Automatic location of surface-monitored microseismicity with deep learning

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.

Keywords

Microseismic monitoring / Source location / Deep learning

Data availability statement

The codes and a numerical test for the 1D label-based FCN can be accessed on GitHub via the following link: https://github.com/ XiaoTian09/1D-label-based-FCN. The repository includes the network architecture and a numerical case. The training datasets are not provided due to their large size (about 40 GB). Instead, a training dataset demo with 2000 samples is available to run the network.

CRediT authorship contribution statement

Zhaolong Gan: Writing - original draft, Validation, Software, Methodology. Xiao Tian: Writing - review & editing, Resources, Methodology, Investigation, Funding acquisition. Xiong Zhang: Writing review & editing, Visualization, Software. Mengxue Dai: Writing - review & editing, Investigation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author agreement and acknowledgement

I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously and is not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.

This study was supported by National Natural Science Foundation of China Grant (No. 42004040, 42474092, U2239204, and 42304145) and Natural Science Foundation of Jiangxi Province Grant (20242BAB25190 and 20232BAB213077).

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