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 advance geothermal exploration and safe geothermal energy production. As part of the project, this submission provides data arrays for 149 microearthquakes between the year 2012 and 2013 at the Newberry EGS Site for use with the Deep Learning Algorithm that has been developed. The data provided includes raw waveform data, location data, normalized waveform data, and processed waveform data.

Penn State Geothermal Team has shared the following files from the project: - 149 microearthquakes (MEQs) between 2012 and 2013 at Newberry EGS sites, 'Normalized Waveform Inputs.npz' are normalized waveforms. - labels of 149 MEQs: Processed Waveform Inputs.npz - location labels of 149 MEQs: Location Data.npz Note: .npz is the python file format by NumPy that provides storage of array data.

Data e Risorse

Campo Valore
DOI 10.15121/1787546
accessLevel public
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dataQuality true
identifier https://data.openei.org/submissions/4077
issued 2021-05-05T06:00:00Z
landingPage https://gdr.openei.org/submissions/1310
license https://creativecommons.org/licenses/by/4.0/
modified 2021-06-10T15:44:52Z
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programCode {019:006}
projectLead Mike Weathers
projectNumber EE0008763
projectTitle Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties
publisher Pennsylvania State University
resource-type Dataset
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Gruppi
  • AmeriGEOSS
  • National Provider
  • North America
Tag
  • ai
  • amerigeo
  • amerigeoss
  • artificial-intelligence
  • ckan
  • code
  • deep-learning
  • egs
  • energy
  • engineered-geothermal-systems
  • enhanced-geothermal-systems
  • geo
  • geophysical
  • geophysics
  • geoss
  • geothermal
  • machine-learning
  • meq
  • microearthquake
  • microseismicity
  • ml
  • national
  • newberry
  • newberry-volcanic-site
  • newberry-volcano
  • north-america
  • numpy
  • oregon
  • preprocessed
  • processed-data
  • python
  • raw-data
  • seismic
  • united-states
  • waveform
isopen True
license_id cc-by
license_title Creative Commons Attribution
license_url http://www.opendefinition.org/licenses/cc-by
maintainer Chris Marone
maintainer_email cjm38@psu.edu
metadata_created 2025-11-23T00:34:44.335461
metadata_modified 2025-11-23T00:34:44.335465
notes 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 advance geothermal exploration and safe geothermal energy production. As part of the project, this submission provides data arrays for 149 microearthquakes between the year 2012 and 2013 at the Newberry EGS Site for use with the Deep Learning Algorithm that has been developed. The data provided includes raw waveform data, location data, normalized waveform data, and processed waveform data. Penn State Geothermal Team has shared the following files from the project: - 149 microearthquakes (MEQs) between 2012 and 2013 at Newberry EGS sites, 'Normalized Waveform Inputs.npz' are normalized waveforms. - labels of 149 MEQs: Processed Waveform Inputs.npz - location labels of 149 MEQs: Location Data.npz Note: .npz is the python file format by NumPy that provides storage of array data.
num_resources 4
num_tags 35
title Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites