Seismic Imaging and Inversion Techniques

Derniers papiers

• Physical-State-Guided Diffusion Sampling for Full-Waveform Inversion

  Chen Min, Haowen Jiang, Zheng Ma, Xiongbin Yan · 14 septembre 2026

  Full waveform inversion (FWI) estimates subsurface velocity from seismic recordings, but its ill-posedness and nonlinearity make accurate reconstruction strongly dependent on initialization and prior information. Diffusion posterior sampling provides a learned geological prior, yet directly coupling…

• Data-driven rational function neural networks: a new method for generating analytical models of rock physics

  Weitao Sun · 9 septembre 2026

  Seismic wave velocity of underground rock plays important role in detecting internal structure of the Earth. Rock physics models have long been the focus of predicting wave velocity. However, construction of a theoretical model requires careful physical considerations and mathematical derivations, w…

• HarmoCore: Functional Latent Diffusion for Sparse Reconstruction of Oscillatory Wave Fields

  Lihao Chen, Xinyu Zhang, Panqi Chen, Lei Cheng, Ting Zhang, Jianlong Li, Shikai Fang · 2 septembre 2026

  Reconstructing oscillatory wave fields from scattered sensors is a severely underdetermined inverse problem. Beyond the challenges of general physical-field reconstruction, wave responses are complex-valued, frequency-sensitive, and highly oscillatory, while costly simulation and sensing often leave…

• ROMNet: a hybrid reduced order modeling and machine learning approach to waveform inversion

  Liliana Borcea, Alexander Mamonov, Kui Ren, Haizhao Yang, Chugang Yi · 27 août 2026

  Waveform inversion seeks to estimate the wave speed of a heterogeneous, inaccessible medium, from time-resolved measurements of the waves at user controlled sensors. We consider this inverse problem for acoustic waves and an active array of source/receiver sensors that emit probing signals and measu…

• Seismic Acoustic Impedance Inversion Framework Based on Conditional Latent Generative Diffusion Model

  Jie Chen, Hongling Chen, Jinghuai Gao, Chuangji Meng, Tao Yang, XinXin Liang · 25 août 2026

  Seismic acoustic impedance plays a crucial role in lithological identification and subsurface structure interpretation. However, due to the inherently ill-posed nature of the inversion problem, directly estimating impedance from post-stack seismic data remains highly challenging. Recently, diffusion…

• SeisEvo: Evolution of Seismic Data Reconstruction Algorithms by Agents

  Yingjie Xu, Siwei Yu, Jianwei Ma · 20 août 2026

  Classical seismic data reconstruction relies on manually designed structural priors and iterative operators, whose coupled design space is far larger than manual trial and error can explore systematically. Deep-learning methods encode the reconstruction rules in learned weights rather than in an exp…

• Voxel-based 3D Facies Segmentation from Seismic Data: A Comparative Study

  Duc-Thanh Pham, Minh-Tan Pham, Anh Nguyen, Van Nguyen · 17 août 2026

  Seismic facies segmentation has emerged as a significant challenge in geophysics, requiring robust methods and systems to effectively identify geologically analogous facies with limited labeled data. Although existing studies have shown promising results in 2D facies segmentation, they often preproc…

• RIPPLE: Generating Multi-Channel Phase, Not Recovering It

  Jaehyuk Lee, Yeajin Lee, Dayeon Shin, Donghun Lee · 3 août 2026

  Generative models synthesize magnitude spectra with high fidelity, while phase is delegated to a recovery module---Griffin--Lim, a vocoder, or a latent decoder---applied independently to each channel. For multi-channel waveforms this delegation is costly: the physical content of spatial audio and th…

• Latent Variable-Mediated Cross-Learning for Few-Shot Acoustic Impedance Imaging

  Junheng Peng, Yong Li, Mingwei Wang, Yi Bao · 24 juillet 2026

  Acoustic impedance imaging is a fundamental yet severely ill-posed problem in subsurface analysis: the seismic wavelet is unknown, observations are band-limited, and labeled well-log samples are extremely scarce (typically <1% of all traces). Existing semi-supervised deep learning methods mitigate f…

• Joint Velocity Slope Diffusion Prior for Structurally Constrained Velocity Model Building

  Francesco Brandolin, Tariq Alkhalifah · 7 juillet 2026

  High-resolution velocity models are crucial for reservoir characterization and subsurface delineation. However, the band limited nature of our surface recorded data limits resolution. Utilizing well measurements to enhance the resolution of our subsurface models is an important objective. To this en…

• Generative wave propagator

  Shijun Cheng, Tariq Alkhalifah · 7 juillet 2026

  Seismic wavefield simulation is fundamental to seismology, but conventional finite-difference (FD) methods remain limited by numerical dispersion and stability constraints, which often require dense spatial grids and small time steps and thereby severely limit the effectiveness of iterative inversio…

• Probabilistic Inversion with Flow Matching

  Baldur Paulwitz, Stefan Buske · 1 juillet 2026

  We demonstrate the application of Flow Matching, a technique originating from generative Artificial Intelligence, to probabilistic inversion in geophysical settings, such as seismic Full-Waveform inversion. We adapt the well-established mathematical theory of Flow Matching from generative Artificial…

• RotRNN: Modelling Long Sequences with Rotations

  Kai Biegun, Rares Dolga, Jake Cunningham, David Barber · 25 juin 2026

  Linear recurrent neural networks, such as State Space Models (SSMs) and Linear Recurrent Units (LRUs), have recently shown state-of-the-art performance on long sequence modelling benchmarks. Despite their success, their empirical performance is not well understood and they come with a number of draw…

• Tensor Train Decomposition-based 3D Implicit Full Waveform Inversion with Multi-scale Structural Similarity

  Liangsheng He, Chao Song, Tiansheng Chen, Tao Liu, Cai Liu · 23 juin 2026

  Three-dimensional full waveform inversion (3DFWI) is a powerful technique for reconstructing high-resolution subsurface velocity models. However, its application is often limited by high memory requirements, computational costs, and sensitivity to cycle skipping. To overcome these challenges, we pro…

• Bayesian three-dimensional seismic travel-time tomography for active- and passive-source seismic data using physics-informed neural network

  Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie, Dan Bassett · 23 juin 2026

  Accurate 3D seismic velocity modeling through seismic travel-time tomography using both active- and passive-source data provides critical underpinning models for seismicity monitoring and hazard assessment. Because travel-time tomography is an inherently ill-posed inverse problem, UQ of the estimate…

• SFT Overtraining Predicts Rank Inversion via Entropy Collapse Under RLVR

  Siddharth Aphale, Kelly Liu · 18 juin 2026

  The standard heuristic of selecting the SFT checkpoint with the highest pass@1 for GRPO can fail when SFT compresses the rollout distribution. For binary rewards, the expected within group advantage variance is $p(1{-}p)(g{-}1)/g$; when early GRPO drives $p$ below $p^*(g)$, most groups have identica…

• Deep Learning in Seismic Interpretation: Federated Advances in Salt Dome Segmentation

  Muhammad Zain Mehdi, Muhammad Zaid, Owais Aleem · 16 juin 2026

  Salt-dome delineation is a critical, high-impact task in subsurface geological interpretation, driving decisions in hydrocarbon exploration, reservoir modeling, and drilling safety. While convolutional encoder-decoder architectures have delivered significant improvements in automated salt segmentati…

• Contrastive Learning for Seismic Horizon Tracking with Domain-Specific Priors

  Alexandre Thouvenot, Lionel Boillot, Vincent Gripon · 16 juin 2026

  Unsupervised 3D seismic horizon tracking faces a key limitation: signal-based propagators provide accurate trace-level alignment but often fail near faults, whereas texture-driven deep models are more robust to discontinuities, typically at the cost of labeled data requirements and reduced trace-lev…

• Bridging data-driven priors via the score function for posterior sampling -- Comparative review and experimental study

  Elhadji Cisse Faye, Mame Diarra Fall, Sylvain Delchini, Nicolas Dobigeon · 16 juin 2026

  This paper reviews how a diverse set of popular data-driven priors commonly used in Bayesian inverse problems can be unified through their respective score functions. By framing these priors under this common perspective, we show that they can benefit from their straightfoward and effective integrat…

• Decoupled Latent Optimization of Diffusion Models for Full Waveform Inversion

  Chen Min, Zheng Ma · 15 juin 2026

  Full waveform inversion (FWI) recovers subsurface velocity from seismic recordings by solving a severely ill-posed, nonconvex PDE-constrained optimization. Classical regularizers stabilize the inversion but fail to reproduce realistic geological structures; recent diffusion-prior methods improve rea…

• SubsurfaceGen: Procedural Generation of Field-Scale Earth Models and Seismic Data

  Joseph Stitt, Pratik Rathore, Madeleine Udell, Ching-Yao Lai · 1 juin 2026

  Full waveform inversion (FWI) is the gold standard for subsurface imaging, with applications from carbon sequestration to energy and mineral exploration to earthquake hazard assessment. Machine learning approaches to FWI need field-scale, geologically diverse, and physically realistic training data,…

• TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection

  Yoon Gyo Jung, Jaewoo Park, Jaeho Yoon, Kuan-Chuan Peng, Wonchul Kim, Andrew Beng Jin Teoh, Octavia Camps · 27 mai 2026

  We aim to solve unsupervised anomaly detection in a practical challenging environment where the normal dataset is both contaminated with defective regions and its product class distribution is tailed but unknown. We observe that existing models suffer from tail-versus-noise trade-off where if a mode…

• OpenSeisML: Open Large-Scale Real Seismic and well-log Dataset for Generative AI

  Ipsita Bhar, Huseyin Tuna Erdinc, Thales Souza, Charles Jones, Felix J. Herrmann · 21 mai 2026

  The advent of machine learning (ML) and computer vision has significantly accelerated seismic inversion workflows by reducing the computational cost of traditionally expensive iterative methods. However, the development and evaluation of ML methods remain limited by the scarcity of realistic velocit…

• Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions

  Van Khoa Nguyen, Lionel Blond\'e, Alexandros Kalousis · 19 mai 2026

  Training-free diffusion guidance offers a flexible framework for leveraging off-the-shelf classifiers without additional training. Yet, current approaches hinge on posterior approximations via Tweedie's formula, which often yield unreliable guidance, particularly in low-density regions. Stochastic o…

• Quantum Feature Pyramid Gating for Seismic Image Segmentation

  Taha Gharaibeh, Jyotsna Sharma · 18 mai 2026

  Accurate salt-body delineation is essential for seismic interpretation because salt structures distort wave propagation, complicate velocity-model building, obscure reservoir geometry, and increase uncertainty in exploration and drilling decisions. Although hybrid quantum-classical models have shown…