Denoising Laplace-domain Seismic Wavefields using Deep Learning

• 발행기관: 대한자원환경지질학회
• 저자명: Lydie Uwibambe 조준현(Jun Hyeon Jo) 하완수(Wansoo Ha)
• 간행물 정보: 『자원환경지질』57권 5호, 499~512쪽, 전체 14쪽
• 주제분류: 자연과학 > 지질학
• 파일형태: PDF
• 발행일자: 2024.10.31

국문 초록

Random noise in seismic data can significantly impair hydrocarbon exploration by degrading the quality of subsurface imaging. We propose a deep learning approach to attenuate random noise in Laplace-domain seismic wavefields. Our method employs a modified U-Net architecture, trained on diverse synthetic P-wave velocity models simulating the Gulf of Mexico subsurface. We rigorously evaluated the network’s denoising performance using both the synthetic Pluto velocity model and real Gulf of Mexico field data. We assessed the effectiveness of our approach through Laplace-domain full waveform inversion. The results consistently show that our UNet approach outperforms traditional singular value decomposition methods in noise attenuation across various scenarios. Numerical examples demonstrate that our method effectively attenuates random noise and significantly enhances the accuracy of subsequent seismic imaging processes.

목차

1. Introduction

2. Method

3. Results

4. Discussion

5. Conclusion

Acknowledgement

References

키워드

seismic data processing deep learning random noise attenuation Laplace domain full waveform inversion