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.
Citation Formats
TY - DATA
AB - 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.
AU - Zhu, Tieyuan
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO - 10.15121/1787546
KW - geothermal
KW - energy
KW - code
KW - deep learning
KW - machine learning
KW - ai
KW - artificial intelligence
KW - EGS
KW - enhanced geothermal systems
KW - engineered geothermal systems
KW - Newberry
KW - Oregon
KW - Newberry Volcano
KW - ML
KW - raw data
KW - processed data
KW - microseismicity
KW - NumPy
KW - waveform
KW - preprocessed
KW - Python
KW - Newberry Volcanic Site
KW - microearthquake
KW - MEQ
KW - seismic
KW - geophysics
KW - geophysical
LA - English
DA - 2021/05/05
PY - 2021
PB - Pennsylvania State University
T1 - Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites
UR - https://doi.org/10.15121/1787546
ER -
Zhu, Tieyuan. Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites. Pennsylvania State University, 5 May, 2021, GDR. https://doi.org/10.15121/1787546.
Zhu, T. (2021). Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites. [Data set]. GDR. Pennsylvania State University. https://doi.org/10.15121/1787546
Zhu, Tieyuan. Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites. Pennsylvania State University, May, 5, 2021. Distributed by GDR. https://doi.org/10.15121/1787546
@misc{OEDI_Dataset_7428,
title = {Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites},
author = {Zhu, Tieyuan},
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.},
url = {https://gdr.openei.org/submissions/1310},
year = {2021},
howpublished = {GDR, Pennsylvania State University, https://doi.org/10.15121/1787546},
note = {Accessed: 2025-05-03},
doi = {10.15121/1787546}
}
https://dx.doi.org/10.15121/1787546
Details
Data from May 5, 2021
Last updated Jun 10, 2021
Submitted May 5, 2021
Organization
Pennsylvania State University
Contact
Chris Marone
Authors
Original Source
https://gdr.openei.org/submissions/1310Research Areas
Keywords
geothermal, energy, code, deep learning, machine learning, ai, artificial intelligence, EGS, enhanced geothermal systems, engineered geothermal systems, Newberry, Oregon, Newberry Volcano, ML, raw data, processed data, microseismicity, NumPy, waveform, preprocessed, Python, Newberry Volcanic Site, microearthquake, MEQ, seismic, geophysics, geophysicalDOE Project Details
Project Name Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties
Project Lead Mike Weathers
Project Number EE0008763
