Data Releases
The data collected and the techniques used by USGS scientists should conform to or reference national and international standards and protocols if they exist and when they are relevant and appropriate. For datasets of a given type, and if national or international metadata standards exist, the data are indexed with metadata that facilitates access and integration.
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USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Geophysics Data USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Geophysics Data
This package contains gravity and magnetics data and products generated for the Nevada Machine Learning (NVML) project (DE-FOA-0001956). Data products contained in this release consist of grids and vector data. Grids include: primary anomaly maps (isostatic and PSG), match-filtered maps, horizontal gradient (HG) maps, confidence maps, and maps showing density of specific key structural...
USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Geophysics, Heat Flow, Slip and Dilation Tendency 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...
USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Heat Flow Data USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Heat Flow Data
This package contains a map surface that depicts the estimated spatial variation of conductive heat flow (mW/m?) in a portion of northern Nevada, the extent of the ?Nevada Machine Learning Project? (DE-EE0008762). It was generated using well locations that had an estimated heat flow value from a measured thermal gradient and thermal conductivity, mainly using data from Southern Methodist
USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Slip and Dilation Tendency Data USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Slip and Dilation Tendency Data
This package contains data in a portion of northern Nevada, the extent of the ?Nevada Machine Learning Project? (DE-EE0008762). Slip tendency (TS) and dilation tendency (TD) were calculated for the all the faults in the Nevada ML study area. TS is the ratio between the shear components of the stress tensor and the normal components of the stress tensor acting on a fault plane. TD is the...
Hydrologic Data Collected at Leaky Weirs, Cienega Ranch, Willcox, AZ (March 2019 - October 2020) Hydrologic Data Collected at Leaky Weirs, Cienega Ranch, Willcox, AZ (March 2019 - October 2020)
This dataset contains hydrological data collected at a series of leaky weirs on a working ranchland site in a semiarid ecosystem in Cochise County, Arizona, from 2018-2020. Leaky weirs are a type of structure being experimented with by land managers in aridlands to reduce peak flow events and increase recharge to the aquifer. The weirs are constructed of rock cemented into place in areas...
Southwest Gravity Program Absolute-Gravity Database (updated 2025-12-19) Southwest Gravity Program Absolute-Gravity Database (updated 2025-12-19)
This dataset contains absolute-gravity data collected by the USGS Southwest Gravity Program, a collaborative effort of the Arizona, California, and New Mexico Water Science Centers to monitor and model groundwater-storage change. Data were collected following the methods in "Procedures for Field Data Collection, Processing, Quality Assurance and Quality Control, and Archiving of Relative...