Optimal fault detection from seismic data using intelligent techniques: A comprehensive review of methods
Seismic data plays a pivotal role in fault detection, offering critical insights into subsurface structures and seismic hazards. Understanding fault detection from seismic data is essential for mitigating seismic risks and guiding land-use plans. This paper presents a comprehensive review of existin...
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| Format: | Article |
| Language: | English |
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Groundwater Science and Engineering Limited
2025-05-01
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| Series: | Journal of Groundwater Science and Engineering |
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| Online Access: | https://www.sciopen.com/article/10.26599/JGSE.2025.9280049 |
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| author | Bhaktishree Nayak Pallavi Nayak |
| author_facet | Bhaktishree Nayak Pallavi Nayak |
| author_sort | Bhaktishree Nayak |
| collection | DOAJ |
| description | Seismic data plays a pivotal role in fault detection, offering critical insights into subsurface structures and seismic hazards. Understanding fault detection from seismic data is essential for mitigating seismic risks and guiding land-use plans. This paper presents a comprehensive review of existing methodologies for fault detection, focusing on the application of Machine Learning (ML) and Deep Learning (DL) techniques to enhance accuracy and efficiency. Various ML and DL approaches are analyzed with respect to fault segmentation, adaptive learning, and fault detection models. These techniques, benchmarked against established seismic datasets, reveal significant improvements over classical methods in terms of accuracy and computational efficiency. Additionally, this review highlights emerging trends, including hybrid model applications and the integration of real-time data processing for seismic fault detection. By providing a detailed comparative analysis of current methodologies, this review aims to guide future research and foster advancements in the effectiveness and reliability of seismic studies. Ultimately, the study seeks to bridge the gap between theoretical investigations and practical implementations in fault detection. |
| format | Article |
| id | doaj-art-5e78e7e053aa45f2af5b6c6df9eabbf0 |
| institution | DOAJ |
| issn | 2305-7068 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Groundwater Science and Engineering Limited |
| record_format | Article |
| series | Journal of Groundwater Science and Engineering |
| spelling | doaj-art-5e78e7e053aa45f2af5b6c6df9eabbf02025-08-20T03:05:42ZengGroundwater Science and Engineering LimitedJournal of Groundwater Science and Engineering2305-70682025-05-0113219320810.26599/JGSE.2025.9280049Optimal fault detection from seismic data using intelligent techniques: A comprehensive review of methodsBhaktishree Nayak0Pallavi Nayak1Government college of Engineering, Keonjhar At-Jamunalia Po-Old Towm Dist-Keonjhar Odisha 758001, IndiaRavenshaw University, Cuttack Department of Geology, Ravenshaw University Cuttack Pin-753003, IndiaSeismic data plays a pivotal role in fault detection, offering critical insights into subsurface structures and seismic hazards. Understanding fault detection from seismic data is essential for mitigating seismic risks and guiding land-use plans. This paper presents a comprehensive review of existing methodologies for fault detection, focusing on the application of Machine Learning (ML) and Deep Learning (DL) techniques to enhance accuracy and efficiency. Various ML and DL approaches are analyzed with respect to fault segmentation, adaptive learning, and fault detection models. These techniques, benchmarked against established seismic datasets, reveal significant improvements over classical methods in terms of accuracy and computational efficiency. Additionally, this review highlights emerging trends, including hybrid model applications and the integration of real-time data processing for seismic fault detection. By providing a detailed comparative analysis of current methodologies, this review aims to guide future research and foster advancements in the effectiveness and reliability of seismic studies. Ultimately, the study seeks to bridge the gap between theoretical investigations and practical implementations in fault detection.https://www.sciopen.com/article/10.26599/JGSE.2025.9280049seismic datafault detectionfault segmentationmachine learningdeep learningadaptive learning |
| spellingShingle | Bhaktishree Nayak Pallavi Nayak Optimal fault detection from seismic data using intelligent techniques: A comprehensive review of methods Journal of Groundwater Science and Engineering seismic data fault detection fault segmentation machine learning deep learning adaptive learning |
| title | Optimal fault detection from seismic data using intelligent techniques: A comprehensive review of methods |
| title_full | Optimal fault detection from seismic data using intelligent techniques: A comprehensive review of methods |
| title_fullStr | Optimal fault detection from seismic data using intelligent techniques: A comprehensive review of methods |
| title_full_unstemmed | Optimal fault detection from seismic data using intelligent techniques: A comprehensive review of methods |
| title_short | Optimal fault detection from seismic data using intelligent techniques: A comprehensive review of methods |
| title_sort | optimal fault detection from seismic data using intelligent techniques a comprehensive review of methods |
| topic | seismic data fault detection fault segmentation machine learning deep learning adaptive learning |
| url | https://www.sciopen.com/article/10.26599/JGSE.2025.9280049 |
| work_keys_str_mv | AT bhaktishreenayak optimalfaultdetectionfromseismicdatausingintelligenttechniquesacomprehensivereviewofmethods AT pallavinayak optimalfaultdetectionfromseismicdatausingintelligenttechniquesacomprehensivereviewofmethods |