Microseismic moment tensor inversion based on ResNet model
This paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events. Taking the great advantages of deep networks in classification and regression tasks, it can realize the great potential of fast and accurate inversion of microseismic moment tensors after th...
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/S2666544125000036 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849430803574947840 |
|---|---|
| author | Jiaqi Yan Li Ma Tianqi Jiang Jing Zheng Dewei Li Xingzhi Teng |
| author_facet | Jiaqi Yan Li Ma Tianqi Jiang Jing Zheng Dewei Li Xingzhi Teng |
| author_sort | Jiaqi Yan |
| collection | DOAJ |
| description | This paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events. Taking the great advantages of deep networks in classification and regression tasks, it can realize the great potential of fast and accurate inversion of microseismic moment tensors after the network trained. This ResNet-based moment tensor prediction technology, whose input is raw recordings, does not require the extraction of data features in advance. First, we tested the network using synthetic data and performed a quantitative assessment of the errors. The results demonstrate that the network exhibits high accuracy and efficiency during the prediction phase. Next, we tested the network using real microseismic data and compared the results with those from traditional inversion methods. The error in the results was relatively small compared to traditional methods. However, the network operates more efficiently without requiring manual intervention, making it highly valuable for near-real-time monitoring applications. |
| format | Article |
| id | doaj-art-54ec7979735a4989bc8afde4a6e5b9be |
| 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-54ec7979735a4989bc8afde4a6e5b9be2025-08-20T03:27:51ZengKeAi Communications Co. Ltd.Artificial Intelligence in Geosciences2666-54412025-06-016110010710.1016/j.aiig.2025.100107Microseismic moment tensor inversion based on ResNet modelJiaqi Yan0Li Ma1Tianqi Jiang2Jing Zheng3Dewei Li4Xingzhi Teng5Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi’an, Shaanxi, 710021, China; College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, 100083, ChinaKey Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi’an, Shaanxi, 710021, ChinaState Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology-Beijing, Beijing, 100083, China; China Coal Technology and Engineering Group Shenyang Research Institute, Fushun Liaoning, Liaoning, 113122, China; Corresponding author at: China Coal Technology and Engineering Group Shenyang Research Institute, Fushun Liaoning, Liaoning, 113122, China.College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, 100083, China; State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology-Beijing, Beijing, 100083, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, ChinaCollege of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, 100083, ChinaThis paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events. Taking the great advantages of deep networks in classification and regression tasks, it can realize the great potential of fast and accurate inversion of microseismic moment tensors after the network trained. This ResNet-based moment tensor prediction technology, whose input is raw recordings, does not require the extraction of data features in advance. First, we tested the network using synthetic data and performed a quantitative assessment of the errors. The results demonstrate that the network exhibits high accuracy and efficiency during the prediction phase. Next, we tested the network using real microseismic data and compared the results with those from traditional inversion methods. The error in the results was relatively small compared to traditional methods. However, the network operates more efficiently without requiring manual intervention, making it highly valuable for near-real-time monitoring applications.http://www.sciencedirect.com/science/article/pii/S2666544125000036MicroseismicResNetMoment tensorRegression |
| spellingShingle | Jiaqi Yan Li Ma Tianqi Jiang Jing Zheng Dewei Li Xingzhi Teng Microseismic moment tensor inversion based on ResNet model Artificial Intelligence in Geosciences Microseismic ResNet Moment tensor Regression |
| title | Microseismic moment tensor inversion based on ResNet model |
| title_full | Microseismic moment tensor inversion based on ResNet model |
| title_fullStr | Microseismic moment tensor inversion based on ResNet model |
| title_full_unstemmed | Microseismic moment tensor inversion based on ResNet model |
| title_short | Microseismic moment tensor inversion based on ResNet model |
| title_sort | microseismic moment tensor inversion based on resnet model |
| topic | Microseismic ResNet Moment tensor Regression |
| url | http://www.sciencedirect.com/science/article/pii/S2666544125000036 |
| work_keys_str_mv | AT jiaqiyan microseismicmomenttensorinversionbasedonresnetmodel AT lima microseismicmomenttensorinversionbasedonresnetmodel AT tianqijiang microseismicmomenttensorinversionbasedonresnetmodel AT jingzheng microseismicmomenttensorinversionbasedonresnetmodel AT deweili microseismicmomenttensorinversionbasedonresnetmodel AT xingzhiteng microseismicmomenttensorinversionbasedonresnetmodel |