Seismic Data Enhancement and Representation by Self-Supervised Learning



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Seismic acquisition is a costly and labor-intensive process. As such, advanced technologies have emerged in recent years to strike a balance between data quality and acquisition cost, including improved sensor technology, novel seismic acquisition strategies, and advanced data processing and enhancement algorithms. While some methods are designed to directly enhance data quality or acquisition efficiency, others prioritize one aspect over the other initially, followed by post-processing remediation to achieve the optimal balance between the two.

This dissertation primarily focuses on three critical aspects to address the efficiency-cost trade-off, namely seismic data deblending for simultaneous source acquisition, seismic data interpolation, and seismic data compression. Simultaneous source acquisition, or blended acquisition has been introduced to reduce acquisition costs and improve efficiency by firing denser sources within limited time frames. However, an effective deblending algorithm is necessary to separate the blended energy and eliminate the blending noises. Seismic data interpolation is another area of focus that aims to improve data resolution and restore missing or contaminated seismic traces. As the volume of seismic data continues to grow, data compression methods can alleviate storage, transmission, and computation costs.

Recent advances in deep learning and its data-driven approaches toward feature engineering have yielded many encouraging results for various seismic data processing tasks, including seismic deblending, interpolation, and compression. In this dissertation, we propose new approaches for these tasks that leverage cutting-edge deep learning techniques. Compared to the prevalent supervised learning scheme for seismic data processing, our work targets exploring self-supervised learning schemes to solve the problems. We present innovative networks and algorithms that are specifically tailored for seismic data processing, incorporating unique features and physics information of seismic data. To evaluate the effectiveness of our proposed methods, we conduct experiments using both synthetic and field datasets.



Seismic Data Processing, Machine learning, Self-supervised learning, Signal processing, Seismic Deblending, Seismic Data Interpolation, Seismic Data Compression


Portions of this document appear in: Wang, S., Hu, W., Hu, Y., Wu, X. and Chen, J., 2020. A physics-augmented deep learning method for seismic data deblending. In SEG Technical Program Expanded Abstracts 2020 (pp. 3877-3881). Society of Exploration Geophysicists; and in: Wang, S., Hu, W., Yuan, P., Wu, X., Zhang, Q., Nadukandi, P., Botero, G.O. and Chen, J., 2022. A Self-Supervised Deep Learning Method for Seismic Data Deblending Using a Blind-Trace Network. IEEE Transactions on Neural Networks and Learning Systems; and in: Wang, S., Hu, W., Yuan, P., Wu, X., Zhang, Q., Nadukandi, P., Ocampo Botero, G. and Chen, J., 2021, October. Seismic deblending by self-supervised deep learning with a blind-trace network. In SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy. OnePetro; and in: Yuan, P., Wang, S., Hu, W., Nadukandi, P., Botero, G.O., Wu, X., Van Nguyen, H. and Chen, J., 2022. Self-Supervised Learning for Efficient Antialiasing Seismic Data Interpolation. IEEE Transactions on Geoscience and Remote Sensing, 60, pp.1-19.