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
Pennsylvania State University. (2021). Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites [data set]. Retrieved from https://dx.doi.org/10.15121/1787546.
Zhu, Tieyuan. Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites. United States: N.p., 05 May, 2021. Web. doi: 10.15121/1787546.
Zhu, Tieyuan. Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites. United States. https://dx.doi.org/10.15121/1787546
Zhu, Tieyuan. 2021. "Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites". United States. https://dx.doi.org/10.15121/1787546. https://gdr.openei.org/submissions/1310.
@div{oedi_4077, 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.}, doi = {10.15121/1787546}, url = {https://gdr.openei.org/submissions/1310}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {05}}
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
