Decomposition and reconstruction of ore-caused anomalies information by airborne gamma-ray spectrum based on fractal singular value decomposition method

Abstract

Identification method research on ore-caused anomalies information is one of the core technologies of airborne gamma-ray spectrum data post-processing. This paper proposed a decomposition and reconstruction method of anomaly information called fractal singular value decomposition (FSVD), aiming at the current ubiquitous truncation error and boundary effect problems in filter design in ore-caused anomaly information identification methods in airborne gamma-ray spectrum. Based on the multi-fractal theory, this method decomposes the singular value of the specific nucleus activity data of the airborne gamma-ray spectrum under time series, and quantitatively calculates the fractal singular value truncation parameter according to residual square sum minimum principle. According to the calculation result, the original data is partially reconstructed to rebuild the ore-caused anomalies information. This paper used the 1:50000 airborne gamma-ray spectrum trial production flight data of a certain area in Inner Mongolia (the surveying area contains proved metal ore occurrences) to verify the method. The results show that: after the original signals are decomposed by FSVD, the high, medium and low singular values can represent the background, anomaly, and noise signal, respectively. At the same time, the false anomaly signals caused by flight error exist in high singular values. After reconstructing the decomposed signals according to median singular values, background noises can be effectively separated, the ore-caused anomaly in the known ore-caused anomalies can be identified and band-like false anomalies caused by time domain batches can be removed.