Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling

Yousefi, Mahyar; Carranza, Emmanuel John M.

doi:10.1016/j.cageo.2015.03.007

Cited by 253 publications

(78 citation statements)

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Section: Evaluation Of Prospectivity Modelsmentioning

confidence: 99%

“…Defining a suitable non-linear transformation into a new space could facilitate interpretation of a pattern for a set of evidential values in MPM compared to their original space (Bishop, 2006;Yousefi et al, 2014;Yousefi and Carranza, 2015b). A logistic transformation has played an important role in many classification algorithms and pattern recognition (Bishop, 2006), such as statistics, neural networks, machine learning, and expert systems (e.g., Micheli-Tzanakou, 1999;Berthold and Hand, 2002;Alpaydm, 2004;Fink, 2007).…”

Section: Fuzzification Functionsmentioning

confidence: 99%

“…A logistic transformation has played an important role in many classification algorithms and pattern recognition (Bishop, 2006), such as statistics, neural networks, machine learning, and expert systems (e.g., Micheli-Tzanakou, 1999;Berthold and Hand, 2002;Alpaydm, 2004;Fink, 2007). Recently, Yousefi et al (2014) and Yousefi and Carranza (2015b) (Carranza and Hale, 2002;Carranza, 2008;Theodoridis and Koutroumbas, 2006;Yousefi et al, 2012Yousefi et al, , 2013Yousefi et al, , 2014Mutele et al, 2017;Nykänen et al, 2017). Unbounded spatial evidence values can properly be transformed into in the [0,1] range using a logistic function (e.g., Bishop, 2006).…”

Section: Fuzzification Functionsmentioning

confidence: 99%

Section: Fuzzification Functionsmentioning

confidence: 99%

“…9b) based on the geometric average prospectivity model. According to Yousefi and Carranza (2015b) and 2.09, respectively while for the fuzzy prospectivity model they are 6.14 and 1.81, respectively.For further evaluation of the generated prospectivity models, we used a modified success-rate ). In addition, if the success-rate curve of a certain prospectivity model appears in higher above the gauge line in comparison with the success-rate curves of other prospectivity models, it represents that the generated target areas have better spatial association with the mineralization.…”

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confidence: 99%

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Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran

Almasi

Yousefi

Carranza

2017

Ore Geology Reviews

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Diverse deposit-types or mineral systems form by diverse geological processes, so translation of knowledge about the controls of mineralization acquired from the 4D geological modeling into 2D spatial predictor maps is a major challenge for prospectivity analysis. In this regard, mathematical functions have been used to model the conceptual or perceived spatial relationships between geological variables and targeted type or system of mineralization. In this paper, due to the different models of spatial relationships between predictors and mineral deposits, we investigated the performance of different fuzzification functions to quantify the relationships. We demonstrated that various types of relationships between exploration features and a mineralization-type sought could be quantified using different fuzzification functions for prospectivity analysis. We illustrated the process of the prospectivity analysis by using a data set of orogenic gold deposits in Saqez-Sardasht Goldfield, Iran. Prospectivity modeling of orogenic gold mineralization in the study area showed that the NE-SW trending targets have priority for further prospecting of the deposits. Saqez-Sardasht Goldfield, Zagros Orogen, Iran (by A. Almasi, M. Yousefi, E. J.M. Carranza) Manuscript submitted to Ore Geology Reviews 2 Prospectivity analysis of orogenic gold deposits in IntroductionFor mineral prospectivity mapping (MPM) of a deposit-type sought in an area, mathematical functions have been used to model spatial relationships between geological variables system of mineralization (e.g., Bonham-Carter, 1994;Luo and Dimitrakopoulos, 2003; Porwal et al., 2003a,b,c;Carranza, 2008Carranza, , 2017. In MPM, mathematical functions, have been widely used to assign weights to discretized spatial evidence values as fuzzified evidential maps in the [0,1] range or to rank target areas as fuzzy prospectivity models (e.g., Bonham-Carter, 1994;Carranza and Hale, 2002;Luo and Dimitrakopoulos, 2003;Porwal et al., 2003c;Carranza, 2008Carranza, , 2009Carranza, , 2017Lisitsin et al., 2013;Mutele et al., 2017;Nykänen et al., 2017). The weights assigned to classes of discretized evidential values may be based on (a) expert judgment directly, (b) locations of known mineral occurrences (KMOs), (c) a combination of (a) and (b), or (d) subjectively-defined functions, so indirectly-assigned by analyst (e.g., Luo, 1990;Bonham-Carter, 1994;Cheng and Agterberg, 1999;Luo and Dimitrakopoulos, 2003;Porwal et al., 2003a Porwal et al., ,b,c, 2004Porwal et al., , 2006Carranza et al., 2005;Carranza, 2008Carranza, , 2014Porwal and Kreuzer, 2010;Mejía-Herrera et al., 2014;Carranza and Laborte, 2016;McKay and Harris, 2016). All these methods impart bias due to discretization of continuous spatial values, use of subjective expert judgments, and sparse or incomplete data on locations of KMOs in knowledge-and data-driven MPM (Coolbaugh et al., 2007;Lusty et al., 2012;Ford et al., 2016).To reduce bias in the assignment of weights to continuous-value spatial evidence, various re...

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Section: Evaluation Of Prospectivity Modelsmentioning

confidence: 99%

Section: Fuzzification Functionsmentioning

confidence: 99%

Section: Fuzzification Functionsmentioning

confidence: 99%

Section: Fuzzification Functionsmentioning

confidence: 99%

mentioning

confidence: 99%

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Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran

Almasi

Yousefi

Carranza

2017

Ore Geology Reviews

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Mineral prospectivity mapping of tungsten polymetallic deposits using machine learning algorithms and comparison of their performance in the Gannan region, China

Liu

Yonghang

2022

Preprint

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The current study aimed at assessing the capabilities of five machine learning models in term of mapping tungsten polymetallic prospectivity in the Gannan region of China. The five models include logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and light gradient boosting machine (LGBM) models. Geochemical, lithostratigraphic, and structural datasets were used to generate 16 evidential maps, which were integrated into the machine learning models. Tungsten polymetallic deposits were randomly separated into two parts: 80% for training and 20% for validating. Performances of the models were evaluated through receiver operating characteristic (ROC) and K-fold cross validation, with an emphasis on the variable influence within different machine learning methods. The results show that the models are especially sensitive to the chemical elements: Be, Bi, Pb and Cd, implying that these are closely related to tungsten polymetallic mineralization. Compared to other models, the LGBM and CNN models performed best, while the LR model was the most stable. The results also indicated that the CNN model can predict maximum known deposits within a minimum area, based on the prediction-area plot analysis of the five models, while the RF model can capture the most well-known deposits within the smallest study area. Finally, eighteen prospective areas were delineated according to the predicting results of the machine learning models, which will provide important guidance for further tungsten polymetallic exploration and associated studies.

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Non-Euclidean distance measures in spatial data decision analysis: investigations for mineral potential mapping

Abedi

2020

Ann Oper Res

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Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling

Cited by 253 publications

References 54 publications

Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran

Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran

Mineral prospectivity mapping of tungsten polymetallic deposits using machine learning algorithms and comparison of their performance in the Gannan region, China

Non-Euclidean distance measures in spatial data decision analysis: investigations for mineral potential mapping

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