GPR Full-Waveform Inversion through Adaptive Filtering of Model Parameters and Gradients Using CNN

TL;DR

This paper introduces a CNN-based adaptive filtering framework for GPR full-waveform inversion that improves model accuracy by reducing gradient errors and enhancing anomaly detection.

Contribution

The novel integration of CNNs into the FWI process for adaptive filtering of model parameters and gradients, maintaining differentiability and improving inversion quality.

Findings

Filtering during forward modeling improves model accuracy.

Gradient filtering reduces errors like ghost values.

Enhanced detection of subsurface anomalies.

Abstract

GPR full-waveform inversion optimizes the subsurface property model iteratively to match the entire waveform information. However, the model gradients derived from wavefield continuation often contain errors, such as ghost values and excessively large values at transmitter and receiver points. Furthermore, models updated based on these gradients frequently exhibit unclear characterization of anomalous bodies or false anomalies, making it challenging to obtain accurate inversion results. To address these issues, we introduced a novel full-waveform inversion (FWI) framework that incorporates an embedded convolutional neural network (CNN) to adaptively filter model parameters and gradients. Specifically, we embedded the CNN module before the forward modeling process and ensured the entire FWI process remains differentiable. This design leverages the auto-grad tool of the deep learning library, allowing model values to pass through the CNN module during forward computation and model gradients to pass through the CNN module during backpropagation. Experiments have shown that filtering the model parameters during forward computation and the model gradients during backpropagation can ultimately yield high-quality inversion results.