Accelerating Full Waveform Inversion By Transfer Learning

TL;DR

This paper introduces a transfer learning approach to neural network-based full waveform inversion, significantly improving convergence speed and reconstruction quality by better initializations derived from pretraining on simulated data.

Contribution

The novel transfer learning method enhances neural network initialization for FWI, leading to faster and more accurate material field reconstructions compared to existing methods.

Findings

Transfer learning accelerates FWI convergence.

Pretrained networks produce more physically meaningful solutions.

The method outperforms conventional and non-pretrained NN-based FWI.

Abstract

Full waveform inversion (FWI) is a powerful tool for reconstructing material fields based on sparsely measured data obtained by wave propagation. For specific problems, discretizing the material field with a neural network (NN) improves the robustness and reconstruction quality of the corresponding optimization problem. We call this method NN-based FWI. Starting from an initial guess, the weights of the NN are iteratively updated to fit the simulated wave signals to the sparsely measured data set. For gradient-based optimization, a suitable choice of the initial guess, i.e., a suitable NN weight initialization, is crucial for fast and robust convergence. In this paper, we introduce a novel transfer learning approach to further improve NN-based FWI. This approach leverages supervised pretraining to provide a better NN weight initialization, leading to faster convergence of the subsequent optimization problem. Moreover, the inversions yield physically more meaningful local minima. The network is pretrained to predict the unknown material field using the gradient information from the first iteration of conventional FWI. In our computational experiments on two-dimensional domains, the training data set consists of reference simulations with arbitrarily positioned elliptical voids of different shapes and orientations. We compare the performance of the proposed transfer learning NN-based FWI with three other methods: conventional FWI, NN-based FWI without pretraining and conventional FWI with an initial guess predicted from the pretrained NN. Our results show that transfer learning NN-based FWI outperforms the other methods in terms of convergence speed and reconstruction quality.