Real-time multi-station microseismic event detection via transfer learning of computer vision deep learning model

Clicks: 12
ID: 326918
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #287 of 337 articles by views in geophysical journal international

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 337 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Summary With the shale gas exploration and development into deep strata (> 3500 m in depth), weak signal detection becomes more challenging and vital in surface-based microseismic monitoring. Most existing deep learning approaches are trained on synthetic or single-station data, which limits their performance in detecting weak events in field seismic recordings. Here, we propose a deep learning approach based on the computer vision object detection model YOLOv5 via transfer learning and train it on a dataset from field observation to better detect microseismic events in multi-channel seismic data. First, we convert continuous seismic data into waveform images in grayscale with a record length of 20 s, and manually annotate the microseismic events containing the first arrival signals using rectangular boxes. To train the model, the transfer learning strategy is used by using parameters of YOLOs in computer vision to initiate the model. After training and validation, we conducted comprehensive tests to examine the performance of the model. Compared with the single-station short-term average/long-term average (STA/LTA) detector, our method reduces false positives markedly, yet it also delivers higher recall rate than the available multi-station detection scheme. When applied to continuous dataset from a different area, the model achieved good performance with a precision of 0.9832 and a recall of 0.9702, indicating it learns representative features of microseismic signals. All these tests illustrate that the proposed method achieves high detection accuracy, low false positive rates, and robustness. Meanwhile, the method possesses the superiority for real-time monitoring and can be easily updated by adding a few new samples to the training dataset.
Reference Key
openalex_W7205002769 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chunlan Xie, Chaoliang Wang, C. Liang, Jian Tang
Are you one of these authors? Claim your profile
Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag354
URL
https://doi.org/10.1093/gji/ggag354
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.