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Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites

The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to Microearthquake (MEQ) data for imaging geothermal reservoir properties and forecasting seismic events, in order to...

Zhu, T. Pennsylvania State University

May 05, 2021
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Utah FORGE: Seismic Activity from April, 2019

This dataset contains seismic event detections acquired using the 151 Nodal geophones deployed at the Utah FORGE site in April 2019. Details regarding the publishing are available in the paper linked below (Mesimeri, M. and K. L. Pankow et al. 2020). A frequency-domain-based algor...

Pankow, K. University of Utah Seismograph Stations

May 01, 2019
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EGS Collab Experiment 1: Accelerometer orientations

Document describing the methodology used to determine the accelerometers' three-component orientations at the first EGS Collab testbed using Continuous Active-Source Seismic Monitoring (CASSM) data and hodogram analysis. Original submission: gdr.openei.org/submissions/1166

Hopp, C. et al Lawrence Berkeley National Laboratory

Aug 19, 2020
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Mapping Fracture Network Creation with Microseismicity During EGS Demonstrations

This a report for the project "Mapping Fracture Network Creation with Microseismicity During EGS Demonstrations". Effective enhanced geothermal systems (EGS) require optimal fracture networks for efficient heat transfer between hot rock and fluid. Microseismic mapping is a key too...

Templeton, D. et al Lawrence Livermore National Laboratory

Apr 18, 2014
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Full Moment Tensor Inversion Software

The link points to a website at NCEDC to download the full moment tensors inversion software The moment tensor analysis conducted in the current project is based on the full moment tensor model described in Minson and Dreger (2008). The software including source, examples and tut...

Gritto, R. and Dreger, D. Array Information Technology

Oct 31, 2018
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Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a n...

Marone, C. et al Pennsylvania State University

May 14, 2021
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EGS Collab Experiment 1: Microseismic Monitoring

The U.S. Department of Energy's Enhanced Geothermal System (EGS) Collab project aims to improve our understanding of hydraulic stimulations in crystalline rock for enhanced geothermal energy production through execution of intensely monitored meso-scale experiments. The first expe...

Schoenball, M. et al Lawrence Berkeley National Laboratory

Jul 29, 2019
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