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Imperial Valley Dark Fiber Project Continuous DAS Data

The Imperial Valley Dark Fiber Project acquired Distributed Acoustic Sensing (DAS) seismic data on a ~28 km segment of dark fiber between the cities of Calipatria and Imperial in the Imperial Valley, Southern California. Dark fiber refers to unused optical fiber cables in telecomm...

Ajo-Franklin, J. et al Lawrence Berkeley National Laboratory

Nov 10, 2020
4 Resources
0 Stars
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PoroTomo Natural Laboratory Horizontal and Vertical Distributed Acoustic Sensing Data

This dataset includes links to the PoroTomo DAS data in both SEG-Y and hdf5 (via h5py and HSDS with h5pyd) formats with tutorial notebooks for use. Data are hosted on Amazon Web Services (AWS) Simple Storage Service (S3) through the Open Energy Data Initiative (OEDI). Also include...

Feigl, K. et al University of Wisconsin

Mar 29, 2016
20 Resources
0 Stars
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Utah FORGE: Triggered DAS and Continuous Downhole Geophone Data from April 2024 Stimulations

This dataset contains distributed acoustic sensing (DAS) and downhole geophone data collected during the April 2024 stimulation experiments at the Utah FORGE site. DAS data were acquired from well 16B(78)-32 by Neubrex and processed by GeoEnergie Suisse (GES), covering the period ...

Dyer, B. et al Energy and Geoscience Institute at the University of Utah

Apr 01, 2024
5 Resources
0 Stars
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Transient Data Library of Solar Grid Integrated Distributed System

This submission contains an open-source library of transient events in distributed system with high solar PV. The library includes the collected data, related documents and scripts for loading the data. The data library is built for transient event detection and machine learning b...

Kuruganti, T. and Dong, Y. Oak Ridge National Laboratory

Nov 10, 2022
3 Resources
0 Stars
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Tracking the Sun

Berkeley Lab's Tracking the Sun report series is dedicated to summarizing installed prices and other trends among grid-connected, distributed solar photovoltaic (PV) systems in the United States. The present report, the 11th edition in the series, focuses on systems installed thro...

Barbose, G. and Darghouth, N. Lawrence Berkeley National Laboratory (LBNL)

Oct 01, 2019
6 Resources
1 Stars
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GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources

Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Di...

Ahmmed, B. Stanford University

Apr 04, 2022
3 Resources
0 Stars
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