USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Geophysics, Heat Flow, Slip and Dilation Tendency

This package contains USGS data contributions to the DOE-funded Nevada Geothermal Machine Learning Project (DE-FOA-0001956), with the objective of developing a machine learning approach to identifying new geothermal systems in the Great Basin. This package contains three major data products (geophysics, heat flow, and fault dilation and slip tendencies) that cover a large portion of northern Nevada. The geophysics data include map surfaces related to gravity and magnetic data, and line and point data derived from those surfaces. Heat flow data include an interpolated map of heat flow in mW/m?, an error surface, and well data used to construct them. The dilation and slip tendency information exist as attributes assigned to each line segment of mapped faults and geophysical lineaments.

本数据集包含美国地质调查局（United States Geological Survey, USGS）向美国能源部（Department of Energy, DOE）资助的内华达地热机器学习项目（DE-FOA-0001956）所贡献的数据，该项目旨在开发机器学习方法以识别大盆地地区的新型地热系统。本数据集包含三大类核心数据产品，分别为地球物理学数据、热流数据以及断层扩张与滑动倾向数据，覆盖内华达州北部大片区域。其中，地球物理学数据涵盖与重力、磁测数据相关的地图曲面，以及由这些曲面衍生出的线状与点状数据；热流数据包含以mW/m²为单位的热流插值地图、误差曲面，以及用于构建上述数据的钻井数据；断层扩张与滑动倾向信息则以属性字段的形式，被赋予至已测绘断层与地球物理线性构造的每一段线段之上。