First-Arrival Picking for Microseismic Monitoring Based on Deep Learning
In microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location. In the era of big data, the traditional first-arrival picking method cannot meet the real-time processing requir...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Geophysics |
| Online Access: | http://dx.doi.org/10.1155/2021/5548346 |
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| _version_ | 1849693386967089152 |
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| author | Xiaolong Guo |
| author_facet | Xiaolong Guo |
| author_sort | Xiaolong Guo |
| collection | DOAJ |
| description | In microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location. In the era of big data, the traditional first-arrival picking method cannot meet the real-time processing requirements of microseismic monitoring process. Using the advanced idea of deep learning-based end-to-end classification and the prominent feature extraction advantages of a fully convolution neural network, this paper proposes a first-arrival picking method of effective signals for microseismic monitoring based on UNet++ network, which can significantly improve the accuracy and efficiency of first-arrival picking. In this paper, we first introduced the methodology of the UNet++-based picking method. And then, the performance of the proposed method is verified by the experiments with finite-difference forward modeling simulated signals and actual microseismic records under different signal-to-noise ratios, and finally, comparative experiments are performed using the U-Net-based first-arrival picking algorithm and the Short-Term Average to Long-Term Average (STA/LTA) algorithm. The results show that compared to the U-Net network, the proposed method can obviously improve the first-arrival picking accuracy of the low signal-to-noise ratio microseismic signals, achieving significantly higher accuracy and efficiency than the STA/LTA algorithm, which is famous for its high efficiency in traditional algorithms. |
| format | Article |
| id | doaj-art-2db9be4ecc7e4761ab956110ce9197ab |
| institution | DOAJ |
| issn | 1687-885X 1687-8868 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Geophysics |
| spelling | doaj-art-2db9be4ecc7e4761ab956110ce9197ab2025-08-20T03:20:26ZengWileyInternational Journal of Geophysics1687-885X1687-88682021-01-01202110.1155/2021/55483465548346First-Arrival Picking for Microseismic Monitoring Based on Deep LearningXiaolong Guo0School of Electronic and Information, Yangtze University, Jingzhou 434023, ChinaIn microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location. In the era of big data, the traditional first-arrival picking method cannot meet the real-time processing requirements of microseismic monitoring process. Using the advanced idea of deep learning-based end-to-end classification and the prominent feature extraction advantages of a fully convolution neural network, this paper proposes a first-arrival picking method of effective signals for microseismic monitoring based on UNet++ network, which can significantly improve the accuracy and efficiency of first-arrival picking. In this paper, we first introduced the methodology of the UNet++-based picking method. And then, the performance of the proposed method is verified by the experiments with finite-difference forward modeling simulated signals and actual microseismic records under different signal-to-noise ratios, and finally, comparative experiments are performed using the U-Net-based first-arrival picking algorithm and the Short-Term Average to Long-Term Average (STA/LTA) algorithm. The results show that compared to the U-Net network, the proposed method can obviously improve the first-arrival picking accuracy of the low signal-to-noise ratio microseismic signals, achieving significantly higher accuracy and efficiency than the STA/LTA algorithm, which is famous for its high efficiency in traditional algorithms.http://dx.doi.org/10.1155/2021/5548346 |
| spellingShingle | Xiaolong Guo First-Arrival Picking for Microseismic Monitoring Based on Deep Learning International Journal of Geophysics |
| title | First-Arrival Picking for Microseismic Monitoring Based on Deep Learning |
| title_full | First-Arrival Picking for Microseismic Monitoring Based on Deep Learning |
| title_fullStr | First-Arrival Picking for Microseismic Monitoring Based on Deep Learning |
| title_full_unstemmed | First-Arrival Picking for Microseismic Monitoring Based on Deep Learning |
| title_short | First-Arrival Picking for Microseismic Monitoring Based on Deep Learning |
| title_sort | first arrival picking for microseismic monitoring based on deep learning |
| url | http://dx.doi.org/10.1155/2021/5548346 |
| work_keys_str_mv | AT xiaolongguo firstarrivalpickingformicroseismicmonitoringbasedondeeplearning |