# Deep-Learning Inversion of Seismic Data

arXiv: https://arxiv.org/abs/1901.07733 · HTML: https://mlsanity.com/paper/1901.07733/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

## Built on by (most cited first, 30 of 32)

- [A prior regularized full waveform inversion using generative diffusion models](https://mlsanity.com/paper/2306.12776/) (2023)
- [Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop](https://mlsanity.com/paper/2110.07584/) (2021)
- [Deep Learning in Deterministic Computational Mechanics](https://mlsanity.com/paper/2309.15421/) (2023)
- [InversionNet3D: Efficient and Scalable Learning for 3D Full Waveform Inversion](https://mlsanity.com/paper/2103.14158/) (2021)
- [Controllable seismic velocity synthesis using generative diffusion models](https://mlsanity.com/paper/2402.06277/) (2024)
- [Quantitative reconstruction of defects in multi-layered bonded composites using fully convolutional network-based ultrasonic inversion](https://mlsanity.com/paper/2109.07284/) (2021)
- [Physics-informed waveform inversion using pretrained wavefield neural operators](https://mlsanity.com/paper/2509.08967/) (2025)
- [Transfer Learning Enhanced Full Waveform Inversion](https://mlsanity.com/paper/2302.11259/) (2023)
- [Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis](https://mlsanity.com/paper/2408.12396/) (2024)
- [Inversion of Time-Lapse Surface Gravity Data for Detection of 3D CO2 Plumes via Deep Learning](https://mlsanity.com/paper/2209.02850/) (2022)
- [S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning](https://mlsanity.com/paper/2212.13523/) (2022)
- [A generative foundation model for an all-in-one seismic processing framework](https://mlsanity.com/paper/2502.01111/) (2025)
- [A Frequency-Velocity CNN for Developing Near-Surface 2D Vs Images from Linear-Array, Active-Source Wavefield Measurements](https://mlsanity.com/paper/2207.09580/) (2022)
- [Using explainability to design physics-aware CNNs for solving subsurface inverse problems](https://mlsanity.com/paper/2211.08651/) (2022)
- [Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea](https://mlsanity.com/paper/2103.02598/) (2021)
- [Automatic Velocity Picking Using a Multi-Information Fusion Deep Semantic Segmentation Network](https://mlsanity.com/paper/2205.03645/) (2022)
- [Joint inversion of Time-Lapse Surface Gravity and Seismic Data for Monitoring of 3D CO2 Plumes via Deep Learning](https://mlsanity.com/paper/2310.04430/) (2023)
- [Integrating Physics of the Problem into Data-Driven Methods to Enhance Elastic Full-Waveform Inversion with Uncertainty Quantification](https://mlsanity.com/paper/2406.05153/) (2024)
- [DispFormer: A Pretrained Transformer Incorporating Physical Constraints for Dispersion Curve Inversion](https://mlsanity.com/paper/2501.04366/) (2025)
- [On the Robustness and Generalization of Deep Learning Driven Full Waveform Inversion](https://mlsanity.com/paper/2111.14220/) (2021)
- [Complete identification of complex salt geometries from inaccurate migrated subsurface offset gathers using deep learning](https://mlsanity.com/paper/2204.09710/) (2022)
- [CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification](https://mlsanity.com/paper/2210.04776/) (2022)
- [SVInvNet: A Densely Connected Encoder-Decoder Architecture for Seismic Velocity Inversion](https://mlsanity.com/paper/2312.08194/) (2023)
- [Physics-guided Full Waveform Inversion using Encoder-Solver Convolutional Neural Networks](https://mlsanity.com/paper/2405.17696/) (2024)
- [Synergizing Deep Learning and Full-Waveform Inversion: Bridging Data-Driven and Theory-Guided Approaches for Enhanced Seismic Imaging](https://mlsanity.com/paper/2502.17585/) (2025)
- [Improving Full Waveform Inversion in Large Model Era](https://mlsanity.com/paper/2603.00377/) (2026)
- [High-Fidelity Compression of Seismic Velocity Models via SIREN Auto-Decoders](https://mlsanity.com/paper/2603.14284/) (2026)
- [SIGMA: A Physics-Based Benchmark for Gas Chimney Understanding in Seismic Images](https://mlsanity.com/paper/2603.23439/) (2026)
- [Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture](https://mlsanity.com/paper/2606.01110/) (2026)
- [ROMNet: a hybrid reduced order modeling and machine learning approach to waveform inversion](https://mlsanity.com/paper/2608.25160/) (2026)

## Abstract (arXiv)

We propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration, i.e., reconstructing the velocity model directly from seismic data by deep neural networks (DNNs). The conventional way of addressing this ill-posed inversion problem is through iterative algorithms, which suffer from poor nonlinear mapping and strong nonuniqueness. Other attempts may either import human intervention errors or underuse seismic data. The challenge for DNNs mainly lies in the weak spatial correspondence, the uncertain reflection-reception relationship between seismic data and velocity model, as well as the time-varying property of seismic data. To tackle these challenges, we propose end-to-end seismic inversion networks (SeisInvNets) with novel components to make the best use of all seismic data. Specifically, we start with every seismic trace and enhance it with its neighborhood information, its observation setup, and the global context of its corresponding seismic profile. From the enhanced seismic traces, the spatially aligned feature maps can be learned and further concatenated to reconstruct a velocity model. In general, we let every seismic trace contribute to the reconstruction of the whole velocity model by finding spatial correspondence. The proposed SeisInvNet consistently produces improvements over the baselines and achieves promising performance on our synthesized and proposed SeisInv data set according to various evaluation metrics. The inversion results are more consistent with the target from the aspects of velocity values, subsurface structures, and geological interfaces. Moreover, the mechanism and the generalization of the proposed method are discussed and verified. Nevertheless, the generalization of deep-learning-based inversion methods on real data is still challenging and considering physics may be one potential solution.