Subsurface Depths Structure Maps Reconstruction with Generative Adversarial Networks

TL;DR

This paper presents a novel generative adversarial network-based method for reconstructing detailed 3D seismic depth maps from 2D seismic data, leveraging transfer learning and super-resolution techniques.

Contribution

It introduces a new approach combining StyleGAN2-ADA and Pixel2Style2Pixel algorithms for seismic depth map reconstruction, enabling high-quality 3D map generation from limited data.

Findings

Achieved credible detailed depth reconstructions comparable to 3D seismic maps.

Demonstrated transfer of structural knowledge from well-studied to underexplored areas.

Proposed probabilistic depth space for geological form representation.

Abstract

This paper described a method for reconstruction of detailed-resolution depth structure maps, usually obtained after the 3D seismic surveys, using the data from 2D seismic depth maps. The method uses two algorithms based on the generative-adversarial neural network architecture. The first algorithm StyleGAN2-ADA accumulates in the hidden space of the neural network the semantic images of mountainous terrain forms first, and then with help of transfer learning, in the ideal case - the structure geometry of stratigraphic horizons. The second algorithm, the Pixel2Style2Pixel encoder, using the semantic level of generalization of the first algorithm, learns to reconstruct the original high-resolution images from their degraded copies (super-resolution technology). There was demonstrated a methodological approach to transferring knowledge on the structural forms of stratigraphic horizon boundaries from the well-studied areas to the underexplored ones. Using the multimodal synthesis of Pixel2Style2Pixel encoder, it is proposed to create a probabilistic depth space, where each point of the project area is represented by the density of probabilistic depth distribution of equally probable reconstructed geological forms of structural images. Assessment of the reconstruction quality was carried out for two blocks. Using this method, credible detailed depth reconstructions comparable with the quality of 3D seismic maps have been obtained from 2D seismic maps.