Machine Learning for Microseismic - A Python package for implementing automated microseismic event detection with machine learning. This is also the companion repository for our paper titled "Using Supervised Machine Learning to Distinguish Microseismic from Noise Events".
This package was translated from previous Matlab codes. As Python is more popular in machine learning and deep learning, we decided to use Python for future coding.
The data used in this project comes from various sources. Users should prepare their own data for training purpose.
The code is divided as follows:
The ml4ms.py python file contains the necessary code to run an experiement.
The utils folder contains the necessary functions to read the datasets and visualize the plots.
All python packages needed are listed below and can be installed simply using the pip command.
Our results showed that a SVM classifier with Gaussian kernel performs best for the time series (microseismic and noise events) classification task.
If you re-use this work, please cite:
Zhao, Z., & Gross, L. (2017). Using supervised machine learning to distinguish microseismic from noise events. In SEG Technical Program Expanded Abstracts 2017 (pp. 2918-2923). Society of Exploration Geophysicists.
or
@incollection{zhao2017using,
title={Using supervised machine learning to distinguish microseismic from noise events},
author={Zhao, Zhengguang and Gross, Lutz},
booktitle={SEG Technical Program Expanded Abstracts 2017},
pages={2918--2923},
year={2017},
publisher={Society of Exploration Geophysicists}
}