MDA GAN: Adversarial-Learning-based 3-D Seismic Data Interpolation and Reconstruction for Complex Missing

TL;DR

This paper introduces MDA GAN, a novel 3D adversarial network designed to effectively interpolate and reconstruct complex missing seismic data, outperforming existing methods in challenging scenarios like high-ratio random and continuous missing cases.

Contribution

The paper proposes a new 3D GAN framework with multiple discriminators and a feature stitching module to better preserve data continuity and anisotropy in complex seismic missing data reconstruction.

Findings

Achieves up to 95% accuracy in random discrete missing data reconstruction.

Performs well in fault-rich and salt body surveys with complex missing data.

Outperforms existing methods in both simple and complex seismic data reconstruction tasks.

Abstract

The interpolation and reconstruction of missing traces is a crucial step in seismic data processing, moreover it is also a highly ill-posed problem, especially for complex cases such as high-ratio random discrete missing, continuous missing and missing in fault-rich or salt body surveys. These complex cases are rarely mentioned in current works. To cope with complex missing cases, we propose Multi-Dimensional Adversarial GAN (MDA GAN), a novel 3-D GAN framework. It keeps anisotropy and spatial continuity of the data after 3D complex missing reconstruction using three discriminators. The feature stitching module is designed and embedded in the generator to retain more information of the input data. The Tanh cross entropy (TCE) loss is derived, which provides the generator with the optimal reconstruction gradient to make the generated data smoother and continuous. We experimentally verified the effectiveness of the individual components of the study and then tested the method on multiple publicly available data. The method achieves reasonable reconstructions for up to 95% of random discrete missing and 100 traces of continuous missing. In fault and salt body enriched surveys, MDA GAN still yields promising results for complex cases. Experimentally it has been demonstrated that our method achieves better performance than other methods in both simple and complex cases.https://github.com/douyimin/MDA_GAN