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Sahoo, N.; Pandalai, H. S.

doi:10.1023/a:1021698115192

Cited by 20 publications

(2 citation statements)

References 29 publications

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“…The final p value of a mineral occurrence is assigned as either 1 or 0 based on the 0.5 cut-off value [63]. The logistic regression models developed for each mineral were evaluated by -2 log-likelihood(-2LL) tests and Hosmer and Lemeshow tests.…”

Section: Multi-variate Logistic Regressionmentioning

confidence: 99%

Detection of Magnesite and Associated Gangue Minerals using Hyperspectral Remote Sensing—A Laboratory Approach

Chung

Wang

et al. 2020

Remote Sensing

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This study introduced a detection method for magnesite and associated gangue minerals, including dolomite, calcite, and talc, based on mineralogical, chemical, and hyperspectral analyses using hand samples from thirteen different source locations and Specim hyperspectral short wave infrared (SWIR) hyperspectral images. Band ratio methods and logistic regression models were developed based on the spectral bands selected by the random forest algorithm. The mineralogical analysis revealed the heterogeneity of mineral composition for naturally occurring samples, showing various carbonate and silicate minerals as accessory minerals. The Mg and Ca composition of magnesite and dolomite varied significantly, inferring the mixture of minerals. The spectral characteristics of magnesite and associated gangue minerals showed major absorption features of the target minerals mixed with the absorption features of accessory carbonate minerals and talc affected by mineral composition. The spectral characteristics of magnesite and dolomite showed a systematic shift of the Mg-OH absorption features toward a shorter wavelength with an increased Mg content. The spectral bands identified by the random forest algorithm for detecting magnesite and gangue minerals were mainly associated with spectral features manifested by Mg-OH, CO3, and OH. A two-step band ratio classification method achieved an overall accuracy of 92% and 55.2%. The classification models developed by logistic regression models showed a significantly higher accuracy of 98~99.9% for training samples and 82–99.8% for validation samples. Because the samples were collected from heterogeneous sites all over the world, we believe that the results and the approach to band selection and logistic regression developed in this study can be generalized to other case studies of magnesite exploration.

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Section: Multi-variate Logistic Regressionmentioning

confidence: 99%

Detection of Magnesite and Associated Gangue Minerals using Hyperspectral Remote Sensing—A Laboratory Approach

Chung

Wang

et al. 2020

Remote Sensing

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“…In recent years, there have been growing literatures on the application of weights-of-evidence model for mineral exploration (Carranza, 2004;Daneshfar et al, 2006), and the model has also been applied in other fields such as animal habitats (Romero and Luque, 2006), geologic hazards (Zahiri et al, 2006;Neuhäuser and Terhorst, 2007;Song et al, 2008), groundwater resources (Cheng, 2004;Corsini et al, 2009) and hydrology pollution (Masetti et al, 2007). In the last few years, some other spatial statistical models also have been used in mineral resources assessment, such as logistic regression model (Agterberg et al, 1993;Sahoo and Pandala, 1999;Carranza and Hale, 2001), fuzzy logic model (Luo and Dimitrakopoulos, 2003), artificial neural networks model (Koike et al, 2002;Rigol-Sanchez et al, 2003;Nykänen, 2008). Compared with other models, the weights-of-evidence model has some advantages in assessing mineral resources: 1) the weights are relatively easy to be interpreted, they can be confirmed independently, and the favorable targets can be identified easily from the posterior probability or the sum of weights for visual analysis; 2) using proximity analysis to obtain optimal cut-offs, the method provides better estimates for contrast, studentised contrast and buffer size; and 3) it also can be used to capture suitable fuzzy membership (Cheng and Agterberg, 1998;Porwal et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Gold resources potential assessment in eastern Kunlun Mountains of China combining weights-of-evidence model with GIS spatial analysis technique

Chen

Liu

2010

Chin. Geogr. Sci.

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Resources potential assessment is one of the fields in geosciences, which is able to take great advantage of GIS technology as a substitution of traditional working methods. The gold resources potential in the eastern Kunlun Mountains, Qinghai Province, China was assessed by combining weights-of-evidence model with GIS spatial analysis technique. All the data sets used in this paper were derived from an established multi-source geological spatial database, which contains geological, geophysical, geochemical and remote sensing data. Three multi-class variables, i.e., structural intersection, Indosinian k-feldspar granite and regional fault, were used in proximity analysis to examine their spatial association with known gold deposits. A prospectivity map was produced by weightsof-evidence model based on seven binary evidential maps, all of which had passed a conditional independence test. The study area was divided into three target zones of high potential, moderate potential and low potential areas, among which high potential areas and moderate potential areas accounted for 20% of the total area and contained 32 of the 43 gold deposits. The results show that the gold resources potential assessment in the eastern Kunlun Mountains has a higher precision.

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2014

Making Sense of Data I

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Untitled

Cited by 20 publications

References 29 publications

Detection of Magnesite and Associated Gangue Minerals using Hyperspectral Remote Sensing—A Laboratory Approach

Detection of Magnesite and Associated Gangue Minerals using Hyperspectral Remote Sensing—A Laboratory Approach

Gold resources potential assessment in eastern Kunlun Mountains of China combining weights-of-evidence model with GIS spatial analysis technique

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