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

arXiv: https://arxiv.org/abs/2410.08568 · HTML: https://mlsanity.com/paper/2410.08568/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 2 of 2)

- [Total-variation regularization strategies in full-waveform inversion](https://mlsanity.com/paper/1608.06159/) (2016)
- [GPRInvNet: Deep Learning-Based Ground Penetrating Radar Data Inversion for Tunnel Lining](https://mlsanity.com/paper/1912.05759/) (2019)

## Built on by (most cited first, 1 of 1)

- [Development of the Bioinspired Tendon-Driven DexHand 021 with Proprioceptive Compliance Control](https://mlsanity.com/paper/2511.03481/) (2025)

## Abstract (arXiv)

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.