Seismic Inversion by Multi-dimensional Newtonian Machine

TL;DR

This paper introduces multi-dimensional Newtonian machine learning (MNML) for seismic inversion, enabling higher resolution subsurface models by capturing more seismic data features with reduced storage needs.

Contribution

The paper proposes a novel MNML method that inverts multi-dimensional latent space features, improving resolution and data representation over traditional single-dimensional approaches.

Findings

Multi-dimensional LS features preserve more seismic data information.

MNML achieves higher resolution velocity models comparable to FWI.

Requires less storage space than conventional FWI.

Abstract

Newtonian machine learning (NML) is a wave-equation inversion method that inverts single-dimensional latent space (LS) features of the seismic data for retrieving the subsurface background velocity model. The single-dimensional LS features mainly contain the kinematic information of the seismic data, which are automatically extracted from the seismic signal by using an autoencoder network. Because its LS feature dimension is too small to preserve the dynamic information, such as the waveform variations, of the seismic data. Therefore the NML inversion is not able to recover the high-wavenumber velocity details. To mitigate this problem, we propose to invert multi-dimensional LS features, which can fully represent the entire characters of the seismic data. We denote this method as multi-dimensional Newtonian machine learning (MNML). In MNML, we define a new multi-variable connective function that works together with the multi-variable implicit function theorem to connect the velocity perturbations to the multi-dimensional LS feature perturbations. Numerical tests show that (1) the multi-dimensional LS features can preserve more data information than the single-dimensional LS features; (2) a higher resolution velocity model can be recovered by inverting the multi-dimensional LS features, and the inversion quality is comparable to that of FWI; (3) the MNML method requires a much smaller storage space than conventional FWI because only the low-dimensional representations of the high-dimensional seismic data are needed to be stored. The disadvantage of MNML is that it can more easily get stuck in local minima compared to the NML method. So we suggest a multiscale inversion approach that inverts for higher dimensional LS features as the iteration count increase.