Optimal fault detection from seismic data using intelligent techniques: A comprehensive review of methods

Bhaktishree Nayak; Pallavi Nayak

doi:10.26599/JGSE.2025.9280049

Volume 13 Issue 2

Jun. 2025

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Article Navigation > Journal of Groundwater Science and Engineering > 2025 > 13(2): 193-208

Nayak B, Nayak P. 2025. Optimal fault detection from seismic data using intelligent techniques: A comprehensive review of methods. Journal of Groundwater Science and Engineering, 13(2): 193-208 doi: 10.26599/JGSE.2025.9280049

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Optimal fault detection from seismic data using intelligent techniques: A comprehensive review of methods

doi: 10.26599/JGSE.2025.9280049

Bhaktishree Nayak^{1
,
,},
Pallavi Nayak²

1.
Government college of Engineering, Keonjhar At-Jamunalia Po-Old Towm Dist-Keonjhar Odisha 758001, India
2.
Ravenshaw University, Cuttack Department of Geology, Ravenshaw University Cuttack Pin-753003, India

More Information

Corresponding author: scholar.bhaktishreenayak@gmail.com
Received Date: 2024-08-19
Accepted Date: 2025-03-21

Available Online: 2025-05-10

Publish Date: 2025-06-30

Abstract

Abstract

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.
- Seismic data,
- Fault detection,
- Fault Segmentation,
- Machine learning,
- Deep learning,
- Adaptive learning

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References(72)

References

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