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Title: Generative Inpainting Network Applications on Seismic Image Compression

Paper content:
The use of deep learning models as priors for compressive sensing tasks presents new potential for inexpensive seismic data acquisition.
An appropriately designed Wasserstein generative adversarial network is designed based on a generative adversarial network architecture trained on several historical surveys, capable of learning the statistical properties of the seismic wavelets.
The usage of validating and performance testing of compressive sensing are three steps.
First, the existence of a sparse representation with different compression rates for seismic surveys is studied.
Then, non-uniform samplings are studied, using the proposed methodology.
Finally, recommendations for non-uniform seismic survey grid, based on the evaluation of reconstructed seismic images and metrics, is proposed.
The primary goal of the proposed deep learning model is to provide the foundations of an optimal design for seismic acquisition, with less loss in imaging quality.
Along these lines, a compressive sensing design of a non-uniform grid over an asset in Gulf of Mexico, versus a traditional seismic survey grid which collects data uniformly at every few feet, is suggested, leveraging the proposed method.
Conventional computational recovery is suffered from undesired artifacts such as over-smoothing, image size limitations and high computational cost.
The use of deep generative network (GAN) models offers a very promising alternative approach for inexpensive seismic data acquisition, which improved quality and revealing finer details when compared to conventional approaches or pixel-wise deep learning models.
As one of the pioneers to apply a pixel inpainting GAN on large, real seismic compressed image recovery, we contributes the following points:
1) Introduction of a GAN based inpainting model for compressed image recovery, under uniform or nonuniform sampling, capable to recover the heavily sampled data efficiently and reliably.
2) Superior model for compressive sensing on uniform sampling, that performs better than the originial network and the state-of-the-art interpolation method for uniform sampling.
3) Introduction of an effective, non-uniform, sampling survey recommendation, leveraging the GIN uniform sampling reconstructions and a hierarchical selection scheme.
(2) (1) We designed and implemented a modification of the GIN model, the GIN-CS, and successfully tested its performance on uniform samplings with compression rates ×2, ×4, ×8, ×16.
GIN-CS demonstrates superior reconstruction performance relatively to both the original GIN and the conventional biharmonic method.
More precisely, we show that seismic imaging can be successfully recovered by filling the missing traces, revealing finer details, even in high compression rate cases.
In addition, the proposed method runs approximately 300 times faster than the conventional method.
Finally, a strategy for constructing a recommendation of non-uniform survey is proposed for a field dataset from Gulf of Mexico, based on our results from a combination of limited amount of uniform sampling experiments.

assistant: text: Improved a GAN based pixel inpainting network for compressed seismic image recovery andproposed a non-uniform sampling survey recommendatio, which can be easily applied to medical and other domains for compressive sensing technique.