Seismic Inversion by Newtonian Machine Learning

TL;DR

This paper introduces a novel seismic inversion method that combines physics-based modeling with machine learning to improve subsurface velocity models by using autoencoder-derived skeletal data, reducing cycle-skipping issues.

Contribution

It provides a general framework for inverting skeletal data generated by neural networks using PDE solutions, applicable across various geophysical fields and data types.

Findings

Cycle-skipping is largely mitigated compared to conventional FWI.

No manual feature picking is required due to automatic skeletal data selection.

The method is versatile for different neural network features and physical governing equations.

Abstract

We present a wave-equation inversion method that inverts skeletonized data for the subsurface velocity model. The skeletonized representation of the seismic traces consists of the low-rank latent-space variables predicted by a well-trained autoencoder neural network. The input to the autoencoder is the recorded common shot gathers, and the implicit function theorem is used to determine the perturbation of the skeletonized data with respect to the velocity perturbation. The final velocity model is the one that best predicts the observed latent-space parameters. Empirical results suggest that the cycle-skipping problem is largely mitigated compared to the conventional full waveform inversion (FWI) method by replacing the waveform differences by those of the latent-space parameters. The advantage of this method over other skeletonized data methods is that no manual picking of important features is required because the skeletal data are automatically selected by the autoencoder. The most significant contribution of this paper is that it provides a general framework for using solutions to the governing PDE to invert skeletal data generated by any type of a neural network. The governing equation can be that for gravity, seismic waves, electromagnetic fields, and magnetic fields. The input data can be the records from different types of data and their skeletal features, as long as the model parameters are sensitive to their perturbations. The skeletal data can be the latent space variables of an autoencoder, a variational autoencoder, or a feature map from a convolutional neural network (CNN), or principal component analysis (PCA) features. In other words, we have combined the best features of Newtonian physics and the pattern matching capabilities of machine learning to invert seismic data by Newtonian machine learning.