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基于扩散概率模型的非均一地震数据插值方法

陈尧 于四伟 林荣智

煤田地质与勘探2024,Vol.52Issue(8):177-186,10.
煤田地质与勘探2024,Vol.52Issue(8):177-186,10.DOI:10.12363/issn.1001-1986.24.03.0160

基于扩散概率模型的非均一地震数据插值方法

A non-uniform interpolation method for seismic data based on a diffusion probabilistic model

陈尧 1于四伟 1林荣智1

作者信息

  • 1. 哈尔滨工业大学 数学学院,黑龙江 哈尔滨 150001
  • 折叠

摘要

Abstract

[Objective]The non-uniform interpolation of seismic data is identified as a prolonged challenge in energy ex-ploration.Since geophones cannot be precisely placed at positions corresponding to theoretical grid points,current uni-form interpolation techniques frequently suffer deviations and detail distortion.[Methods]This study proposed a novel non-uniform interpolation method based on a diffusion probabilistic model,which is an emerging generative model in deep learning that involves the diffusion and generation processes.In the diffusion process,noise is added to the com-plete seismic data iteratively to train the denoising capability of the neural network.In the generation process,the neural network is employed for iterative denoising of data containing noise to obtain the reconstructed data.In this study,inter-polation operators were employed to calculate the deviations between iterative and sampled data.These deviations were then used as the additional inputs of the neural network to improve the non-uniform interpolation capability of the diffu-sion probabilistic model.In the numerical experiments,the non-uniform sampling was tested using 2D synthetic and ac-tual datasets,and the uniform interpolation model was compared with the model in this proposed study.[Results and Conclusions]The results indicate that the proposed method significantly enhanced the processing capability of the diffu-sion probabilistic model for non-uniform sampling.The tests of synthetic and actual data revealed an increase of approx-imately 7 dB in the signal-to-noise ratio.Therefore,the proposed method can effectively improve the precision of deep learning for non-uniform interpolation,providing a new approach for non-uniform interpolation algorithms of seismic data.

关键词

地震数据插值/非均一采样/深度学习/生成式模型/扩散概率模型

Key words

interpolation of seismic data/non-uniform sampling/deep learning/generative model/diffusion probabilist-ic model

分类

地质学

引用本文复制引用

陈尧,于四伟,林荣智..基于扩散概率模型的非均一地震数据插值方法[J].煤田地质与勘探,2024,52(8):177-186,10.

基金项目

国家自然科学基金项目(42074156) (42074156)

煤田地质与勘探

OA北大核心CSTPCD

1001-1986

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