Accelerated Full Waveform Inversion by Deep Compressed Learning

TL;DR

This paper introduces a deep learning-based method to reduce seismic data dimensionality for Full Waveform Inversion, significantly decreasing computational costs while maintaining accuracy, thus enabling faster large-scale 3D seismic imaging.

Contribution

It presents a novel hierarchical data selection approach combining compressed learning and clustering to efficiently identify relevant seismic data for FWI.

Findings

Outperforms random data sampling in FWI accuracy

Achieves effective inversion using only 10% of data in 2D cases

Paves the way for accelerated large-scale 3D seismic inversion

Abstract

We propose and test a method to reduce the dimensionality of Full Waveform Inversion (FWI) inputs as computational cost mitigation approach. Given modern seismic acquisition systems, the data (as input for FWI) required for an industrial-strength case is in the teraflop level of storage, therefore solving complex subsurface cases or exploring multiple scenarios with FWI become prohibitive. The proposed method utilizes a deep neural network with a binarized sensing layer that learns by compressed learning a succinct but consequential seismic acquisition layout from a large corpus of subsurface models. Thus, given a large seismic data set to invert, the trained network selects a smaller subset of the data, then by using representation learning, an autoencoder computes latent representations of the data, followed by K-means clustering of the latent representations to further select the most relevant data for FWI. Effectively, this approach can be seen as a hierarchical selection. The proposed approach consistently outperforms random data sampling, even when utilizing only 10% of the data for 2D FWI, these results pave the way to accelerating FWI in large scale 3D inversion.