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.

《基于机器学习预测诱发地震性及成像地热储层特性的方法》项目旨在将机器学习（ML）方法应用于微震（MEQ）数据，以实现地热储层特性的成像和地震事件的预测，从而推进地热勘探与地热能源的安全生产。作为项目的一部分，本提交提供了2012年至2013年间在Newberry EGS场地发生的149次微震的数据数组，以便用于与已开发的深度学习算法相结合。所提供的数据包括原始波形数据、位置数据、标准化波形数据和处理后的波形数据。

宾夕法尼亚州立地热团队已从项目中共享以下文件：
- 2012年至2013年间在Newberry EGS场地记录的149次微震（MEQ），其中'标准化波形输入.npz'包含标准化波形。
- 149次MEQ的标签：'处理后的波形输入.npz'
- 149次MEQ的位置标签：'位置数据.npz'
注：.npz是NumPy的Python文件格式，用于数组数据的存储。