A background noise empirical green's function interpolation method based on a conditional diffusion model

By using a deep neural network based on a conditional diffusion model, seismograph coordinate information is learned to perform high-dimensional background noise empirical Green's function interpolation, which solves the shortcomings of traditional algorithms in frequency component phase recovery and achieves high-precision underground structure detection.

CN121679707BActive Publication Date: 2026-06-23UNIV OF SCI & TECH OF CHINA
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Patent Information

Application Number
CN202511816020.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-12-04
Publication Date
2026-06-23
Estimated Expiration
2045-12-04

AI Technical Summary

Technical Problem

Existing technologies struggle to accurately interpolate empirical Green's function signals for high-dimensional background noise, especially when the number of seismographs deployed is insufficient. Traditional algorithms also struggle to recover the phase characteristics of different frequency components in the signal, affecting the quality of underground structure detection.

Method used

A deep neural network based on a conditional diffusion model is used to learn seismograph coordinates as prior information, gradually remove Gaussian noise, and generate empirical Green's function signals for points where no seismographs are deployed, thereby achieving high-precision interpolation.

Benefits of technology

It improves the accuracy and efficiency of underground structure detection, and can replace real observation data under low-cost conditions, thereby enhancing the effectiveness of underground mineral exploration and disaster assessment.

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Abstract

The present application relates to the technical field of underground structure detection, and discloses a background noise empirical Green function interpolation method based on a conditional diffusion model, which comprises the following steps: collecting empirical Green function signals between station pairs formed by all seismographs in a target area and position coordinates of all seismographs as a training data set; inputting the empirical Green function signal containing noise, position coordinates of two seismographs in a station and a current denoising step number into a denoising neural network; taking the original empirical Green function signal in the training data set as a training target, inputting corresponding position coordinates of any two target points without seismographs into the trained denoising neural network, and generating an interpolated empirical Green function signal between the two target points. The method is more accurate than a traditional empirical Green function signal interpolation algorithm, can replace real observation data to a certain extent in a scenario where the number of seismographs is insufficient, and improves the underground structure detection capability.
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