A Dual-Prior Conditional Probability Diffusion Model for Seismic Data Resolution Enhancement

Abstract

Seismic interpretation is crucial in seismic exploration to identify geological structures in the field. However, interpretation is often challenging due to inherent low-resolution (LR) limitations, and acquiring high-quality data is expensive with no guaranteed fidelity. To address these challenges, we propose a novel supervised deep learning-based method to enhance seismic data resolution by simultaneously focusing on dominant wavelet frequency and the spatial domain. First, we employ a deep learning module to estimate the location of low-frequency wavelets as semantic information, providing a prior that allows the model to process these signals and precisely enhance the dominant frequency. Subsequently, we use a feature wrapper to integrate the LR data with the semantic priors. We use them as conditions for a diffusion model to generate high-frequency features, considered priors with higher-frequency wavelets. Finally, we input the priors and the LR data into a feature fusion module (FFM) to generate the final output, doubling the sampling points and traces to achieve high-resolution (HR) data with high-frequency wavelets. The models are trained separately using cross-entropy, Kullback-Leibler divergence, and Charbonnier loss. The priors we use and the design of the generative diffusion model ensure high fidelity, preventing false seismic events and over-smoothing. Experiments on field data demonstrate the superiority of our method compared to three other approaches, highlighting its potential as a powerful seismic super-resolution tool in practical applications.