@unpublished {zeng2026IMAGEbjv,
	title = {Bayesian Joint Velocity and Impedance Inversion via Diffusion Models Conditioned on Common Image Gathers},
	year = {2026},
	month = {3},
	abstract = {We present a multi-parameter simulation-based inference framework for joint Bayesian recovery of subsurface velocity and acoustic impedance from seismic data. A score-based diffusion model is conditioned on two complementary Common Image Gathers (CIGs): an inverse-scattering CIG encoding reflectivity amplitude and an anti-ISIC CIG encoding kinematic velocity errors. The model simultaneously samples the posterior distributions of both parameters. Training labels are deliberately decoupled to prevent the model from exploiting the Gardner relationship: velocity targets are lightly smoothed to match the long-wavelength content of the anti-ISIC CIG, while impedance targets retain the unsmoothed ground truth. On the Compass benchmark the model achieves velocity SSIM of 0.967 (RMSE 0.050 km/s) and impedance SSIM 0.867 (RMSE 0.279 km/s{\textperiodcentered}g/cm{\textthreesuperior}), with velocity quality confirmed by CIG focusing.},
	keywords = {Bayesian inference, CIG, deep learning, diffusion models, FWI, IMAGE, Imaging, Inverse problems, MVA, RTM, SEG, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2026/zeng2026IMAGEbjv/abstract.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2026/zeng2026IMAGEbjv/IMAGE_Yunlin.pdf},
	author = {Yunlin Zeng and Huseyin Tuna Erdinc and Felix J. Herrmann}
}
@unpublished {park2026IMAGEnoa,
	title = {Neural Operators with Accurate Jacobian for High-Fidelity Image-Domain Seismic Inversion},
	year = {2026},
	month = {3},
	abstract = {Neural operators (NOs) provide efficient surrogates for wave-equation-based imaging operators, but accurate forward prediction alone does not guarantee reliable inversion. As inversion updates depend on the adjoint Jacobian, errors in learned sensitivities lead to unphysical updates and degraded quality in recovered models. To this end, we propose a Jacobian-informed training strategy that enforces accuracy in Jacobian in the directions where gradient descent occurs during Least-Squares. Numerical experiments with Fourier Neural Operators demonstrate that supervising only a small fraction ($0.1 \sim 0.01\%$) of Jacobian information significantly improves gradient quality and leads to more accurate subsurface velocity reconstruction. In particular, the proposed approach reduces mid to deep layer artifacts and improves structural continuity compared to models trained only with forward misfit term. These results highlight that learning inversion-relevant sensitivities, rather than forward mappings alone, is critical for reliable surrogate-assisted seismic inversion.},
	keywords = {deep learning, derivative-informed, Fisher-information, FNO, IMAGE, Imaging, Inverse problems, reduced-order modeling, RTM, SEG, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2026/park2026IMAGEnoa/abstract.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2026/park2026IMAGEnoa/IMAGE_Jayjay.pdf},
	author = {Jeongjin Park and Huseyin Tuna Erdinc and Grant Bruer and Richard Rex and Nisha Chandramoorthy and Felix J. Herrmann}
}
@unpublished {bhar2026IMAGEolr,
	title = {OpenSeisML: Open Large-Scale Real Seismic and well-log Dataset for Generative AI},
	year = {2026},
	month = {3},
	abstract = {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 remains limited by the scarcity of realistic velocity models, as most high-quality data are privately owned by oil and gas companies. To address this gap, we present *OpenSeisML*, a collection of real seismic datasets designed to support generative AI (Gen-AI) workflows for seismic inversion. The datasets are curated from publicly available surveys in the UK National Data Repository (NDR). When seismic volumes are in the time domain and wells are in depth, a time-to-depth conversion is required. We use checkshot data to establish the time{\textendash}depth relationship and construct a velocity model through interpolation for accurate conversion of post-stack seismic data. Here, we present an automated data curation pipeline that enables seismic data preparation while ensuring reproducibility. The objective is to train a generative model that captures the statistical distribution of subsurface properties, enabling the synthesis of multiple statistically consistent realizations for uncertainty quantification which can act as a prior for seismic inversion.},
	keywords = {deep learning, generative model, IMAGE, Imaging, Inverse problems, real seismic data, SEG, time-to-depth conversion, well logs, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2026/bhar2026IMAGEolr/abstract.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2026/2026IMAGEolr/IMAGE_Ipsita.pdf},
	author = {Ipsita Bhar and Huseyin Tuna Erdinc and Thales Souza and Charles Jones and Felix J. Herrmann}
}
@unpublished {erdinc2026IMAGEsai,
	title = {SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior},
	year = {2026},
	month = {3},
	abstract = {Recent advances in generative networks have enabled new approaches to subsurface velocity model synthesis, offering a compelling alternative to traditional methods such as Full Waveform Inversion. However, these approaches predominantly rely on the availability of large-scale datasets of high-quality, geologically realistic subsurface velocity models, which are often difficult to obtain in practice. We introduce SAGE, a novel framework for statistically consistent proxy velocity generation from incomplete observations, specifically sparse well logs and migrated seismic images. During training, SAGE learns a proxy posterior over velocity models conditioned on both modalities (wells and seismic); at inference, it produces full-resolution velocity fields conditioned solely on migrated images, with well information implicitly encoded in the learned distribution. This enables the generation of geologically plausible and statistically accurate velocity realizations. We validate SAGE on both synthetic and field datasets, demonstrating its ability to capture complex subsurface variability under limited observational constraints. Furthermore, samples drawn from the learned proxy distribution can be leveraged to train downstream networks, supporting inversion workflows. Overall, SAGE provides a scalable and data-efficient pathway toward learning geological proxy posterior for seismic imaging and inversion. Repo link: https://github.com/slimgroup/SAGE."},
	keywords = {Bayesian inference, deep learning, generative model, IMAGE, Imaging, Inverse problems, RTM, SAGE, score, SEG, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2026/erdinc2026IMAGEsai/abstract.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2026/erdinc2026IMAGEsai/IMAGE_Tuna.pdf},
	author = {Huseyin Tuna Erdinc and Ipsita Bhar and Rafael Orozco and Thales Souza and Felix J. Herrmann}
}
@unpublished {orozco2024ARXIVmle,
	title = {Machine learning-enabled velocity model building with uncertainty quantification},
	year = {2024},
	month = {11},
	abstract = {Accurately characterizing migration velocity models is crucial for a wide range of geophysical applications, from hydrocarbon exploration to monitoring of CO2 sequestration projects. Traditional velocity model building methods such as Full-Waveform Inversion (FWI) are powerful but often struggle with the inherent complexities of the inverse problem, including noise, limited bandwidth, receiver aperture and computational constraints. To address these challenges, we propose a scalable methodology that integrates generative modeling, in the form of Diffusion networks, with physics-informed summary statistics, making it suitable for complicated imaging problems including field datasets. By defining these summary statistics in terms of subsurface-offset image volumes for poor initial velocity models, our approach allows for computationally efficient generation of Bayesian posterior samples for migration velocity models that offer a useful assessment of uncertainty. To validate our approach, we introduce a battery of tests that measure the quality of the inferred velocity models, as well as the quality of the inferred uncertainties. With modern synthetic datasets, we reconfirm gains from using subsurface-image gathers as the conditioning observable. For complex velocity model building involving salt, we propose a new iterative workflow that refines amortized posterior approximations with salt flooding and demonstrate how the uncertainty in the velocity model can be propagated to the final product reverse time migrated images. Finally, we present a proof of concept on field datasets to show that our method can scale to industry-sized problems.},
	keywords = {arxiv, attention, Bayesian inference, CIG, deep learning, diffusion models, FWI, Imaging, Inverse problems, learned summary statistics, RTM, uncertainty calibration, Uncertainty quantification, velocity model building, WISE},
	doi = {10.48550/arXiv.2411.06651},
	author = {Rafael Orozco and Huseyin Tuna Erdinc and Yunlin Zeng and Felix J. Herrmann}
}
