Natural Resources Research Publications on Geochemical Anomaly and Mineral Potential Mapping, and Introduction to the Special Issue of Papers in These Fields

Carranza, Emmanuel John M.

doi:10.1007/s11053-017-9348-1

Cited by 50 publications

(17 citation statements)

References 232 publications

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“…Over the past few decades, the term "ANN" has become notable and has been adopted by a wide variety of applications, including atmospheric science, renewable energy systems, civil engineering [46,47], microwave computer-aided design, medicine and treatment, and natural resources research [48][49][50][51][52][53][54]. It is a branch of the AI family inspired by human brain biological neurons.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

et al. 2019

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Fly-rock induced by blasting is an undesirable phenomenon in quarries. It can be dangerous for humans, equipment, and buildings. To minimize its undesirable hazards, we proposed a state-of-the-art technology of fly-rock prediction based on artificial neural network (ANN) models and their robust combination, called EANNs model (ensemble of ANN models); 210 fly-rock events were recorded to develop and test the ANN and EANNs models. Of thi sample, 80% of the whole dataset was assigned to develop the models, the remaining 20% was assigned to confirm the models developed. Accordingly, five ANN models were designed and developed using the training dataset (i.e., 80% of the whole original data) first; then, their predictions on the training dataset were ensembled to generate a new training dataset. Subsequently, another ANN model was developed based on the new set of training data (i.e., EANNs model). Its performance was evaluated through a variety of performance indices, such as MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root-mean-square error), R2 (correlation coefficient), and VAF (variance accounted for). A promising result was found for the proposed EANNs model in predicting blast-induced fly-rock with a MAE = 2.777, MAPE = 0.017, RMSE = 4.346, R2 = 0.986, and VAF = 98.446%. To confirm the performance of the proposed EANNs model, another ANN model with the same structure was developed and tested on the training and testing datasets. The findings also indicated that the proposed EANNs model yielded better performance than those of the ANN model with the same structure.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

et al. 2019

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“…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).…”

Section: Introductionmentioning

confidence: 99%

“…For 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).…”

Section: Introductionmentioning

confidence: 99%

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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“…Mineral exploration aims to identify areas that may include new mineral deposits (Yue et al, .2017). However, finding new mineral deposits is becoming increasingly difficult, with the most obvious already having been identified, and requires sustained research and development of new methods (Carranza 2017.). Mineral prospectivity mapping (MPM) can be a key procedure in mineral exploration; the approach is concerned with quantifying and mapping the likelihood that a mineral may be found by exploration in a study area.…”

Section: Introductionmentioning

confidence: 99%

Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China

Zhang

Zhou

2018

Earth Sci Inform

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Machine Learning technologies have the potential to deliver new nonlinear mineral prospectivity mapping (MPM) models. In this study, Back Propagation (BP) neural network Support Vector Machine (SVM) methods were applied to MPM in the Hatu region of Xinjiang, northwestern China. First, a conceptual model of mineral prospectivity for Au deposits was constructed by analysis of geological background. Evidential layers were selected and transformed into a binary data format. Then, the processes of selecting samples and parameters were described. For the BP model, the parameters of the network were 9-10 − 1; for the SVM model, a radial basis function was selected as the kernel function with best C = 1 and γ = 0.25. MPM models using these parameters were constructed, and threshold values of prediction results were determined by the concentration-area (C-A) method. Finally, prediction results from the BP neural network and SVM model were compared with that of a conventional method that is the weight-of-evidence (W-of-E). The prospectivity efficacy was evaluated by traditional statistical analysis, prediction-area (P-A) plots, and the receiver operating characteristic (ROC) technique. Given the higher intersection position (74% of the known deposits were within 26% of the total area) and the larger AUC values (0.825), the result shows that the model built by the BP neural network algorithm has a relatively better prediction capability for MPM. The BP neural network algorithm applied in MPM can elucidate the next investigative steps in the study area.

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Natural Resources Research Publications on Geochemical Anomaly and Mineral Potential Mapping, and Introduction to the Special Issue of Papers in These Fields

Cited by 50 publications

References 232 publications

Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

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

Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China

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