Enhanced and multi-purpose analysis of aeromagnetic anomalies by combining topographic data

Abstract

Abstract Tie-lines are used in aeromagnetic surveys to detect measurement errors and compensate for insufficient measurement data in the north-south direction. However, increasing the number of tie-lines to improve the accuracy of interpretation can be very challenging. The aim of this study was to overcome this issue by combining aeromagnetic and related multi-parametric data. Therefore, we generated an aeromagnetic map using simple kriging with local varying means (SK-lvm) based on topographic data correlated with the distribution patterns of the aeromagnetic data. The generated aeromagnetic map achieved reduced uncertainties and increased resolution compared with the conventional aeromagnetic map as a result of the interpolation of the magnetic data, which leveraged the dense distribution of the topographic data points and enabled a clearer analysis of geological structures. Generally, topographic and aeromagnetic data were positively correlated with deviating patterns displayed in some regions; however, all the regions shown were classified into Types 1–4 based on the correlation regression equation. Types 1 and 4 showed positive correlations, while Types 2 and 3 exhibited negative correlations with the following characteristics: Type 2 regions are characterized by high altitude and low magnetic anomalies, while Type 3 regions are characterized by low altitude and high magnetic anomalies. The analysis revealed that Type 2 regions are distributed around high-altitude mountainous areas covered with igneous and sedimentary rocks with low magnetic anomalies, whereas Type 3 regions abound with igneous rocks with high magnetic anomalies and mining sites in low-altitude alluvium or coastal areas. Thus, the proposed aeromagnetic map improved resolution and enhanced the reliability of the interpretation (compared with those of a conventional aeromagnetic map) by combining topographic datasets using SK-lvm to process and interpret the comprehensive aeromagnetic survey data. Moreover, the correlation analysis results for the two datasets are expected to serve as useful reference data for determining the characteristic type of rocks distributed in the study area.