产生的扭曲
- 从colormpas中撕下数据
- 开放地球科代码项目
地理空间
- 地理空间> - 安装多个常见的Python软件包
- 地理空间python->策划清单
技术堆栈
C
- GDAL->绝对关键的数据转换和分析框架
- 工具 - >注意具有许多命令行工具，它们也非常有用
朱莉娅
- 朱莉娅（Julia Earth） - >在地球科学中促进地理空间数据科学和地统计建模
- 朱莉娅的地球动力学 - >计算地球动力学代码
- 朱莉娅地球科学简介
Python -Pydata
- Anaconda->使用此软件包管理器已经安装了很多东西。
- GDAL等人 - >在此处将疼痛从GDAL和TensorFlow安装中消除
- Git Bash->让Conda在Git Bash中工作
- 数量多维阵列
- 熊猫表格数据分析
- matplotlib可视化
- Zarr->压缩，分布的分布式阵列
- dask->平行，分布式计算
- dask云提供商 - >自动在云上启动dask群集
- dask中间 - >笔记本提供DASK中间功能原型
- Python地理空间生态系统 - >策划的信息
生锈 - 乔治
数据库
- duckdb->在速度上的过程OLAP DB中 - 具有一些地理空间和阵列功能
- ibis + duckdb地理 - > scipy2024谈话
数据科学
- Python数据科学模板 - >项目包设置
- 很棒的Python数据科学 - >策划指南
可能性
科学
码头工人
- AWS 深度学习容器
- 空间码头
- DL Docker地理空间
- 摇杆
- Docker Lambda
- 地理数据库
- DL Docker地理空间
本体论
- 昆士兰州地质学会词汇
- 西澳大利亚地质学会
- 地层学
- 地球科学知识经理
- 地理词汇
图书
Python
- Python地理空间分析食谱
- 与Python的地理处理 - > Manning Livebook
其他
- 教科书
- 石油和天然气行业的机器学习
- r
- Earthdata云食谱 - >如何访问NASA资源
- 数据清洁剂的食谱 - >将UNIX工具放在数据争吵和清洁
- 数学地球科学百科全书
- 数学地球科学手册
其他
- gxpy-> Geosoft Python API
- EartharXiv->从预印本档案中下载论文
- Essoar->预印纸档案
数据集
世界
地质学
- 基岩 - >世界广义地质
- Glim->全球岩性图
- 古质地理位置图
- 沉积层 - >全球1公里的土壤，岩石和沉积层的厚度
- 世界应力图 - >在地壳当前压力场上信息的全球汇编
- GMBA->全球山库存
地球物理
重力
- 曲率 - >重力梯度数据的全局曲率分析
- WGM 2012
磁学
- EAMG2V3 _>地球磁异常网格
- WDMAM->世界数字磁异常图
大地电磁学
地震
- 实验室Slnaafsa
- 实验室CAM2016
- Moho-> Gemma数据
- moho-> szwillus数据
- 地震速度 - > Debayle等人
- 岩石层 - >岩石圈和上地幔的全局参考模型，以及对多个数据集的分析
- CRUST1.0->全球地壳模型NetCDF
- 概述主页
热的
一般的
- 深度数字地球 - >各种数据源和模型的数据和可视化
- Earthchem->社区驱动的保存，发现，访问和可视化地质，地球学和岩石学数据
- Georoc->岩石的地球化学组成
- 全球地质 - >用GIS格式制作全球地质图的简短食谱（例如ShapeFile），年龄范围映射到GTS2020时标
- 大型属省委员会
- 地幔羽
- 沉积物厚度 - >地图
- atpatialReference.org->网站的存储库
澳大利亚
- 普通地球模型
- 重型矿物图
- 澳大利亚重型矿物地图飞行员
- 闪亮的应用
地球化学
- 澳大利亚大陆上表面岩石和雷果石中主要氧化物浓度的预测网格 - >各种氧化物
地质学
- 碱性岩石地图集
- 新生代
- 中生代
- 古生代
- 太古代
- 搜索
- 原始碱性岩石 - >澳大利亚GIS的原始碱性和相关的火成岩
- 新生代
- 中生代
- 古生代
- 太古代
- 纸https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/147963
- 水文地质 - >澳大利亚水文地质图
- 水文地质学 - > 5m
- 分层地质 - > 1M
- 表面地质 - > 1m比例
- 澳大利亚镁铁质粉状岩浆事件GIS数据集
地球物理
磁学
- TMI->澳大利亚磁异常地图，第七版，2019 TMI
- 40m-> 40m版本
- VRTP->澳大利亚的总磁强度（TMI）网格，可变为POL（VRTP）2019
- 1VD-> 2019年澳大利亚的总磁强度网格 - 第一个垂直导数（1VD）
放射线学
- 放射学 - >澳大利亚的完整放射线（RADMAP）V4 2019与建模的填充物
- K->澳大利亚辐射电网（RADMAP）V4 2019过滤PCT钾网格
- U->澳大利亚辐射电网（RADMAP）V4 2019过滤PPM铀
- TH->澳大利亚辐射电网（RADMAP）V4 2019过滤PPM Thorium
- TH/K->澳大利亚的辐射电网（RADMAP）V4 2019 2019比率thor thorium胜于钾
- U/K->澳大利亚辐射电网（RADMAP）V4 2019 2019铀比钾
- U/Th->澳大利亚的辐射电网（RADMAP）V4 2019 2019比率铀超过th
- U平方/Th->澳大利亚辐射网格（Radmap）V4 2019 2019比率铀平方
- 澳大利亚（RADMAP）V4 2019的剂量率 - >辐射网格过滤陆地剂量率
- 三元图 - >澳大利亚辐射电网（RADMAP）V4 2019-三元图像（K，Th，U）
奥萨姆
- AUSAEM 1-> AUSAEM 1年NT/QLD机载电磁调查； GA分层地球反转产品
- AUSAEM 1-> AUSAEM 1年NT/QLD：Tempest®机载电磁数据和EMFlow®电导率估计值
- AUSAEM 1-> AUSAEM1解释数据包
- AUSAEM 2-> AUSAEM 02 WA/NT 2019-20机载电磁调查
- Ausaem – Wa-> Ausaem – Wa，Murchison机载电磁调查块
- Ausaem – Wa-> Ausaem-Wa，西南 - 阿尔巴尼空降电磁调查块
- Ausaem – Wa-> Ausaem WA 2020-21，东部戈德菲尔德和东伊尔加恩空降
- Ausaem – Wa-> Ausaem（WA）2020-21，Earaheedy＆Desert Strip
- Ausaem ERC-> Ausaem东部资源走廊
- Ausaem WRC-> Ausaem西方资源走廊
- Interp概述
- 国家表面和近地面电导率网格 - >澳大利亚的国家ML插值与北澳大利亚类似
auslamp
- Auslamp Sea->澳大利亚大陆的电阻率模型来自auslamp磁铁数据
- 维多利亚数据
- 新南威尔士州数据
- auslamp tisa->源自磁电磁素的电阻率模型：auslamp -tisa项目
- Auslamp Delamerian->从auslamp Magnettoteltoluric Data的Delamerian造山机的岩石圈电阻率模型
- auslamp ne sa
- 奥斯兰普·高沃勒（Auslamp Gawler）
- auslamp站 - >大约2017年
- 塔斯曼尼斯纸
莫霍面
矿藏
- 澳大利亚主要矿物质矿床的地质环境，年龄和捐赠
- 1799年至2021年澳大利亚矿山生产的综合数据集
矿物潜力
- 概述 - 澳大利亚地球科学 - >出版物和数据集的概述
- 沉积物托管锌
- 报告
- 沉积物托管铜
- 报告
- 抽象的
- 碳酸盐稀土元素
我的废物
本地标题
遥感
- Landsat Bare Earth-来自Landsat的裸露地球中位数
- 增强土壤和岩性建模的最大地球图像：数据集 - >增强的详细信息
- 由高分辨率卫星图像映射的全球采矿足迹**
- dem->澳大利亚1秒SRTM各种品种
结构
速度
- Au Tomo->来自同步和异步环境噪声成像的澳大利亚地壳的下一代速度模型
地形
- 多尺度地形位置-RGB
- 信息
- 地形湿度指数-1和3弧秒
- 信息
- 地形位置指数-1和3弧秒
- 信息
- 风化强度模型
- 信息
- {info]（https://researchdata.edu.au/weathering-intenty-model-australia/1361069）
北方
- 覆盖厚度tisa->覆盖Tennant Creek Mt Isa的厚度点，带有插值网格
- 使用区域AEM调查和机器学习 - > ML电导率插值的高分辨率电导率映射
- 扩展摘要
- 固体地质 - >北澳大利亚州克拉顿的固体地质
- 反转模型 - >北澳大利亚州craton 3D重力和磁反转模型
- Ni-Cu-pge->澳大利亚入侵托管的Ni-Cu-pge硫化物沉积物的潜力：矿物系统前景性的大陆规模分析
- tisa iocg->氧化铁铜金（IOCG）矿物质的矿物电位评估 - 田纳特小溪 - Mt ISA地区：地理空间数据
- TISA改变 - >使用3D重力和磁反转产生磁铁矿和赤铁矿改变代理
南澳大利亚
地质学
- 基岩地质
- 晶体地下室 - >晶体地下室相交的钻孔
- 矿床和矿藏
- 矿物钻孔
- 固体地质3D
- 100k故障
- 太古代
- 古细菌
- 中元 - >中间
- 中元 - >中间故障
- 中元 - >迟到
- 中元断层 - >晚故障
- 新元古代
- 新元古代断层
- 斯图尔特货架沉积铜3D型号
- 表面地质
地球物理
- Auslamp 3D->磁电逆反转
- GCAS-> Gawler Craton空降调查
- 重力 - >重力网格
- 电台 - >重力站
- 磁性 - >磁力
- 地震线 - >地震线
加勒
- Gawler MPP-> Gawler矿产促销项目 - 数据
昆士兰州
- 概述
- 深矿昆士兰州 - >深矿昆士兰州
- 存入地图集 - >西北矿产省存款地图集
- 地质 - >地质系列概述
- 矿产和能源报告 - >昆士兰西北部矿产和能源省报告2011年报告-NWQMEP
- 矢量 - >矿物地球化学矢量
- 石油井
- 煤层气井
- 钻孔
克隆咖喱
北领地
- Arunta iocg->南部阿伦塔地区的氧化铁氧化铁质势
- 南铀 - >南部北领地铀和地热能系统评估Digil数据包
- Tennant Creek->电导率模型来自北领地东Tennant地区的Magnetoteltotelluric Data
新南威尔士州
地质学
- 无缝地质 - >新南威尔士州无缝地质数据包（此页面也较旧版本）
矿产潜在数据包
- Curnamona
- 东部拉克兰
- 中央拉克兰
- 新英格兰南部
西澳大利亚州
地球化学
地质学
- 100k基岩
- 100K地表的地表您必须单独下载并组合 - 它们不一致
- 250k地表的地图您必须单独下载并组合 - 它们不一致
- 500k基岩
- 废弃矿井
- 矿物发生
矿物潜力
潜在性
- 摩ri座 - >使用矿物系统方法的前景分析 - 摩ri座案例研究项目
- 利奥波德国王 - >国王利奥波尔德造山基因和伦纳德架的矿物准入：西金伯利地区的潜在现场数据分析
- Yilgarn Gold
- Yilgarn 2->东部伊尔加恩·克拉顿（Eastern Yilgarn Craton）的预测矿物发现：造山矿物系统的区域尺度靶向示例
- [商店笔记] - > WA有一些可在USB驱动器上购买的潜在套件，价格为50-60AU类型 - 请参见Geospaital Maps部分
塔斯马尼亚
地质学
维多利亚
新西兰
北美
- 国家规模的地球物理，地质和矿产资源数据和网格 - >也有一些澳大利亚数据
- 地下水井 - >数据库
- 整个北美
加拿大
地质学
- 地图
- 地质 - >更新的基岩地质图
- 地质 - >基岩地质汇编和南方的区域综合以及霍恩域的部分地区，丘吉尔省，西北地区，萨斯喀彻温省，努纳武特，曼尼托巴省和艾伯塔省
- MOHO-> MOHO DEPTH的国家数据库估计估计地震折射和远程震荡调查
地球物理
- DAP搜索 - >地球搜索 - 令人讨厌的注意这些位于Geosoft网格中 - 请参阅elsewere，以了解转换possibilties
- [重力，磁学，放射线学] - >主要是国家规模
欧洲
芬兰
爱尔兰
带有代码的论文
自然语言处理
- https://www.sciendirect.com/science/article/pii/s2590197422000064?via%3dihub#bib20--->地球科学语言模型及其内在评估及其内在评估 - >上面的NRCAN代码
- https://www.researchgate.net/publication/334507958_word_embeddings_for_application_in_geosciences_geosciences_development_evaluation_evaluation_and_examples_of_soil-related_soil-related_concepts-> geovec-> geovec [包括模型]
- https://www.researchgate.net/publication/347902344_portuguese_word_word_embeddings_for_the_oil_oil_oil_oil_oil_ and_gas_industry_development_and_evaluation-petrovec [包括模型]
- 从日记补品中自动搜索和整理地球化学数据集的资源
地球化学
- https://www.researchgate.net/publication/365758387_a_resource_for_automated_search_search_and_collation_of_geochemical_geochemical_datasets_from_journal_supplements
- https://github.com/erinlmartin/figshare_geoscrape?s=09
地质学
- https://github.com/sydney-machine-learning/autoencoders_remotesensing->用于岩性映射的堆叠自动编码器
矿物
- https://www.researchgate.net/publication/318839364_network_analsys_of_mineralogical_systems
具有功能数据的论文
矿物的潜在性
- https://www.sciendirect.com/science/article/pii/s0169136821000x#s0135->>加拿大岩浆Ni（±Cu±Co±Co±PGE）硫化物矿物矿物系统的前瞻性模型
- https://www.sciendirect.com/science/article/pii/s01691368210066612#b0510->>数据驱动的沉积物Zn -PB矿物质矿物质系统及其关键原材料的沉积物Zn – PB矿物质系统的前景建模[值得阅读]
- https://www.researchgate.net/publication/358956673_towards_a_a_a_oul_data-driven_prospectivitivitivitivitivitivitivitivitivitive_mapping_methodology_a_case_study_of_the_southe_southe_southe_southeastern_cherneastern_christeastern_churneastern_christir_province_quincebecbec_quebecbec_labrador.labrador
英格兰
- https://www.researchgate.net/publication/358083076_machine_learning_for_geochemical_geochemical_exploration_classifity_classifying_metallogen_fertitily_in_in_arc_arc_arc_magmas_magmas_insights_into_into_pperphyry_pperphyry_copper_copperper_depeposit_depeposit_deposit_deposit_deposit_formntion
地球化学
- https://www.researchgate.net/publication/361076789_automated_machine_learning_pipeline_pipeline_for_geochemical_analysis
地质学
- https://eprints.utas.edu.au/32368/->机器 - 辅助建模
地球物理
- https://github.com/tomasnaprstek/aeromagnetic_cnn-航空磁CNN
- Paper https://www.researchgate.net/pablication/354772176_convolution_neural_neal_networks_applied_the_the_tthe_tthe_tthe_interpretation_of_lineaments_in_aeromagnetic_data
- 博士学位 - >用于航空磁数据中线的插值和解释的新方法
- 论文https://www.researchgate.net/publication/3547772176_convolution_neural_neal_networks_applied_the_tthe_tthe_interpretation_of_lineaments_in_aeromagnetic_date_data-
地理空间输出 - 无代码
- https://geoscience.data.qld.gov.au/report/cr113697-> NWMP数据驱动的矿物探索和地质映射[CSIRO]
期刊
- https://www.sciendirect.com/journal/artavering-intelligence-in-geosciences->地球科学中的人工智能
文件
- 通常不是ML，或没有代码/数据，有时根本没有可用性
- 最终将分为具有数据包或不像新南威尔士州区域研究的事物。
- 但是，如果对某个区域感兴趣，您通常可以在图片中查看图片，如果没有其他内容作为粗略的指南。
- 通常，这些是不可再现的 - 像新南威尔士州的前瞻性区研究和NWQMP一样，有一些工作。
- 本节中偶尔的论文可能在上面列出
新的文件
一般的
- https://www.researchgate.net/publication/337650865_a_combinative_knowledge-nowledge-drived-drive_integration_method_method_for_for_for_geophysical_geophysical_geophys_geolys_geologation_geological_geological_geological_geochemical_geochemical_datasets
- https://link.springer.com/article/10.1007/s11053-023-023-10237-w-新一代用于矿物前瞻性映射的人工智能算法
- https://www.researchgate.net/publication/235443297_ADDRESSING_CHALLENGES_WITH_WITH_WITH_EXPLOTOR_DATASETS_TO_TO_TO_GENEMER_GENEMERE_USABLE_MINERALE_MINERAL_MINERAL_POTENTER_POTENTION_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-Area_Plot_and_Logistic_Functions
- 矿物探索的人工智能：数据科学未来方向的评论和观点 - > https：//www.sciencedirect.com/science/article/pii/pii/s001282224002691
- https://www.researchgate.net/project/bayesian-machine-machine-learning-for-goological-modeling-modeling-and-ephyphysical-wementical-sepentation
- https://www.researchgate.net/publication/229714681_classifiers_for_modeling_of_mineral_potential
- https://www.researchgate.net/publication/352251078_data_analysis_methods_for_for_prospectivity_modelitivity_modeling_as_applied_to_mineral_mineral_exploration_targoration_targeting_targeting_state_state_state_state of-the-art_and_and_outlook
- https://www.researchgate.net/publication/267927728_data-driven_evidential_belief_modeling_of_mineral_potential_potential_potential_few_few_few_prospects_and_evidess_and_evidence_evidese_with_with_missing_values
- https://www.linkedin.com/pulse/deep-learning-meets-downward-continature-continuation-caldera-analytics/?trackingid = ybkv3ukni7ygh3irchzdgw%3d%3d；
- https://www.researchgate.net/publication/382560010_DINOV2_ROCKS_GEOLICALIC_IMAGE_IMAGE_ANALESIP_CLASSIASION_CLASSIFICAY_SEMENGENTION_ENDEMENGATION_AND_INTERPRETABITY
- https://www.researchgate.net/publication/368489689_discrimination_of_pb-zn_deposit_types_using_sphalerite_geochemistion_geochemistion_new_inew_insights_from_machine_machine_leargorithm
- https://link.springer.com/article/10.1007/s11430-024-1309-9->可解释的矿物潜在映射的可解释人工智能模型
- 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/3399997675_lever_reversible_neural_nearworks_for_for_large-scale_surface_and_surface_and_surface_characterization_varacterization_via_remote_sensing
- https://www.researchgate.net/publication/220164488_GEOCOMPOUNT_OF_MINERAL_EXPLOTORY_TARGETS
- https://www.researchgate.net/publication/272494576_geological_knowledge_discovery_discovery_and_minerals_targeting_from_regolith_regolith_a_a_a_machine_machine_learning_learning_apphack
- https://www.researchgate.net/publication/280013864_GEOMETRIC_AVARE_FAVER_OF_SPATIAL_SPATIAL_DATA_DATA_DATA_GIS_A_GIS_A_GIS基础_multi-Criteria_decision-decision-decision-decision-making_making_apphacd_apphacd_to_to_to_mineral_mineral_prospectivity_mineral_prospectivity_mapping
- https://www.researchgate.net/publication/355467413_harnessing_the_power_power_of_of_artsover_intelligence_and_machine_machine_machine_in_mineral_mineral_mineral_exploration-opportunities-opportunities_and_caution_caution _cautionary_notes
- https://www.researchgate.net/publication/335819474_IMPORTANCE_OF_SPATIAL_SPATIAL_PREDICTOR_VARIABLIABE
- https://www.researchgate.net/publication/337003268_impreved_supervise_classification_classification_bedrock_bedrock_in_areas_areas_of_transported_overburden_opplying_applying_domain_epteres_expertise_expertise_at_at_kerkasha_eritrea-gazley/guazley/guazley/hood
- https://www.researchgate.net/publication/360660467_lithospheric_conductors_reveal_reveal_source_source_egions_of_convergent_margin_mineral_mineral_systems
- https://api.research-repository.uwa.edu.au/portalfiles/portal/5263287/Lysytsyn_Volodymyr_2015.pdf (PhD thesis) GIS-based epithermal copper prospectivity mapping of the Mt Isa Inlier, Australia: Implications for exploration targeting
- https://www.researchgate.net/publication/374972769_Knowledge_and_and_technology_transfer_transfer_in_in_and_beyond_mineral_mineral_mineral_exploration->知识和技术转移
- https://www.researchgate.net/publication/331946100_machine_learning_for_data-driven_discovery_in_solid_earth_geoscience
- https://theses.hal.science/tel-04107211/document-地下地质异质来源的机器学习方法
- https://www.researchgate.net/publication/309715081_magmato-hydrothermal_space_a_a_new_metric_metric_geochemical_geochemical_characterisation_of_metallic_metallic_ore_ore_deposit
- https://www.researchgate.net/publication/220164234_Mapping_complexity_of_spatial_spatial_distribution_of_faults_usings_fractal_fractal_and_multifractal_multifractal_models_models_vector_vectoring_towards_towards_towards_towards_expletorator_targoratorator_targetss
- https://www.researchgate.net/publication/220163838_Objective_selection_selection_of_suitable_unit_cell_cell_size_in_data-data-data-driven_modeling_of_mineral_mineral_prospectivity
- https://www.researchgate.net/publication/273500012_PREDICTION-AREA_P-A_PLOT_PLOT_AND_AND_CC-A_FRACTAL_FRACTAL_ANALSY_TO_TO_CLASSIFY_CLASSIFY_AND_EVALUITAIDE_AND_EVALUATION_EVALUITAIL_EVALUYE_EVINDIAL_MAPS_MINERAL_MAPS_FOR_MINERAL_FOR_MINERAL_FOR_MINERAL_PROSPECTIOTITIOD_MODEITIVE_MODELITIVE_MODELITITIVE
- https://www.researchgate.net/publication/354925136_SOIL-SAMPLE_GEOCHEMISTION_NORMALISED_BY_BY_CLASS_MEMBERHIP_FROM_MACHINE-LEARNT_CLUSTER_CLUSTER_OF_SATELLITE_SATELLITE_GEOPHYS_GEOPHYSICS_DATA [
- https://link.springer.com/article/10.1007/s12665-024-11870-1->依赖于人类感觉参与的地球科学图的不确定性的量化
- https://www.researchgate.net/publication/2354443294_the_effect_of_map-scale_on_geological_geologal_complexity
- https://www.researchgate.net/publication/2354443305_the_effect_of_map_map_scale_scale_geological_geological_geological_complexity_for_computer-aided_exploration_targeting
- https://link.springer.com/article/10.1007/s11053-024-10322-8->工作流诱导的数据驱动的矿物的潜在映射不确定性
矿物的潜在性
澳大利亚
- https://www.mdpi.com/2072-4292/15/16/4074->用于矿物潜在映射的空间数据驱动方法
- https://www.researchgate.net/publication/353253570_A_TRULY_SPATIAL_SPATIAL_RANDOM_FORESTS_ALGORITHM_FOR_FOR_GEOSCIENCE_DATA_DATA_ANALESIS_ANALESIS_ANALESIS_AND_AND_AND_MODELLED
- https://www.researchgate.net/publication/253217016_advanced_methodologies_for_the_analsys_of_databases_of_mineral_mineral_deposits_and_and_major_faults
- https://www.researchgate.net/publication/362260616_assessing_the_impact_of_conceptual_mineral_syeral_systemss_uncternyty_on_prospectivity_predictions_predictions
- https://www.researchgate.net/publication/352310314_central_lachlan_mineral_potential_study
- https://meg.resourcesregulator.nsw.gov.au/sites/default/default/files/2024-05/eith%202024%20muller_exploration_in_the_house_keynote.pdf-
- https://www.tandfonline.com/doi/pdf/10.10.1080/22020586.2019.12073159?NeedAccess = True->>将矿物系统方法与机器学习相结合：MT Woods Inlier的现代矿物探索的案例研究南澳大利亚的Gawler Craton
- https://www.researchgate.net/publication/365697240_mineral_potential_modelling_modelling_of_orogenic_gold_systems_in_the_granites_granites_granites-tanami_orogen_orogen_northern_territory_auttroritory_australia_australia_a _a_a_multi-technique_apphife
- https://publications.csiro.au/publications/publication/publication/pablication/picsiro：ep2022-0483->昆士兰州伊森山省关键矿物系统的签名：来自数据分析的新观点
- https://link.springer.com/article/10.1007/s11004-021-09989-z->矿物勘探目标的随机建模
- https://www.researchgate.net/publication/276171631_supervise_neural_neur_network_network_targeting_and_classification_analsision_of_airborne_airborne_em_magnetic_magnetotic_magnetic_gamma-and_gamma-ray_spectrry_spectrry_dataa_dataa_dataa_dataa_mer_miserer_miseraleral_minereraleral_exextortor
- https://www.researchgate.net/publication/353058758_ususe_machine_learning_to_to_map_western_australian_australian_landscapes_for_mineral_exploration
- https://www.researchgate.net/publication/264535019_weights-of-evidence_and_logistic_regission_modeling_modeling_of_magmatic_magmatic_nickel_nickel_nickel_sulfide_prospection_propection_propetive_privetivity_the_yilgarn_yilgarn_yilgarn_yilgarn_craton_weston_western_western_austern_austern_austern_austern_austern_austern_austern_austern_australia
阿根廷
- https://www.researchgate.net/publication/263542691_analsys_of_spatial_distribution_of_epithermal_gold_gold_gold_deposits_in_the_deseado_deseado_massif_santa_santa_cruz_province
- https://www.researchgate.net/publication/263542560_evidential_belief_mapping_mapping_of_epithermal_gold_potential_potential_in_the_deseado_massif_massif_santa_santa_santa_cruz_province_argentina
- https://www.researchgate.net/publication/277940917_porphyry_epitherry_epithermal_and_orgoent_gold_gold_prospectivity_of_argentina
- https://www.researchgate.net/publication/269518805_prospectivity_for_epithermal_gold-silver_deposits_in_in_the_deseado_massif_argentina
- https://www.researchgate.net/publication/235443303_prospectivity_mappitivity_mapping_for_multi-stage_epither_gold_gold_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_of_Quadrilatero_Ferrifero_Brazil
- 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 -> Enhancing Lithium Exploration in the Borborema Province, Northeast Brazil: Integrating Airborne Geophysics, Low-Density Geochemistry, and Machine Learning Algorithms
- 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 -> Conceptual Fuzzy Logic Prospectivity Analysis of the Kuusamo Area
- https://www.researchgate.net/publication/356508827_Geophysical-spatial_Data_Modeling_using_Fuzzy_Logic_Applied_to_Nova_Aurora_Iron_District_Northern_Minas_Gerais_State_Southeastern_Brazil
- 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 -> Advanced Geoscience Targeting via Focused Machine Learning Applied to the QUEST Project Dataset, British Columbia
- https://open.library.ubc.ca/soa/cIRcle/collections/ubctheses/24/items/1.0340340 -> Application of machine learning algorithms to mineral prospectivity mapping
- 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_driven_mineral_prospectivity_maps_for_Canada's_North
- https://www.researchgate.net/publication/300153215_Data_mining_for_real_mining_A_robust_algorithm_for_prospectivity_mapping_with_uncertainties
- https://www.sciencedirect.com/science/article/pii/S1674987123002268 -> Development and application of feature engineered geological layers for ranking magmatic, volcanogenic, and orogenic system components in Archean greenstone belts
- https://qspace.library.queensu.ca/bitstream/handle/1974/28138/Cevik_Ilkay_S_202009_MASc.pdf?sequence=3&isAllowed=y -> MACHINE LEARNING ENHANCEMENTS FOR KNOWLEDGE DISCOVERY IN MINERAL EXPLORATION AND IMPROVED MINERAL RESOURCE 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.researchgate.net/publication/365782501_Improving_Mineral_Prospectivity_Model_Generalization_An_Example_from_Orogenic_Gold_Mineralization_of_the_Sturgeon_Lake_Transect_Ontario_Canada
- https://www.researchgate.net/publication/348983384_Mineral_prospectivity_mapping_using_a_VNet_convolutional_neural_network
- corporate link
- https://www.researchgate.net/publication/369048379_Mineral_Prospectivity_Mapping_Using_Machine_Learning_Techniques_for_Gold_Exploration_in_the_Larder_Lake_Area_Ontario_Canada
- https://www.researchgate.net/publication/337167506_Orogenic_gold_prospectivity_mapping_using_machine_learning
- https://www.researchgate.net/publication/290509352_Precursors_predicted_by_artificial_neural_networks_for_mass_balance_calculations_Quantifying_hydrothermal_alteration_in_volcanic_rocks
- https://link.springer.com/article/10.1007/s11053-024-10369-7 -> Predictive Modeling of Canadian Carbonatite-Hosted REE +/− Nb Deposits
- https://www.sciencedirect.com/science/article/pii/S0098300422001406 -> Preliminary geological mapping with convolution neural network using statistical data augmentation on a 3D model
- https://www.researchgate.net/publication/352046255_Study_of_the_Influence_of_Non-Deposit_Locations_in_Data-Driven_Mineral_Prospectivity_Mapping_A_Case_Study_on_the_Iskut_Project_in_Northwestern_British_Columbia_Canada
- https://www.researchgate.net/publication/220164155_Support_vector_machine_A_tool_for_mapping_mineral_prospectivity
- https://www.researchgate.net/publication/348111963_Support_Vector_Machine_and_Artificial_Neural_Network_Modelling_of_Orogenic_Gold_Prospectivity_Mapping_in_the_Swayze_greenstone_belt_Ontario_Canada
- PhD thesis -> https://zone.biblio.laurentian.ca/bitstream/10219/3736/1/PhD%20Thesis%20Maepa_20210603.%281%29.pdf -> Exploration targeting for gold deposits using spatial data analytics, machine learning and deep transfer learning in the Swayze and Matheson greenstone belts, Ontario, Canada
- https://data.geology.gov.yk.ca/Reference/95936#InfoTab -> Updates to the Yukon Geological Survey's mineral potential mapping methodology
- http://www.geosciencebc.com/i/pdf/SummaryofActivities2015/SoA2015_Granek.pdf -> Advanced Geoscience Targeting via Focused Machine Learning Applied to the QUEST Project Dataset, British Columbia
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
- https://www.researchgate.net/publication/334436808_Lithological_Mapping_in_the_Central_African_Copper_Belt_using_Random_Forests_and_Clustering_Strategies_for_Optimised_Results
智利
- https://www.researchgate.net/publication/341485750_Evaluation_of_random_forest-based_analysis_for_the_gypsum_distribution_in_the_Atacama_desert
中国
- https://www.researchgate.net/publication/374968979_3D_cooperative_inversion_of_airborne_magnetic_and_gravity_gradient_data_using_deep_learning_techniques - 3D cooperative inversion of airborne magnetic and gravity gradient data using deep learning techniques [UNSEEN]
- https://www.researchgate.net/publication/369919958_3D_mineral_exploration_Cu-Zn_targeting_with_multi-source_geoscience_datasets_in_the_Weilasituo-bairendaba_district_Inner_Mongolia_China
- https://www.researchgate.net/publication/350817136_3D_Mineral_Prospectivity_Mapping_Based_on_Deep_Metallogenic_Prediction_Theory_A_Case_Study_of_the_Lala_Copper_Mine_Sichuan_China
- https://www.researchgate.net/publication/336771580_3D_Mineral_Prospectivity_Mapping_with_Random_Forests_A_Case_Study_of_Tongling_Anhui_China
- https://www.sciencedirect.com/science/article/pii/S0169136823005772 -> 3D mineral prospectivity modeling in the Sanshandao goldfield, China using the convolutional neural network with attention mechanism
- https://www.sciencedirect.com/science/article/pii/S0009281924001144 -> 3D mineral prospectivity modeling using deep adaptation network transfer learning: A case study of the Xiadian gold deposit, Eastern China
- https://www.sciencedirect.com/science/article/pii/S0009281924000497 -> 3D mineral prospectivity modeling using multi-scale 3D convolution neural network and spatial attention approaches
- https://www.researchgate.net/publication/366201930_3D_Quantitative_Metallogenic_Prediction_of_Indium-Rich_Ore_Bodies_in_the_Dulong_Sn-Zn_Polymetallic_Deposit_Yunnan_Province_SW_China
- 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
- https://www.researchgate.net/publication/349034539_A_Comparative_Study_of_Machine_Learning_Models_with_Hyperparameter_Optimization_Algorithm_for_Mapping_Mineral_Prospectivity
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埃及
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英格兰
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厄立特里亚
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芬兰
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芬兰
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加纳
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格陵兰
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印度
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印度尼西亚
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伊朗
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- https://www.researchgate.net/publication/220164381_Application_of_geochemical_zonality_coefficients_in_mineral_prospectivity_mapping
- https://www.researchgate.net/publication/330359897_Application_of_hybrid_AHP-TOPSIS_method_for_prospectivity_modeling_of_Cu_porphyry_in_Varzaghan_district_Iran
- https://www.researchgate.net/publication/356872819_Application_of_self-organizing_map_SOM_and_K-means_clustering_algorithms_for_portraying_geochemical_anomaly_patterns_in_Moalleman_district_NE_Iran
- https://www.researchgate.net/publication/258505300_Application_of_staged_factor_analysis_and_logistic_function_to_create_a_fuzzy_stream_sediment_geochemical_evidence_layer_for_mineral_prospectivity_mapping
- https://www.researchgate.net/publication/358567148_Applications_of_data_augmentation_in_mineral_prospectivity_prediction_based_on_convolutional_neural_networks
- https://www.researchgate.net/publication/353761696_Assessing_the_effects_of_mineral_systems-derived_exploration_targeting_criteria_for_Random_Forests-based_predictive_mapping_of_mineral_prospectivity_in_Ahar-Arasbaran_area_Iran
- https://www.researchgate.net/publication/270586282_Data-Driven_Index_Overlay_and_Boolean_Logic_Mineral_Prospectivity_Modeling_in_Greenfields_Exploration
- https://www.researchgate.net/publication/356660905_Deep_GMDH_Neural_Networks_for_Predictive_Mapping_of_Mineral_Prospectivity_in_Terrains_Hosting_Few_but_Large_Mineral_Deposits
- https://www.researchgate.net/publication/317240761_Enhancement_and_Mapping_of_Weak_Multivariate_Stream_Sediment_Geochemical_Anomalies_in_Ahar_Area_NW_Iran
- https://www.sciencedirect.com/science/article/pii/S0009281924001223 -> Enhancing training performance of convolutional neural network algorithm through an autoencoder-based unsupervised labeling framework for mineral exploration targeting
- https://www.researchgate.net/publication/356580903_Evidential_data_integration_to_produce_porphyry_Cu_prospectivity_map_using_a_combination_of_knowledge_and_data_driven_methods
- https://research-repository.uwa.edu.au/en/publications/exploration-feature-selection-applied-to-hybrid-data-integration-Exploration feature selection applied to hybrid data integrationmodeling: Targeting copper-gold potential in central
- https://www.researchgate.net/publication/333199619_Incorporation_of_principal_component_analysis_geostatistical_interpolation_approaches_and_frequency-space-based_models_for_portraying_the_Cu-Au_geochemical_prospects_in_the_Feizabad_district_NW_Iran
- https://www.researchgate.net/publication/351965039_Intelligent_geochemical_exploration_modeling_using_multiclass_support_vector_machine_and_integration_it_with_continuous_genetic_algorithm_in_Gonabad_region_Khorasan_Razavi_Iran
- https://www.researchgate.net/publication/310658663_Multifractal_interpolation_and_spectrum-area_fractal_modeling_of_stream_sediment_geochemical_data_Implications_for_mapping_exploration_targets
- https://www.researchgate.net/publication/267635150_Multivariate_regression_analysis_of_lithogeochemical_data_to_model_subsurface_mineralization_A_case_study_from_the_Sari_Gunay_epithermal_gold_deposit_NW_Iran
- https://www.researchgate.net/publication/330129457_Performance_evaluation_of_RBF-_and_SVM-based_machine_learning_algorithms_for_predictive_mineral_prospectivity_modeling_integration_of_S-A_multifractal_model_and_mineralization_controls
- 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
- https://www.researchgate.net/publication/349957803_Regional-Scale_Mineral_Prospectivity_Mapping_Support_Vector_Machines_and_an_Improved_Data-Driven_Multi-criteria_Decision-Making_Technique
- 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
- https://www.researchgate.net/publication/304904242_Stepwise_regression_for_recognition_of_geochemical_anomalies_Case_study_in_Takab_area_NW_Iran
- https://www.researchgate.net/publication/350423220_Supervised_mineral_exploration_targeting_and_the_challenges_with_the_selection_of_deposit_and_non-deposit_sites_thereof
- 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
- https://www.researchgate.net/publication/307874730_The_use_of_decision_tree_induction_and_artificial_neural_networks_for_recognizing_the_geochemical_distribution_patterns_of_LREE_in_the_Choghart_deposit_Central_Iran
- https://www.researchsquare.com/article/rs-4760956/v1 -> Uncertainty reduction with Hyperparameter Optimization in mineral prospectivity mapping: A Regularized Artificial Neural Network approach [UNSEEN]
爱尔兰
- 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
韩国
- 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
韩国
- 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
菲律宾
- 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
俄罗斯
- 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
苏丹
- 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
坦桑尼亚
- 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
乌干达
- 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
英国
- https://www.researchgate.net/publication/383580839_Improved_mineral_prospectivity_mapping_using_graph_neural_networks
美国
- 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: Examples from the Rosemont Cu-Mo-Ag skarn deposit, SE Tucson亚利桑那
- [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
赞比亚
- 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
津巴布韦
- 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
环境、社会及治理
- 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 Case of锂
地球化学
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_geochemistry_New_insights_from_machine_learning_algorithm
- 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 sediments: A case study of Xupu, Hunan Province [UNSEEN ]
- 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
地质学
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
钻芯
- 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,
地质年代学
- https://www.researchgate.net/publication/379077847_Tracing_Andean_Origins_A_Machine_Learning_Framework_for_Lead_Isotopes
地貌学
- 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
矿物学
- 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
地层学
- 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
结构
- 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
构造学
- 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
基础
- 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 Markov chain Monte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
紧急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
高光谱
- 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]
联合反演
- 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]
磁学
- 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
大地电磁学
- 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
表面电阻率
- https://www.researchgate.net/publication/367606119_Deriving_Surface_Resistivity_from_Polarimetric_SAR_Data_Using_Dual-Input_UNet
不确定
- 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]
地热
- 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
地图
- https://www.researchgate.net/publication/347786302_Semantic_Segmentation_Deep_Learning_for_Extracting_Surface_Mine_Extents_from_Historic_Topographic_Maps
矿物
- 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
自然语言处理
- 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
地球化学
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
不确定
- 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
澳大利亚
- https://www.researchgate.net/publication/334440382_Mapping_iron_oxide_Cu-Au_IOCG_mineral_potential_in_Australia_using_a_knowledge-driven_mineral_systems-based_approach
南澳大利亚
- 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
新台币
- 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 CSIRO Prospectivity analysis using a mineral systems approach - Capricorn case study project (13.5 GB下载）
- 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
澳大利亚
- 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
新台币
- 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
新南威尔士州
- 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
澳大利亚
- 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
南澳大利亚
- 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
昆士兰州
- 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
新南威尔士州
- 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
塔斯马尼亚
- 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
维多利亚
- 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
西澳大利亚州
- 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_of_Mineral_Prospectivity_to_Quantitative_Estimation_of_Number_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]
Spectral Unmixing
- Overviews and examples, with some focus on neural network approaches.
神经网络
- 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
中国
- 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
格陵兰
- 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
秘鲁
- 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
自然语言处理
- 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?针对地球科学训练的基于 GPT 的人工智能系统的性能分析 (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
法学硕士
- 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?针对地球科学训练的基于 GPT 的人工智能系统的性能分析 (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
深度学习
- 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
