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

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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.
Export Citation to RIS
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_1310, 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

Tieyuan Zhu

Pennsylvania State University

DOE Project Details

Project Name Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties

Project Lead Mike Weathers

Project Number EE0008763

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