# Seismic Velocity Inversion from Multi-Source Shot Gathers Using Deep Segmentation Networks: Benchmarking U-Net Variants and SeismoLabV3+

arXiv: https://arxiv.org/abs/2509.21331 · HTML: https://mlsanity.com/paper/2509.21331/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 7 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [InversionNet: A Real-Time and Accurate Full Waveform Inversion with CNNs and continuous CRFs](https://mlsanity.com/paper/1811.07875/) (2018)
- [Deep-learning inversion: a next generation seismic velocity-model building method](https://mlsanity.com/paper/1902.06267/) (2019)
- [Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks](https://mlsanity.com/paper/1805.08826/) (2018)

## Abstract (arXiv)

Seismic velocity inversion is a key task in geophysical exploration, enabling the reconstruction of subsurface structures from seismic wave data. It is critical for high-resolution seismic imaging and interpretation. Traditional physics-driven methods, such as Full Waveform Inversion (FWI), are computationally demanding, sensitive to initialization, and limited by the bandwidth of seismic data. Recent advances in deep learning have led to data-driven approaches that treat velocity inversion as a dense prediction task. This research benchmarks three advanced encoder-decoder architectures -- U-Net, U-Net++, and DeepLabV3+ -- together with SeismoLabV3+, an optimized variant of DeepLabV3+ with a ResNeXt50 32x4d backbone and task-specific modifications -- for seismic velocity inversion using the ThinkOnward 2025 Speed & Structure dataset, which consists of five-channel seismic shot gathers paired with high-resolution velocity maps. Experimental results show that SeismoLabV3+ achieves the best performance, with MAPE values of 0.03025 on the internal validation split and 0.031246 on the hidden test set as scored via the official ThinkOnward leaderboard. These findings demonstrate the suitability of deep segmentation networks for seismic velocity inversion and underscore the value of tailored architectural refinements in advancing geophysical AI models.