Satellite magnetic field data fusion seismic anomaly extraction method based on tensor decomposition

Abstract

The invention relates to a satellite magnetic field data fusion seismic anomaly extraction method based on tensor decomposition. The method comprises the following steps: subtracting CHAOS-7 magnetic field model data from multi-channel satellite magnetic field data to remove an earth main magnetic field and a static lithosphere magnetic field; removing low-frequency components of the residual data by using wavelet decomposition; constructing a corresponding third-order time-frequency spectrum tensor according to the time-frequency amplitude matrix of the magnetic field data of each channel obtained by synchronous extrusion wavelet transform; decomposing the constructed third-order time-frequency spectrum tensor by using non-negative tensor decomposition to obtain a fused local feature component related to the earthquake; and selecting a fusion local feature component according to the energy-entropy ratio, extracting fusion seismic anomalies in the component through a threshold segmentation method, and outputting a fusion seismic anomaly result. According to the method, the information of the multi-channel satellite magnetic field data is combined, the fusion local feature component related to the earthquake is obtained, and the extracted fusion earthquake anomaly is more reliable.

Description

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AI Technical Summary

Benefits of technology

This patented technology uses techniques called Tity Decomposition (TDA) that reduces noise from multiple sources such as geomagnetic fields or other physical phenomena related to space exploration. By removing specific parts of these signals while preserving their important properties like orientation, velocity, etc., we aimed at extracting useful information about them through various methods. These technical improvements make it possible to better identify faulty areas caused by environmental factors during explorations.

Problems solved by technology

Technological Problem: Current Ground Surface Mapping (GSM) methods used during exploration involve combining multiple channel image datasets together without assuming specific relationships between these sources. However, current G SM approaches cannot accurately determine if one source's anomalys exist within another type of dataset.

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