A method for reducing low latitude magnetic anomalies

By constructing a model space and an FCN network structure, the problems of instability and insufficient accuracy in low-latitude magnetic anomaly pole-forming calculations were solved, resulting in a significant improvement in pole-forming results.

CN115423004BActive Publication Date: 2025-12-05SOUTHWEST JIAOTONG UNIV
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Patent Information

Application Number
CN202211004962.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-08-22
Publication Date
2025-12-05
Estimated Expiration
2042-08-22

AI Technical Summary

Technical Problem

Existing technologies for magnetic anomaly polarization in low-latitude regions suffer from computational instability and insufficient accuracy, resulting in discrepancies between the polarization results and actual theoretical outcomes.

Method used

A model space for the sample dataset is constructed, and a subsurface half-space is formed by combining cuboid units. A stable initial model is obtained through frequency domain suppression, and polarization prediction is performed by combining FCN network structure training and experimental data.

Benefits of technology

It improves the operational stability and accuracy of low-latitude magnetic anomaly polarization, significantly enhancing the effectiveness, robustness, and accuracy of the polarization results, with the coefficient of determination increasing to over 98%.

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Abstract

The application discloses a method for reducing low-latitude magnetic anomaly, comprising the following steps: S1, constructing a model space of a sample data set, wherein the model space is composed of an underground half space and a plurality of cuboid units, and the plurality of cuboid units are arranged in a combined form in a non-edge area of the underground half space; S2, performing traversal sampling and data amplification to obtain a sample data equivalent magnetic anomaly body; S3, acquiring a stable initial model by using a frequency domain suppression method; S4, taking the stable initial model and a magnetic anomaly with an arbitrary magnetization direction as inputs of a FCN network structure, taking a magnetic anomaly perpendicular to the magnetization direction as a label, and training the FCN reduction network structure; and S5, taking measured data and the stable initial model as inputs of the trained FCN reduction network structure to obtain a reduction prediction result. The application can obtain more accurate reduction results and provide technical support for geological interpretation.
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