# Transfer Learning Enhanced Full Waveform Inversion

arXiv: https://arxiv.org/abs/2302.11259 · HTML: https://mlsanity.com/paper/2302.11259/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 22 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Visualizing the Loss Landscape of Neural Nets](https://mlsanity.com/paper/1712.09913/) (2017)
- [On weight initialization in deep neural networks](https://mlsanity.com/paper/1704.08863/) (2017)
- [Deep-learning inversion: a next generation seismic velocity-model building method](https://mlsanity.com/paper/1902.06267/) (2019)
- [Deep-Learning Inversion of Seismic Data](https://mlsanity.com/paper/1901.07733/) (2019)
- [Deep Recurrent Architectures for Seismic Tomography](https://mlsanity.com/paper/1908.07824/) (2019)
- [Quantitative reconstruction of defects in multi-layered bonded composites using fully convolutional network-based ultrasonic inversion](https://mlsanity.com/paper/2109.07284/) (2021)
- [On the Use of Neural Networks for Full Waveform Inversion](https://mlsanity.com/paper/2303.03260/) (2023)
- [Petrophysical Property Estimation from Seismic Data Using Recurrent Neural Networks](https://mlsanity.com/paper/1901.08623/) (2019)
- [Immersed boundary parametrizations for full waveform inversion](https://mlsanity.com/paper/2209.07826/) (2022)

## Built on by (most cited first, 4 of 4)

- [Deep Learning in Deterministic Computational Mechanics](https://mlsanity.com/paper/2309.15421/) (2023)
- [Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems using Transfer Learning](https://mlsanity.com/paper/2401.02810/) (2024)
- [Neural Networks for Generating Better Local Optima in Topology Optimization](https://mlsanity.com/paper/2407.17957/) (2024)
- [Accelerating Full Waveform Inversion By Transfer Learning](https://mlsanity.com/paper/2408.00695/) (2024)

## Abstract (arXiv)

We propose a way to favorably employ neural networks in the field of non-destructive testing using Full Waveform Inversion (FWI). The presented methodology discretizes the unknown material distribution in the domain with a neural network within an adjoint optimization. To further increase efficiency of the FWI, pretrained neural networks are used to provide a good starting point for the inversion. This reduces the number of iterations in the Full Waveform Inversion for specific, yet generalizable settings.