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

Marone, Chris

doi:10.15121/1787546

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

Dataset
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Abstract

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.

Authors:

Marone, Chris

Pennsylvania State University

Publication Date:: Wed May 05 00:00:00 EDT 2021

Other Number(s):: 1310

DOE Contract Number:: EE0008763

Research Org.:: DOE Geothermal Data Repository; Pennsylvania State University

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)

Collaborations:: Pennsylvania State University

Subject:: 15 GEOTHERMAL ENERGY; EGS; MEQ; ML; Newberry; Newberry Volcanic Site; Newberry Volcano; NumPy; Oregon; Python; ai; artificial intelligence; code; deep learning; energy; engineered geothermal systems; enhanced geothermal systems; geophysical; geophysics; geothermal; machine learning; microearthquake; microseismicity; preprocessed; processed data; raw data; seismic; waveform

OSTI Identifier:: 1787546

DOI:: https://doi.org/10.15121/1787546

Citation Formats


                    Marone, Chris. Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites.  United States: N. p., 2021. 
        Web.  doi:10.15121/1787546.

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                    Marone, Chris. Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites.  United States.  doi:https://doi.org/10.15121/1787546

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                    Marone, Chris. 2021.  
"Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites".  United States.  doi:https://doi.org/10.15121/1787546.  https://www.osti.gov/servlets/purl/1787546. Pub date:Wed May 05 00:00:00 EDT 2021

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@article{osti_1787546,

  title        = {Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites},

  author       = {Marone, Chris},

  abstractNote = {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.},

  doi          = {10.15121/1787546},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Wed May 05 00:00:00 EDT 2021},

  month        = {Wed May 05 00:00:00 EDT 2021}

}

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DOI: https://doi.org/10.15121/1787546

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