A novel filtering technique for enhancing mineralization associated geochemical and geophysical anomalies

Abstract

A novel method named optimal filtering is introduced in this paper. This method is aimed at producing maps resulting from the filtering of geochemical or geophysical data in order to indicate occurrences of mineral deposits most effectively. Two measures are proposed to enhance the capability of indicating deposits: (1) area under operating characteristic curve (AUC); and (2) Fisher's measure for discriminant analysis. Six Fourier filter functions introduced for different situations are ideal high-pass filter (IHPF), ideal low-pass filter (ILPF), Butterworth high-pass filter (BHPF), Butterworth low-pass filter (BLPF), Gauss high-pass filter (GHPF) and Gauss low-pass filter (GLPF). The effectiveness of this approach is demonstrated in a case study using a geochemical and aeromagnetic dataset from southwestern Nova Scotia, Canada. The anomalies resulting from ILPF and IHPF show strong association with the occurrences of gold deposits in this area. The high-pass cutoff wavelength correctly predicts regular spacing between mineralization zones. Pros and cons of the parameters for optimal filtering are discussed, and preliminary guidelines are proposed for how to choose these parameters. Additionally, a test of the case study results shows the method is robust to lack of deposits.