Gravity Data Fusion and Imaging of Geological Structures in the Red River Fault Zone and Adjacent Areas

TL;DR

This study improves gravity data analysis in the Red River Fault Zone to better understand geological structures and crustal density variations.

Contribution

A new gravity data-fusion method is proposed to enhance crustal imaging in data-scarce regions.

Findings

Fused gravity data accurately reflect both regional and local anomaly trends with high precision.

Low-density zones in the northern and southern parts of the fault are shallower (~20 km) compared to the middle section.

The method achieves a root-mean-square error of less than 5% and a correlation coefficient over 90%.

Abstract

The geological structure in the Red River fault zone (RRF) and adjacent areas is complex. Due to the lack of high-precision gravity data in the study area, it is difficult to obtain the distribution of materials within the Earth’s crust. In this study, a gravity data-fused method is proposed. The Moho depth model data are utilized to construct the gravity anomaly trend, and the mapping relation between the gravity field model data and the measured gravity data is established. Using 934 high-precision measured gravity data as control points, the bilinear interpolation method is used to calculate high-precision grid data of the RRF. Finally, the apparent density inversion method is used to obtain clear crustal density images across the RRF. The experimental results show that the fuses data not only reflect the regional anomaly trend but also maintain the local anomaly information; the root-mean-square error of the fused data is less than 5% and the correlation coefficient is greater than 90%. Through an in-depth comparative analysis of density images, it is found that the low-density anomalous zones, with depths of ~20 km in the northern and southern sections of the RRF, are shallower than those in the middle. The data-fused method provides a new way to process geophysical data more efficiently.

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