.png){kind=icon}
返回
A Prior Regularized Full Waveform Inversion Using Generative Diffusion Models
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2023.3337014.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Full waveform inversion (FWI) has the potential to provide high-resolution subsurface model estimations. However, due to limitations in observation, e.g., regional noise, limited aperture, and band-limited data, it is hard to obtain the desired high-resolution model with FWI. To address this challenge, we propose a new paradigm for FWI regularized by generative diffusion model. Specifically, we pretrain a diffusion model in a fully unsupervised manner on a prior velocity model distribution that represents our expectations of the subsurface and then adapt it to the seismic observations by incorporating the FWI into the sampling process of the generative diffusion models. What makes diffusion models uniquely appropriate for such an implementation is that the generative process retains the form and dimensions of the velocity model. Numerical examples demonstrate that our method can outperform the conventional FWI with only negligible additional computational cost. Even in cases of very sparse observations or observations with strong noise, the proposed method could still reconstruct a high-quality subsurface model. Thus, we can incorporate our prior expectations of the solutions in an efficient manner. We further test this approach on field data, which demonstrates the effectiveness of the proposed method.
Keyword:
Mathematical models
Data models
Adaptation models
Training
Computational modeling
Numerical models
Noise reduction
Denoising diffusion probabilistic models (DDPMs)
full-waveform inversion (FWI)
high-resolution
regularization
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
分享收藏Effectiveness of Diagnosis and Treatment of Spiral Fracture of the Distal Third of the Tibia Combined with Posterior Malleolus Fracture胫骨远端1/3螺旋骨折合并后踝骨折的诊断与治疗效果
PREAI
分享收藏Full model wavenumber inversion: Identifying sources of information for the elusive middle model wavenumbers.png)
GEOPHYSICS
GEOPHYSICSIF3.2
PREAI
分享收藏分享收藏Multi-Task Learning for Low-Frequency Extrapolation and Elastic Model Building From Seismic Data
OAAI
OAAI分享收藏Sparse-promoting full-waveform inversion based on online orthonormal dictionary learningGEOPHYSICS
OAAI
GEOPHYSICSIF3.2
OAAI分享收藏
查看更多内容