Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations with Diffusion Models
In this study, we introduce a novel approach to synthesizing subsurface velocity models using diffusion generative models. Conventional methods rely on extensive, high-quality datasets, which are often inaccessible in subsurface applications. Our method leverages incomplete well and seismic observations to produce high-fidelity velocity samples without requiring fully sampled training datasets. The results demonstrate that our generative model accurately captures long-range structures, aligns with ground-truth velocity models, achieves high Structural Similarity Index (SSIM) scores, and provides meaningful uncertainty estimations. This approach facilitates realistic subsurface velocity synthesis, offering valuable inputs for full-waveform inversion and enhancing seismic-based subsurface modeling.
Code (0)
등록된 구현이 없습니다.
Tasks
SSIMMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Physics-informed semantic inpainting: Application to geostatistical modeling
A fundamental problem in geostatistical modeling is to infer the heterogeneous geological field based on limited measurements and some prior spatial statistics. Semantic inpainting, a technique for image processing using…
Generative Adversarial NetworkLeveraging generative adversarial networks with spatially adaptive denormalization for multivariate stochastic seismic data inversion
Probabilistic seismic inverse modeling often requires the prediction of both spatially correlated geological heterogeneities (e.g., facies) and continuous parameters (e.g., rock and elastic properties). Generative advers…
SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior
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 approach…
DeepFlow: History Matching in the Space of Deep Generative Models
The calibration of a reservoir model with observed transient data of fluid pressures and rates is a key task in obtaining a predictive model of the flow and transport behaviour of the earth's subsurface. The model calibr…
Generative Adversarial NetworkMitigation of Spatial Nonstationarity with Vision Transformers
Spatial nonstationarity, the location variance of features' statistical distributions, is ubiquitous in many natural settings. For example, in geological reservoirs rock matrix porosity varies vertically due to geomechan…
Deep Learning