Automatic location of surface-monitored microseismicity with deep learning
Year: 2024
Type: article
Abstract: Accurate and rapid determination of source locations is of great significance for surface microseismic monitoring. Traditional methods, such as diffraction stacking, are time-consuming and challenging for real-time monitoring. In this study, we propose an approach to locate microseismic events using a deep learning algorithm with surface data. A fully convolutional network is designed to predict source locations. The input data is the waveform of a microseismic event, and the output consists of three 1D Gaussian distributions representing the probability distribution of the source location in the x , y , and z dimensions. The theoretical dataset is generated to train the model, and several data augmentation methods are applied to reduce discrepancies between the theoretical and field data. After applying the trained model to field data, the results demonstrate that our method is fast and achieves comparable location accuracy to the traditional diffraction stacking location method, making it promising for real-time microseismic monitoring.
Source: Earthquake research advances
Authors: Zhaolong Gan, Xiao Tian, Xiong Zhang, Mengxue Dai
Institution: East China University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $800
OpenAlex ID: W4405901161