Quantitative inversion modeling of surface gold abundance based on remote sensing imagery and geochemical Data
点击次数：
影响因子：
4.1
所属单位：
中南大学地球科学与信息物理学院
教研室：
GIS
发表刊物：
Physics and Chemistry of the Earth
刊物所在地：
国外
关键字：
Quantitative remote sensing
Mauritania
Remote sensing geochemistry
Inversion
Backpropagation neural network
摘要：
The Tasiast-Tijirit Terrane in northwestern Mauritania is an important gold mining district that mainly consists of
igneous and metamorphic units that are thought to represent the remnants of older greenstone belts. Surface
outcrops typically contain a high concentration of economically valuable elements. This study focuses on the
quantitative inversion of auriferous soil and rock samples based on remote sensing data, highlighting the significance
of using surface geochemical samples to delineate anomaly areas of gold mineralization in desert regions
for effective mineral exploration programs. The backpropagation neural network inversion model was used
in this work to quantitatively invert the soil and rock samples with spectral band reflectance of Landsat-7 ETM+
and GF-2 satellite imagery at 1:50000 and 1:5000 scale, respectively. Landsat-7 ETM+ was chosen because its
spectral bands are almost identical to the GF-2 remote sensing data, allowing for a reasonable correlation between
the datasets. Results indicate that the established model achieved R2 values of modeling and test sets are
0.65 and 0.63, 0.52 and 0.49 with RMSE values of 0.009 and 0.014, 0.034 and 0.055 for soil and rock samples,
respectively, using Landsat-7 ETM+. Similarly, GF-2 imagery R2 values of modeling and test sets are 0.73 and
0.69, 0.60 and 0.57, with RMSE values of 0.005 and 0.004, 0.015 and 0.023 for soil and rock samples,
respectively. The inversion modeling and predicted anomaly areas are well aligned with the geochemical
exploration map and actual mining area. The findings suggest that although Landsat-7 imagery provides an
overall distribution of surface gold elements, it is restricted in its ability to delineate high gold-rich zones in
desert regions due to relatively coarse resolution besides the geological and environmental conditions such as
wind erosion and weathering effects. Conversely, GF-2 imagery enabled precise delineation of the anomaly locations
with rock samples, proving to be more effective owing to its higher resolution scale of 1:5000. Overall,
the adopted innovative methodology that implements high-resolution satellite data with the bakpropagation
neural network model promise to be very effective in enhacing minerals prediction accuracy and lowering the
exploration costs.
合写作者：
Xiaoqing Deng
第一作者：
Gong Cheng
论文类型：
期刊论文
通讯作者：
Asad Atta
论文编号：
103991
学科门类：
工学
一级学科：
地质资源与地质工程
文献类型：
J
卷号：
140
是否译文：
否
收录刊物：
SCI
附件：
学位：博士学位
学科：地质资源与地质工程. 地质学. 测绘科学与技术
所在单位：地球科学与信息物理学院