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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.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
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|
| contactPoint |
{
"fn": "Chris Marone",
"@type": "vcard:Contact",
"hasEmail": "mailto:cjm38@psu.edu"
}
|
| dataQuality |
true
|
| description | 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. |
| distribution |
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{
"@type": "dcat:Distribution",
"title": "Normalized Waveform Inputs.npz",
"format": "npz",
"accessURL": "https://gdr.openei.org/files/1310/fieldDatapair0729_nor.npz",
"mediaType": "application/octet-stream",
"description": "Includes normalized waveform inputs for the 149 recorded MEQs. The data is in a .npz file which requires Python and NumPy to open. These inputs are for use with the deep learning algorithm."
},
{
"@type": "dcat:Distribution",
"title": "Processed Waveform Inputs.npz",
"format": "npz",
"accessURL": "https://gdr.openei.org/files/1310/input_labelNewberry0730.npz",
"mediaType": "application/octet-stream",
"description": "Includes processed waveforms of the 149 recorded MEQs which act as inputs for the deep learning algorithm. The data is in a .npz file which requires Python and NumPy to open. These inputs are for use with the deep learning algorithm."
},
{
"@type": "dcat:Distribution",
"title": "Location Data.npz",
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"accessURL": "https://gdr.openei.org/files/1310/locationData0805.npz",
"mediaType": "application/octet-stream",
"description": "Includes catalog locations of the 149 recorded MEQs. The data is in a .npz file which requires Python and NumPy to open. Locations are given in the form of coordinates."
},
{
"@type": "dcat:Distribution",
"title": "Raw Waveform Data.npz",
"format": "npz",
"accessURL": "https://gdr.openei.org/files/1310/rawDataNewBerry_0923.npz",
"mediaType": "application/octet-stream",
"description": "Includes raw waveforms of the 149 recorded microearthquakes. The data is in a .npz file which requires Python and NumPy to open. "
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]
|
| DOI | 10.15121/1787546 |
| identifier | https://data.openei.org/submissions/7428 |
| issued | 2021-05-05T06:00:00Z |
| keyword |
[
"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"
]
|
| landingPage | https://gdr.openei.org/submissions/1310 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2021-06-10T15:44:52Z |
| programCode |
[
"019:006"
]
|
| projectLead | Mike Weathers |
| projectNumber | EE0008763 |
| projectTitle | Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties |
| publisher |
{
"name": "Pennsylvania State University",
"@type": "org:Organization"
}
|
| spatial |
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|
| title | Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites |
