The article introduces a novel diffusion model, SeisFusion, designed for reconstructing complex 3D seismic data. It addresses challenges faced by traditional methods in handling missing traces within seismic data. The proposed model incorporates conditional supervision constraints and a 3D neural network architecture to extend the 2D diffusion model to 3D space. By refining the generation process and incorporating missing data, SeisFusion achieves reconstructions with higher consistency. Through ablation studies, optimal parameter values were determined, showcasing superior accuracy in field datasets and synthetic datasets. The method demonstrates effectiveness in addressing a wide range of complex missing patterns.
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by Shuang Wang,... lúc arxiv.org 03-19-2024
https://arxiv.org/pdf/2403.11482.pdfYêu cầu sâu hơn