Coupling Relationship Analysis of Gold Content Using Gaofen-5 (GF-5) Satellite Hyperspectral Remote Sensing Data: A Potential Method in Chahuazhai Gold Mining Area, Qiubei County, SW China

Abstract

The gold (Au) geochemical anomaly is an important indicator of gold mineralization. While the traditional field geochemical exploration method is time-consuming and expensive, the hyperspectral remote sensing technique serves as a robust technique for the delineation and mapping of hydrothermally altered and weathered mineral deposits. Nonetheless, mineralization element anomaly detection was still seldomly used in previous hyperspectral remote sensing applications in mineralization. This study explored the coupling relationship between Gaofen-5 (GF-5) hyperspectral data and Au geochemical anomalies through several models. The Au geochemical anomalies in the Chahuazhai mining area, Qiubei County, Yunnan Province, SW China, was studied in detail. First, several noise reduction methods including radiometric calibration, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Savitzky–Golay filter, and endmember choosing methods including Minimum Noise Fraction (MNF) transformation, matched filtering, and Fast Fourier Transform (FFT) transformation were applied to the Gaofen-5 (GF-5) hyperspectral data processing. The Spectrum-Area (S-A) method was introduced to build an FFT filter to highlight the spectral abnormal characteristics associated with Au geochemical anomaly information. Specifically, the Matched Filtering (MF) technique was applied to the dataset to find the Au geochemical anomaly abundances of endmembers with innovative large-sample learning. Then, Multiple Linear Regression (MLR), Partial Least Squares (PLS) regression, a Back Propagation (BP) network, and Geographically Weighted Regression (GWR) were used to reveal the coupling relationship between the spectra of the processed hyperspectral data and the Au geochemical anomalies. The results show that the GWR analysis has a much higher coefficient of determination, which implies that the Au geochemical anomalies and the spectral information are highly related to spatial locations. GWR works especially well for showing the regional Au geochemical anomaly trend and simulating the Au concentrated areas. The GWR model with application of the S-A method is applicable to the detection of Au geochemical anomalies, which could provide a potential method for Au deposit exploration using GF-5 hyperspectral data.

Space technologies of earth remote sensing in applied problems and theoretical studies in mining

“…On different continents, including the Arctic region, remote sensing helps detect promising areas bearing ore containing zinc, lead, gold, copper, tin, tungsten, etc. [14][15][16][17][18][19][20][21][22][23].…”

“…Searching of ancient underground mines [14][15][16][17][18][19][20][21][22][23][24][25] Prediction and estimation of deformations of slopes and walls in surface and underground mines, at overburden dumps and at tailings dams [26][27][28] Detection of surface and underground fires in mines [29][30][31][32],…”

Research on Remote Sensing Geochemical Modeling Based on Surface to Surface Model

“…Although the traditional geochemical exploration method has high accuracy due to its high cost and long cycle, it is difficult to meet the requirements of current generation production. With the continuous development of remote sensing technology, the spatial resolution and spectral resolution of remote sensing images are improved, which makes it possible to analyze geochemical anomaly characteristics by using the remote sensing method [1][2]. Researchers have used laboratory spectra to effectively invert the contents of N, P, K and other elements and heavy metal elements in soil [3][4][5] and others have used ground reflection spectra for geochemical prospecting [6][7][8].…”