Unsupervised Diffusion Model for Seismic Deconvolution

Yu, Hongzhi

doi:10.1109/LGRS.2025.3549055

Unsupervised Diffusion Model for Seismic Deconvolution

2025-01-01

0

PRE

AI

DOI：10.1109/LGRS.2025.3549055

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Seismic data deconvolution is vital for enhancing resolution and accurate subsurface interpretation. Traditional methods heavily rely on predefined assumptions that limit their robustness to noisy data. As state-of-the-art generative models, diffusion models excel in capturing accurate prior distributions, which are beneficial to inversion. Moreover, diffusion models inherently resist noise due to their training in reverse noisy processes. Building on this foundation, we introduce an unsupervised diffusion model for seismic deconvolution, leveraging diffusion posterior sampling (DPS) to incorporate observed seismic data into the sampling process to guide high-accuracy reflectivity generation. Unlike traditional single-trace approaches, our method performs deconvolution across entire 2-D profiles, effectively capturing spatial continuity. Though solely trained on synthetic data, our method exhibits satisfactory performance when applied to synthetic and field datasets, demonstrating strong noise resistance and remarkable generalization capabilities.

Keyword:

Deconvolution

Diffusion models

Training

Noise

Reflection coefficient

Mathematical models

Data models

Noise measurement

Deep learning

Robustness

Diffusion model

high-resolution seismic data

seismic deconvolution

unsupervised

Unsupervised Diffusion Model for Seismic Deconvolution

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