Enhanced Seismic Imaging with Predictive Neural Networks for Geophysics

TL;DR

This paper introduces a neural network-based approach to improve seismic velocity model building and full waveform inversion in salt-prone environments, enhancing convergence speed and accuracy.

Contribution

The study develops a novel 3D convolutional neural network architecture with advanced data augmentation and ensemble methods for seismic velocity model enhancement.

Findings

Faster convergence of FWI with neural network-inferred initial models

Improved salt body and inclusion detection accuracy

Enhanced seismic imaging quality through probabilistic regularization

Abstract

We propose a predictive neural network architecture that can be utilized to update reference velocity models as inputs to the full waveform inversion. Deep learning models are explored to augment velocity model building workflows during processing the 3D seismic volume in salt-prone environments. Specifically, a neural network architecture, with 3D convolutional, de-convolutional layers, and 3D max-pooling, is designed to take standard amplitude 3D seismic volumes as an input. Enhanced data augmentations through generative adversarial networks and a weighted loss function enable the network to train with few sparsely annotated slices. Batch normalization is also applied for faster convergence. A 3D probability cube for salt bodies and inclusions is generated through ensembles of predictions from multiple models in order to reduce variance. Velocity models inferred from the proposed networks provide opportunities for FWI forward models to converge faster with an initial condition closer to the true model. In addition, in each iteration step, the probability cubes of salt bodies and inclusions inferred from the proposed networks can be used as a regularization term within the FWI forward modelling, which may result in an improved velocity model estimation while the output of seismic migration can be utilized as an input of the 3D neural network for subsequent iterations.