WISE: Full-waveform variational inference via subsurface extensions

Yin, Ziyi; Orozco, Rafael; Louboutin, Mathias; Herrmann, Felix J.

doi:10.1190/geo2023-0744.1

GEOPHYSICS

2024

DOI: 10.1190/geo2023-0744.1

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WISE: Full-waveform variational inference via subsurface extensions

Ziyi Yin,

Rafael Orozco,

Mathias Louboutin

et al.

Abstract: We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are … Show more

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2024

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Cited by 3 publications

References 34 publications

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Controllable Seismic Velocity Synthesis Using Generative Diffusion Models

Wang,

Huang,

Alkhalifah

2024

Journal of Geophysical Research: Machine Learning and Computati

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Accurate seismic velocity estimations are vital to understanding Earth's subsurface structures, assessing natural resources, and evaluating seismic hazards. Machine learning‐based inversion algorithms have shown promising performance in regional (i.e., for exploration) and global velocity estimations, while their effectiveness hinges on access to large and diverse training data sets whose distributions generally cover the target solutions. Additionally, enhancing the precision and reliability of velocity estimation also requires incorporating prior information, for example, geological classes, well logs, and subsurface structures, but current statistical or neural network‐based methods are not flexible enough to handle such multimodal information. To address both challenges, we propose to use conditional generative diffusion models for seismic velocity synthesis in which we readily incorporate those priors. This approach enables the generation of seismic velocities that closely match the expected target distribution, offering data sets informed by both expert knowledge and measured data to support training for data‐driven geophysical methods. We demonstrate the flexibility and effectiveness of our method through training diffusion models on the OpenFWI data set under various conditions, including class labels, well logs, reflectivity images, and the combination of these priors. The performance of the approach under out‐of‐distribution conditions further underscores its generalization ability, showcasing its potential to provide tailored priors for velocity inverse problems and create specific training data sets for machine learning‐based geophysical applications.

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Controllable Seismic Velocity Synthesis Using Generative Diffusion Models

Wang,

Huang,

Alkhalifah

2024

Journal of Geophysical Research: Machine Learning and Computati

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Paired autoencoders for likelihood-free estimation in inverse problems

Chung,

Hart,

Chung

et al. 2024

Mach. Learn.: Sci. Technol.

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We consider the solution of nonlinear inverse problems where the forward problem is a discretization of a partial differential equation. Such problems are notoriously difficult to solve in practice and require minimizing a combination of a data-fit term and a regularization term. The main computational bottleneck of typical algorithms is the direct estimation of the data misfit. Therefore, likelihood-free approaches have become appealing alternatives. Nonetheless, difficulties in generalization and limitations in accuracy have hindered their broader utility and applicability. In this work, we use a paired autoencoder framework as a likelihood-free estimator for inverse problems. We show that the use of such an architecture allows us to construct a solution efficiently and to overcome some known open problems when using likelihood-free estimators. In particular, our framework can assess the quality of the solution and improve on it if needed. We demonstrate the viability of our approach using examples from full waveform inversion and inverse electromagnetic imaging.

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Time-lapse full-waveform permeability inversion: A feasibility study

Yin,

Louboutin,

Møyner

et al. 2024

The Leading Edge

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Time-lapse seismic monitoring necessitates integrated workflows that combine seismic and reservoir modeling to enhance reservoir property estimation. We present a feasibility study of an end-to-end inversion framework that directly inverts for permeability from multiple prestack time-lapse seismic data sets. To assess the method's robustness, we design experiments focusing on its sensitivity to initial models, potential errors in modeling, and crosstalk during multiparameter inversion. Our study leverages the publicly available Compass model to simulate CO2 storage in saline aquifers. This model is derived from well and seismic data from the North Sea in an area that is currently considered for geologic carbon storage.

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WISE: Full-waveform variational inference via subsurface extensions

Cited by 3 publications

References 34 publications

Controllable Seismic Velocity Synthesis Using Generative Diffusion Models

Controllable Seismic Velocity Synthesis Using Generative Diffusion Models

Paired autoencoders for likelihood-free estimation in inverse problems

Time-lapse full-waveform permeability inversion: A feasibility study

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