Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals
Microseismic monitoring is essential for understanding subsurface dynamics and optimizing oil and gas production. However, traditional methods for the automatic detection of microseismic events rely heavily on characteristic functions and human intervention, often resulting in suboptimal performance...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2025-06-01
|
| Series: | Artificial Intelligence in Geosciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666544125000255 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849424591602057216 |
|---|---|
| author | Kun Chen Meng Li Xiaolian Li Guangzhi Cui Jia Tian JiaLe Li RuoYao Mu JunJie Zhu |
| author_facet | Kun Chen Meng Li Xiaolian Li Guangzhi Cui Jia Tian JiaLe Li RuoYao Mu JunJie Zhu |
| author_sort | Kun Chen |
| collection | DOAJ |
| description | Microseismic monitoring is essential for understanding subsurface dynamics and optimizing oil and gas production. However, traditional methods for the automatic detection of microseismic events rely heavily on characteristic functions and human intervention, often resulting in suboptimal performance when dealing with complex and noisy data. In this study, we propose a novel approach that leverages deep learning frame to extract multiscale features from microseismic data using a TransUNet neural network. Our model integrates the advantages of Transformer and UNet architectures to achieve high accuracy in multivariate image segmentation and precise picking of P-wave and S-wave first arrivals simultaneously. We validate our approach using both synthetic and field microseismic datasets recorded from gas storage monitoring and roof fracturing in a coal seam. The robustness of the proposed method has been verified in the testing of synthetic data with various levels of Gaussian and real background noises extracted from field data. The comparisons of the proposed method with UNet and SwinUNet in terms of the model architecture and classification performance demonstrate the TransUNet achieves the optimal balance in its architecture and inference speed. With relatively low inference time and network complexity, it operates effectively in high-precision microseismic phase pickings. This advancement holds significant promise for enhancing microseismic monitoring technology in hydraulic fracturing and reservoir monitoring applications. |
| format | Article |
| id | doaj-art-11aee602ccc04e6a8504e79262d3853c |
| institution | Kabale University |
| issn | 2666-5441 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Geosciences |
| spelling | doaj-art-11aee602ccc04e6a8504e79262d3853c2025-08-20T03:30:05ZengKeAi Communications Co. Ltd.Artificial Intelligence in Geosciences2666-54412025-06-016110012910.1016/j.aiig.2025.100129Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivalsKun Chen0Meng Li1Xiaolian Li2Guangzhi Cui3Jia Tian4JiaLe Li5RuoYao Mu6JunJie Zhu7School of Earth Sciences and Engineering, Xi'an Shiyou University, Xi'an, Shaanxi, 710065, China; Key Laboratory of Intelligent Geophysical Exploration for Oil and Gas Resources of Shaanxi Higher Education Institutions, Xi'an, Shaanxi, 710065, ChinaSchool of Earth Sciences and Engineering, Xi'an Shiyou University, Xi'an, Shaanxi, 710065, China; Key Laboratory of Intelligent Geophysical Exploration for Oil and Gas Resources of Shaanxi Higher Education Institutions, Xi'an, Shaanxi, 710065, China; Corresponding author. School of Earth Sciences and Engineering, Xi'an Shiyou University, Xi'an, Shaanxi, 710065, China.Research Institute of Petroleum Exploration&Development, Beijing, 10083, ChinaE & D Research Institute of PetroChina Liaohe Oilfield Company, Panjin, Liaoning, 124010, ChinaSchool of Earth Sciences and Engineering, Xi'an Shiyou University, Xi'an, Shaanxi, 710065, China; Key Laboratory of Intelligent Geophysical Exploration for Oil and Gas Resources of Shaanxi Higher Education Institutions, Xi'an, Shaanxi, 710065, ChinaSchool of Earth Sciences and Engineering, Xi'an Shiyou University, Xi'an, Shaanxi, 710065, China; Key Laboratory of Intelligent Geophysical Exploration for Oil and Gas Resources of Shaanxi Higher Education Institutions, Xi'an, Shaanxi, 710065, ChinaSchool of Earth Sciences and Engineering, Xi'an Shiyou University, Xi'an, Shaanxi, 710065, China; Key Laboratory of Intelligent Geophysical Exploration for Oil and Gas Resources of Shaanxi Higher Education Institutions, Xi'an, Shaanxi, 710065, ChinaSchool of Earth Sciences and Engineering, Xi'an Shiyou University, Xi'an, Shaanxi, 710065, China; Key Laboratory of Intelligent Geophysical Exploration for Oil and Gas Resources of Shaanxi Higher Education Institutions, Xi'an, Shaanxi, 710065, ChinaMicroseismic monitoring is essential for understanding subsurface dynamics and optimizing oil and gas production. However, traditional methods for the automatic detection of microseismic events rely heavily on characteristic functions and human intervention, often resulting in suboptimal performance when dealing with complex and noisy data. In this study, we propose a novel approach that leverages deep learning frame to extract multiscale features from microseismic data using a TransUNet neural network. Our model integrates the advantages of Transformer and UNet architectures to achieve high accuracy in multivariate image segmentation and precise picking of P-wave and S-wave first arrivals simultaneously. We validate our approach using both synthetic and field microseismic datasets recorded from gas storage monitoring and roof fracturing in a coal seam. The robustness of the proposed method has been verified in the testing of synthetic data with various levels of Gaussian and real background noises extracted from field data. The comparisons of the proposed method with UNet and SwinUNet in terms of the model architecture and classification performance demonstrate the TransUNet achieves the optimal balance in its architecture and inference speed. With relatively low inference time and network complexity, it operates effectively in high-precision microseismic phase pickings. This advancement holds significant promise for enhancing microseismic monitoring technology in hydraulic fracturing and reservoir monitoring applications.http://www.sciencedirect.com/science/article/pii/S2666544125000255Deep learningMicroseismic event detectionTransUNetImage segmentationAttention mechanism |
| spellingShingle | Kun Chen Meng Li Xiaolian Li Guangzhi Cui Jia Tian JiaLe Li RuoYao Mu JunJie Zhu Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals Artificial Intelligence in Geosciences Deep learning Microseismic event detection TransUNet Image segmentation Attention mechanism |
| title | Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals |
| title_full | Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals |
| title_fullStr | Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals |
| title_full_unstemmed | Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals |
| title_short | Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals |
| title_sort | enhancing microseismic event detection with transunet a deep learning approach for simultaneous pickings of p wave and s wave first arrivals |
| topic | Deep learning Microseismic event detection TransUNet Image segmentation Attention mechanism |
| url | http://www.sciencedirect.com/science/article/pii/S2666544125000255 |
| work_keys_str_mv | AT kunchen enhancingmicroseismiceventdetectionwithtransunetadeeplearningapproachforsimultaneouspickingsofpwaveandswavefirstarrivals AT mengli enhancingmicroseismiceventdetectionwithtransunetadeeplearningapproachforsimultaneouspickingsofpwaveandswavefirstarrivals AT xiaolianli enhancingmicroseismiceventdetectionwithtransunetadeeplearningapproachforsimultaneouspickingsofpwaveandswavefirstarrivals AT guangzhicui enhancingmicroseismiceventdetectionwithtransunetadeeplearningapproachforsimultaneouspickingsofpwaveandswavefirstarrivals AT jiatian enhancingmicroseismiceventdetectionwithtransunetadeeplearningapproachforsimultaneouspickingsofpwaveandswavefirstarrivals AT jialeli enhancingmicroseismiceventdetectionwithtransunetadeeplearningapproachforsimultaneouspickingsofpwaveandswavefirstarrivals AT ruoyaomu enhancingmicroseismiceventdetectionwithtransunetadeeplearningapproachforsimultaneouspickingsofpwaveandswavefirstarrivals AT junjiezhu enhancingmicroseismiceventdetectionwithtransunetadeeplearningapproachforsimultaneouspickingsofpwaveandswavefirstarrivals |