Seismic Data Reconstruction with Generative Adversarial Networks

Abstract

A main challenge in seismic imaging is acquiring densely sampled data. Compressed Sensing has provided theoretical foundations upon which desired sampling rate can be achieved by applying a sparsity promoting algorithm on sub-sampled data. The key point in successful recovery is to deploy a randomized sampling scheme. In this paper, we propose a novel deep learning-based method for fast and accurate reconstruction of heavily under-sampled seismic data, regardless of type of sampling. A neural network learns to do reconstruction directly from data via an adversarial process. Once trained, the reconstruction can be done by just feeding the frequency slice with missing data into the neural network. This adaptive nonlinear model makes the algorithm extremely flexible, applicable to data with arbitrarily type of sampling. With the assumption that we have access to training data, the quality of reconstructed slice is superior even for extremely low sampling rate (as low as 10%) due to the data-driven nature of the method.

Seismic data interpolation using deep learning with generative adversarial networks

“…Richardson (2018) applied GANs for model order reduction in seismic full-waveform inversion. Siahkoohi et al (2018) and Kaur et al (2019b) used GANs for seismic data reconstruction. Picetti et al (2018), Herrmann et al (2019), Oliveira et al (2019) and Kaur et al (2019aKaur et al ( , 2020 adopted GANs for seismic imaging applications.…”

Surface-related multiple elimination with deep learning

“…Machine learning is rapidly attracting interest in the exploration seismology research community. In the past few years, there has been numerous attempts to deploy deep learning algorithms to address problems in active research areas in the field of seismic, including but not limited to pre-stack seismic data processing (Mikhailiuk and Faul, 2018;Siahkoohi et al, 2018a;Ovcharenko et al, 2018;Sun and Demanet, 2018;Siahkoohi et al, 2018b), modeling and imaging (Moseley et al, 2018;Siahkoohi et al, 2019;Rizzuti et al, 2019), and inversion (Lewis and Vigh, 2017;Araya-Polo et al, 2018;Richardson, 2018;Das et al, 2018;Kothari et al, 2019).…”

Deep-learning based ocean bottom seismic wavefield recovery

“…Recent successes in the application of machine learning algorithms have been reported in the areas of seismic interpretation (Huang et al, 2017;Di et al, 2018;Veillard et al, 2018), seismic data processing (Siahkoohi et al, 2018b;Sun and Demanet, 2018;Ovcharenko et al, 2018), seismic modeling and imaging Rizzuti et al, 2019;Moseley et al, 2018), and inversion (Mosser et al, 2018;Kothari et al, 2019). Several authors have addressed the seismic wavefield recovery (Siahkoohi et al, 2018a;Mandelli et al, 2018) as well. Mandelli et al (2018) trained a neural network to jointly denoise and interpolate 2D shot records by operating on extracted patches from shot records.…”

“…Mandelli et al (2018) trained a neural network to jointly denoise and interpolate 2D shot records by operating on extracted patches from shot records. Siahkoohi et al (2018a) recovered seismic wavefields by reconstructing subsampled frequency slices, by assuming that 5% of shot records are fully-sampled. We extend ideas in Siahkoohi et al (2018a) by relaxing the assumption of having few fully-sampled shot records by exploiting reciprocity.…”

“…Siahkoohi et al (2018a) recovered seismic wavefields by reconstructing subsampled frequency slices, by assuming that 5% of shot records are fully-sampled. We extend ideas in Siahkoohi et al (2018a) by relaxing the assumption of having few fully-sampled shot records by exploiting reciprocity.…”