Complete identification of complex salt geometries from inaccurate migrated subsurface offset gathers using deep learning

TL;DR

This paper presents a deep learning approach using a U-Net CNN to accurately identify complex salt geometries from migrated subsurface images generated with inaccurate velocity models, reducing reliance on manual interpretation.

Contribution

The study introduces a novel CNN-based method that predicts salt inclusion shapes from inaccurate migrated images, improving automation and accuracy in salt delineation tasks.

Findings

High accuracy in salt mask prediction on synthetic data

Effective generalization to unseen benchmark datasets

Robustness to inaccuracies in velocity models

Abstract

Delimiting salt inclusions from migrated images is a time-consuming activity that relies on highly human-curated analysis and is subject to interpretation errors or limitations of the methods available. We propose to use migrated images produced from an inaccurate velocity model (with a reasonable approximation of sediment velocity, but without salt inclusions) to predict the correct salt inclusions shape using a Convolutional Neural Network (CNN). Our approach relies on subsurface Common Image Gathers to focus the sediments' reflections around the zero offset and to spread the energy of salt reflections over large offsets. Using synthetic data, we trained a U-Net to use common-offset subsurface images as input channels for the CNN and the correct salt-masks as network output. The network learned to predict the salt inclusions masks with high accuracy; moreover, it also performed well when applied to synthetic benchmark data sets that were not previously introduced. Our training process tuned the U-Net to successfully learn the shape of complex salt bodies from partially focused subsurface offset images.