Application of modified wavelet and fractal modeling for detection of geochemical anomaly

Abstract

Aim of this research is to propose a wavelet-fractal model for detection of geochemical anomaly based on a modified Morlet Wavelet Kernel (MMWK). This study was carried out for detection of lithium anomalies regarding stream sediment samples. Major Li anomalies commence from 51.1 ppm obtained by the wavelet-fractal modeling based on Decomposed Wavelet Transformation (DWT) using a conventional Morlet wavelet. A modified kernel for the Morlet wavelet was utilized for separation of the Li anomalies. The MMWK benefits from the use of L1 norm (instead of L2) which consequently provides more robustness and resistance to outliers for stream sediment data. Results derived via the modified Morlet kernel-fractal modeling show that there are principal lithium anomalies with Li ≥ 55.2 ppm. In addition, staged factor analysis was used and Li was placed with Rb, Cs and F in F2-2. Then, the F2-2 main anomalies commence from 2.7 and 2.9 using the conventional Morlet and MMWK, respectively. Subsequently, both methods were compared by a log-ratio matrix with an Overall Accuracy (OA) due to rock samples retaken from these anomalies. These rock samples were classified using a Concentration-Number (C-N) fractal model and high intensive anomalous samples with Li ≥ 126 ppm. This correlation indicated that the OA between the main obtained anomalies by the MMWK and the rock samples are higher than conventional Morlet-fractal modeling.