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Computers & Geosciences 37 (2011) 1907–1916
Contents lists available at SciVerse ScienceDirect
Computers & Geosciences
0098-30
doi:10.1
journal homepage: www.elsevier.com/locate/cageo
Editorial
Geocomputation of mineral exploration targets
1. Introduction
Mineral exploration endeavors to find ore deposits (i.e., eco-nomically viable concentrations of minerals or metals) for miningpurposes. Delineation of targets for mineral exploration is thecrucial initial stage in a series of mapping activities that mayresult in ore deposit discovery. That is because mineral depositsare ‘freaks’ of nature and, thus, vectoring towards ore depositsbased on multi-source and multi-scale geoscience explorationdata sets is a multi-faceted spatial problem. Therefore, applica-tions of geocomputation, which is ‘‘the art and science of solving
complex spatial problems with computers’’ (Xue et al., 2004), andapplications of geological concepts of ore genesis are essential inmineral exploration target delineation.
The late 1980s through the 1990s saw rapid and far-reachingdevelopments in geocomputational techniques for delineatingexploration targets. During those years, substantial improvementsin the efficiency and availability of computer hardware and software(Agterberg, 1989), including geographic information system (GIS)technology (Bonham-Carter and Agterberg, 1990), took place. As GIStechnology rapidly developed during the last decade of the secondmillennium, two textbooks (Bonham-Carter, 1994; Pan and Harris,2000) and several papers published in exploration-related literaturehave explained and documented various GIS-aided and/or GIS-basedgeocomputational methods for delineating exploration targets.Roughly 50% of published papers about delineating explorationtargets (see reference list) appear in the three journals of theInternational Association of Mathematical Geosciences—Computers& Geosciences (C&G), Mathematical Geosciences (MG), and Nat-ural Resources Research (NRR). The C&G and MG publish novel orinnovative geocomputational techniques for delineating explora-tion targets, whereas NRR publishes mainly case applications ofvarious geocomputational techniques for delineating explorationtargets.
In 2001, C&G published a special issue (number 8, volume 27)on GeoComp 99: GeoComputation and the Geosciences. The 10papers in that special issue describe various geocomputationaltechniques for problem solving in the geosciences (Ehlen andHarmon, 2001). Of those 10 papers, only one relates to delineationof exploration targets (Asadi and Hale, 2001). The present specialissue of the C&G, containing seven papers, aims to documentrecent developments in geocomputation mineral explorationtargets. The following sections provide (a) a brief overview ofexploration target delineation and (b) brief reviews of geocom-putational techniques relevant to delineation of explorationtargets.
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016/j.cageo.2011.11.009
2. Mineral exploration target delineation
Delineation of mineral exploration targets is a multi-stagemapping activity from regional to local scales. Every scale ofexploration target delineation involves collection, analysis andintegration of various thematic geoscience data sets in order toextract spatial geo-information, namely (a) geological, geochemicaland/or geophysical anomalies associated with mineral deposits ofthe type sought and (b) prospective exploration targets, which areareas defined by overlaps of such anomalies. Thus, delineation ofexploration targets intrinsically assumes that (a) a target is char-acterized by evidential features, which are the same as or similar tothose of the known locations of mineral deposits of the type soughtand (b) if more important evidential features are present in onetarget than in another, then the former has higher mineral pro-spectivity than the latter. Therefore, delineation of explorationtargets is equivalent to predictive modelling or mapping of mineralpotential or mineral prospectivity. The term mineral potential refersto the chance or likelihood that mineral deposits of the type soughtare contained in an area, whereas the term mineral prospectivity
refers to the chance or likelihood that mineral deposits of the typesought can be found at every location. The terms mineral potentialand mineral prospectivity are therefore synonymous and can beused interchangeably. However, geocomputational modelling ofexploration targets must pertain to just one type of mineral deposit.Thus, a model of exploration targets for epithermal Au deposits isnot applicable to guide exploration for porphyry Cu deposits, andvice versa. Stensgaard et al. (2006) and Carranza et al. (2008a) haveproposed geocomputational methodologies for selecting spatially-coherent deposit-type locations for application in predictive map-ping of exploration targets.
Geocomputational modelling of exploration targets followsspecific steps starting with the definition of a conceptual model
of exploration targets for mineral deposits of the type sought(Fig. 1). Such a conceptual model is prescriptive rather thanpredictive, as it specifies in words and/or diagrams the theoreticalrelationships between various geologic processes or controls interms of how and especially where mineral deposits of the typesought are likely to occur. Defining a conceptual model ofexploration targets for the deposit-type sought requires knowl-edge of various geological processes relevant to the formation ofthe deposit-type sought. That knowledge allows defining keytargeting elements (or exploration criteria). A conceptual modelof mineral exploration targets and the targeting elements providethe framework for predictive mapping of exploration targets interms of determining suitable (a) geoscience spatial data sets to
Editorial / Computers & Geosciences 37 (2011) 1907–19161908
be used, (b) evidential features to be enhanced and extracted fromeach data set, (c) method(s) of mapping evidential features fromindividual geoscience spatial data sets, (d) method(s) of creatingpredictor maps, and (e) method(s) of integrating predictor mapsto create a predictive model or map of exploration targets. Thepreceding items (b), (c) and (d) constitute the geocomputationalanalysis of predictive model parameters.
Creating predictor maps involves assigning weights to eviden-tial features through either knowledge-driven analysis or data-
Fig. 1. Elements of predictive modeling of mineral exploration targets.
Table 1Models/methods of information/data integration used in geocomputational mapping o
Model/method References to seminal works and recent applications
Knowledge-driven
Boolean logic Bonham-Carter (1994), Thiart and De Wit (2000), Ha
Binary index overlay Bonham-Carter (1994), Carranza et al. (1999), Thiart
Multi-class index overlay Bonham-Carter (1994), Harris et al. (2001b), Chica-O
(2008)
Fuzzy logic An et al. (1991), Gettings and Bultman (1993), Bonh
(2000), Porwal and Sides (2000), Venkataraman et al.
(2003), Ranjbar and Honarmand (2004), De Quadros
(2006), Nykanen and Ojala (2007), Nykanen et al. (2
Evidential belief Moon (1990, 1993), Chung and Moon (1991), Moon
Bonham-Carter (1996), Tangestani and Moore (2002
Bivariate data-driven
Weights-of-evidence
analysis
Bonham-Carter et al. (1988, 1989), Agterberg and Bo
Agterberg (1990, 1999), Bonham-Carter (1991, 1994
Cheng and Agterberg (1999), Raines (1999), Singer a
Venkataraman et al. (2000), Asadi and Hale (2001), M
2006a, 2010a), Scott and Dimitrakopoulos (2001), Ta
Raines and Mihalasky (2002), Harris et al. (2003), Ca
Daneshfar et al. (2006), Harris and Sanborn-Barrie (20
Feltrin (2008), Ford and Blenkinsop (2008a), Oh and L
Evidential belief analysis Chung and Fabbri (1993), An et al. (1994b), Carranza
2011), Carranza and Sadeghi (2010)
Multivariate data-driven
Discriminant analysis Chung (1977), Prelat (1977), Bonham-Carter and Chu
Carranza (2008)
Characteristic analysis Botbol et al. (1977, 1978), McCammon et al. (1983, 1
Logistic regression
analysis
Chung (1978, 1983), Chung and Agterberg (1980, 19
et al. (1993a), Harris and Pan (1991, 1999), Sahoo and
Hale (2001b), Raines and Mihalasky (2002), Harris et a
and Ojala (2007), Carranza (2008), Carranza et al. (2
(2011)
Favourability analysis Pan (1993a,b,c), Pan and Portefield (1995), Pan and H
Likelihood ratio analysis Chung and Fabbri (1993), Chung et al. (2002), Chung
et al. (2006), Oh and Lee (2008)
Artificial neural
networks
Singer and Kouda (1996, 1997, 1999), Harris and Pan
(2003), Harris et al. (2003), Porwal et al. (2003a, 200
Skabar (2005, 2007a,b), Nykanen (2008), Pereira Leit
Bayesian network
classifiers
Porwal et al. (2006b), Porwal and Carranza (2008)
driven geocomputation of their spatial associations with mineraldeposits of the type sought (Bonham-Carter, 1994; Carranza,2008). Knowledge-driven analysis of spatial associations betweenevidential features and mineral deposits is appropriate in frontieror less-explored (or so-called ‘greenfields’) geologically permissive
areas, where no or very few mineral deposits of the type soughtare known to occur. By ‘geologically permissive’, it is meant thatthe present or past geological setting of an area representsprocesses that might have led to formation of certain type(s) ofmineral deposits (Singer, 1993). Data-driven geocomputation ofspatial associations between evidential features and knowndeposit-type locations is appropriate in areas representing mod-erately- to well-sampled (or so-called ‘brownfields’) mineralized
landscapes in geologically permissive areas, where the objective isto delineate new targets for further exploration of undiscovereddeposits. Methods for data-driven geocomputation of spatialassociations between known deposit-type locations and spatialevidence can be either bivariate or multivariate (Table 1).
Every knowledge-driven method of creating predictor maps(Table 1) can be used directly in integrating such predictor maps.However, only the methods based on Boolean logic, fuzzy logic,and evidential belief allow representation of knowledge ofmineral systems or inter-play of geologic controls of mineraliza-tion (by using a so-called inference engine) in integrating predictor
maps to obtain a predictive map of exploration targets. Among thedata-driven methods of creating predictor maps (Table 1), onlythe multivariate methods lead automatically to integration of
f mineral exploration targets.
rris et al. (2001b)
and De Wit (2000)
lmo et al. (2002), De Araujo and Macedo (2002), Billa et al. (2004), Harris et al.
am-Carter (1994), D’Ercole et al. (2000), Groves et al. (2000), Knox-Robinson
(2000), Carranza and Hale (2001a), Porwal et al. (2003b), Tangestani and Moore
et al. (2006), Eddy et al. (2006), Harris and Sanborn-Barrie (2006), Rogge et al.
008a,b), Gonzalez-Alvarez et al. (2010)
et al. (1991), An et al. (1992, 1994a,b), Chung and Fabbri (1993), Wright and
), Chen (2004), Rogge et al. (2006)
nham-Carter (1990, 2005), Agterberg et al. (1990, 1993a), Bonham-Carter and
), Agterberg (1992, 2011), Xu et al. (1992), Pan (1996), Rostirolla et al. (1998),
nd Kouda (1999), Carranza and Hale (2000, 2002b,c), Pan and Harris (2000),
ihalasky and Bonham-Carter (2001), Harris et al. (2001b), Porwal et al. (2001,
ngestani and Moore (2001), Agterberg and Cheng (2002), Paganelli et al. (2002),
rranza (2004, 2009b), Chen (2004), Chen et al. (2005), De Quadros et al. (2006),
06), Nykanen and Raines (2006), Nykanen and Ojala (2007), Raines et al. (2007),
ee (2008), Austin and Blenkinsop (2009), Debba et al. (2009), Fallon et al. (2010)
and Hale (2003), Carranza et al. (2005, 2008a,b,c, 2009), Carranza (2008, 2009a,
ng (1983), Harris and Pan (1999), Pan and Harris (2000), Harris et al. (2003),
984), Harris (1984), Pan and Harris (1992a, 2000)
88), Bonham-Carter and Chung (1983), Agterberg (1988, 1992, 1993), Agterberg
Pandalai (1999), Pan and Harris (2000), Harris et al. (2001b, 2006), Carranza and
l. (2003), Agterberg and Bonham-Carter (2005), Daneshfar et al. (2006), Nykanen
008a), Oh and Lee (2008), Fallon et al. (2010), Porwal et al. (2010a), Chen et al.
arris (1992b, 2000), Rostirolla et al. (1998), Chen (2004)
and Keating (2002), Chung (2003), Harris and Sanborn-Barrie (2006), Stensgaard
(1999), Pan and Harris (2000), Brown et al. (2000, 2003a,b), Bougrain et al.
4), Rigol-Sanchez et al. (2003), Harris and Sanborn-Barrie (2006), Behnia (2007),
e and De Souza Filho (2009a,b), Oh and Lee (2010)
Editorial / Computers & Geosciences 37 (2011) 1907–1916 1909
predictor maps. Hence, bivariate methods allow the analyst todecide which predictor maps can be integrated (or removed)according to either the mathematical assumptions of a method(e.g., conditional independence among predictor maps) or theinter-play of geologic controls of mineralization (i.e., by applica-tion of an inference engine). Among the data-driven methods,only evidential belief modelling allows representation of knowl-edge of mineral systems or inter-play of geologic controls ofmineralization in integrating predictor maps (Carranza, 2008;Carranza et al., 2008b). However, hybrids methods, such as fuzzyweights-of-evidence modelling (Cheng and Agterberg, 1999),data-driven fuzzy modelling (Luo and Dimitrakopoulos, 2003;Porwal et al., 2003b; Zuo et al., 2009b) and neuro-fuzzy modelling(Porwal et al., 2004) have been proposed to optimize utilization ofboth conceptual knowledge of mineral systems and empiricalspatial associations between mineral deposits and evidentialfeatures in geocomputational modelling of exploration targets.
Therefore, every method for creating and/or integrating pre-dictor maps has inherent systemic (or procedural) errors withrespect to interactions of geological processes involved in mineraldeposit formation. In addition, every input geoscience spatial dataset or mapped evidential features used in geocomputationalmodelling of exploration targets invariably contains parametric
(or data-related) errors with respect to distribution of discoveredand undiscovered mineral deposits. These errors are inevitable inpredictive modelling. However, because such errors are finallycompounded into the predictive map of exploration targets, it isimperative to apply measures for model calibration and, if possi-ble, to reduce errors through model validation in every step of thegeocomputational analysis. Model calibration and model valida-tion aim, of course, at deriving the best possible predictive map ofexploration targets because its purpose is to provide opportunityfor the discovery of undiscovered ore deposits of the type sought.Fabbri and Chung (2008) provide us with a perspective on how toblind-test, interpret, and compare predictive maps of explorationtargets.
3. Geocomputational tools for understanding mineralsystems
Traditionally, it was important in conceptual modelling ofexploration targets to apply mineral deposit models (e.g., Coxand Singer, 1986; Roberts et al., 1988), which describe the specificgeological characteristics of certain types of mineral deposits andtheir regional geological environments. However, adoption of amineral system approach to delineation of exploration targets(Wyborn et al., 1994; McCuaig et al., 2010) is nowadays advo-cated because of the recognition that mineral deposits are focalpoints of much larger systems of energy and mass flux (Hronskyand Groves, 2008). In contrast to the deposit model approach,which relies mainly on using specific geological features asevidence of mineral prospectivity, the mineral system approachto delineation of exploration targets relies on a 5-questionparadigm (Price and Stoker, 2002; Walshe et al., 2005). The fivequestions, which relate to processes of geologic controls onmineralization, are: (i) What is the architecture and size of thesystem?; (ii) What is the P-T and the geodynamic history of thesystem?; (iii) What is the nature of the fluids and fluid reservoirsin the system?; (iv) What is the nature of fluid pathways andprocesses driving fluid flow?; and (v) What is the chemistry ofmetal transport and deposition in space as well as time? Czarnotaet al. (2010) recently described an application of the mineralsystem approach to define district-scale targets for orogenic goldin the eastern Yilgarn Craton in Western Australia. Using suitablespatial data, they interpreted and mapped expressions (or spatial
evidence or proxies) of processes depicted by each of the fivequestions. Predictor maps, representing mappable mineral systemprocess proxies (or targeting elements), were then created (byassigning weights to spatial evidence or proxies) and integratedthrough a multi-class index overlay knowledge-driven approach.
Process understanding of a mineral system is the key toidentification of targeting elements (Czarnota et al., 2010). Suchunderstanding can be either conceptual or empirical (Porwal andKreuzer, 2010). Fuzzy logic methods have been used to representconceptual process understanding of mineral systems (D’Ercoleet al., 2000; Groves et al., 2000; Knox-Robinson, 2000). However,Porwal and Kreuzer (2010) argue that predictive modelling ofexploration targets can only produce useful results if the spatialdata sets used allow modelling of the critical geological processesand elements controlling mineralization. Inversion of geophysicaldata, which involve various geocomputing algorithms dependingon type of data, can provide insights into the architecturalelements and physical and chemical properties of a mineralsystem (Blewett et al., 2010). Many recent developments ingeocomputational techniques for geophysical data inversionpublished in the C&G (e.g., Agarwal and Srivastava, 2009; Leeal., 2009; Ozgu Arısoy and Dikmen, 2011) are potentially usefulfor improving our understanding of mineral systems based ongeophysical data.
Analyses of the spatial pattern of known mineral depositoccurrences and their spatial association with certain geologicalfeatures provide empirical spatial geo-information that poten-tially improves our conceptual understanding of mineral systems(Carranza, 2008). Fry analysis, which is a graphical method ofspatial autocorrelation analysis, has been used to infer structuralcontrols on occurrence of certain types of mineral deposits(Vearncombe and Vearncombe, 1999, 2002; Stubley, 2004;Mondlane et al., 2006; Kreuzer et al., 2007; Carranza, 2008;Austin and Blenkinsop, 2009; Carranza et al., 2009; Zuo et al.,2009a) and geothermal fields (Carranza et al., 2008c). Fractalanalysis of the spatial distributions of certain types of mineraldeposits with the aim to infer geologic controls on mineralization,has been demonstrated by Carlson (1991); Blenkinsop (1994);Cheng and Agterberg (1995); Cheng et al. (1996b); Shen and Zhao(2002); Weiberg et al. (2004); Hodkiewicz et al. (2005); Carranza(2008); Ford and Blenkinsop (2008a,b); Raines (2008); Carranzaet al. (2009); Zuo et al. (2009a) and Gumiel et al. (2010). In thisspecial issue, Arias et al. (this issue) sought to establish anddevelop more sophisticated simulation models that incorporateboth multifractal concepts and geologic controls on mineraliza-tion to derive spatial constraints for predictive mapping ofexploration targets.
However, as mineral deposits were formed from fluids, geo-computational modelling of fluid flow has traditionally beenimportant in understanding mineral deposit formation (e.g.,Garven and Freeze, 1984; Jiang et al., 1997; Appold and Garven,1999; Hobbs et al., 2000; Driesner and Geiger, 2007; Zhang et al.,2011). Recent studies of fluid flow modelling for understandingmineral deposit formation, which are published in C&G, includethe following. Sheldon (2009) used a Lagrangian finite differencecode – FLAC3D – to simulate deformation, fluid flow, and heattransport in porous media for obtaining insights into mineraliza-tion. Feltrin et al. (2009) also used FLAC3D and found that, in theCentury deposit (Queensland, Australia), subsurface fluid flowwithin more permeable fault zones accounts for secondary redis-tribution of base metals but not for the observed zonation ofmetals in that deposit. Xu et al. (2010) discussed the applicationof cellular nonlinear networks to simulate mineral zonation. Inthis special issue, Xu et al. (this issue) further discuss dynamicsimulation using cellular nonlinear networks to mimic hydro-thermal deposit-forming geochemical processes. Their simulation
Editorial / Computers & Geosciences 37 (2011) 1907–19161910
studies resulted in spatial patterns of geochemical components,which provide insights into hydrothermal deposit-formingsystems and spatial patterns of expressions of the processesinvolved.
Fig. 2. Histogram of publication years of papers (n¼140) listed in Table 1.
4. Geocomputational modelling of mineral explorationtarget evidence
Mapping (i.e., identifying, enhancing and extracting) of spatialfeatures representing mineralization controls from appropriatespatial (geological, geochemical and geophysical) data is anobjective of geocomputational analysis of predictive model para-meters (Fig. 1). Methods of enhancing and extracting spatialevidence of mineral exploration targets are specific to evidentialthemes (i.e., geochemical, geological and geophysical) and typesof geoscience spatial data. Various concepts of mapping signifi-cant geochemical anomalies (i.e., due to mineral deposits) arenow mostly well-established (Cohen et al., 2010; Grunsky, 2010)and various GIS-aided and/or GIS-based geocomputational meth-ods are well-documented in the literature (e.g., Carranza, 2008).However, during the last two decades, methods for fractal/multi-fractal modelling of significant geochemical anomalies have beendeveloped to consider not only the statistical distributions butalso the spatial distributions of geochemical data (Cheng et al.,1994, 1996a, 2000; Cheng and Agterberg, 1995, 2009; Cheng,1999; 2007; Xu and Cheng, 2001; Carranza, 2008; Bai et al., 2010;Cheng and Zhao, 2011; Zuo, 2011). Geocomputational methodsfor mapping of hydrothermal alterations as evidence of mineraldeposit occurrence, involve application of remotely-sensed spec-tral data (e.g., Sabins, 1999; Van Ruitenbeek et al., 2008; Laukampet al., 2011) as well as gamma-ray data (e.g., De Quadros et al.,2003; Cheng, 2006; Ranjbar et al., 2011). However, remotesensing of hydrothermal alteration for mineral exploration invegetated terrains is still problematic, although relative successcan be achieved by integration of other suitable spatial data orapplication of spatial masking techniques (Carranza and Hale,2002a; Liu et al., 2011; Vicente and De Souza Filho, 2011). Remotesensing using spectral and/or gamma-ray data sets can assistmapping of geological features such as faults and intrusive rocksrepresenting, respectively, structural and heat-source controls onmineralization. However, detecting the presence of such geologi-cal features in the subsurface is possible through geocomputa-tional analysis of geophysical data sets (e.g., Cooper and Cowan,2004; Holden et al., 2008; Agarwal and Srivastava, 2010).
In this special issue, three papers describe geocomputationalanalysis of different types of spatial evidence of mineral prospec-tivity. Ziaii et al. (this issue) discuss the application of geochem-ical zonality coefficients, which allow distinction betweensub- and supra-ore anomalies. These anomalies relate to question(v) of the 5-question paradigm of the mineral system approach topredictive mapping of exploration targets. By using these anoma-lies, instead of anomalies of single pathfinder elements, bettermaps of mineral prospectivity can be derived. Wang et al. (thisissue-b) discuss the application of a high-pass filtering technique,based on the multifractal concept called ‘singularity’, to extractand integrate spatial geo-information from geochemical andgeophysical data sets for detection and delineation of outcroppingas well as buried granitic intrusions, which are associated geneti-cally and spatially with mineral deposits. Mapping of outcroppingand buried igneous intrusions relates question (ii) of the 5-ques-tion paradigm of the mineral system approach to predictivemapping of exploration targets. Zhao et al. (this issue) demon-strate the application of fractal and multifractal analyses to createa map of complexity in the spatial distribution of faults, whichcan be used as a layer of spatial evidence of mineral prospectivity.
A map of complexity in the spatial distribution of faults relates toquestion (iv) of the 5-question paradigm of the mineral systemapproach to predictive mapping of exploration targets. Thus, thegeocomputational techniques for mapping of mineral prospectiv-ity evidence described by Ziaii et al. (this issue), Wang et al.(this issue-b) and Zhao et al. (this issue) complement the mineralsystem approach to predictive mapping of exploration targets.
5. Geocomputational tools for integrating mineralexploration target evidence
Harris (1969), Sinclair and Woodsworth (1970) and Agterberg(1971) pioneered the data-driven geocomputational modelling ofexploration targets through integration of evidential data byapplication of probabilistic statistical methods. About a decadelater, Duda et al. (1978) and Campbell et al. (1982) proposedartificial intelligence (i.e., knowledge-driven) techniques forassessment and representation of evidential data to locateexploration targets. The period of development of those artificialintelligence techniques was roughly contemporaneous withthe period of development of data-driven multivariate statisticaltechniques (e.g., discriminant analysis, characteristic analysis, andlogistic regression) (Table 1). However, the proposed artificialintelligence techniques did not gain more popularity thanthe data-driven statistical methods, probably because the formerwere subjective and irreproducible compared to the latter. Inaddition, the substantial improvements in efficiency and avail-ability of computer hardware and software in the mid- to late-1980s, including the advent of commercial GIS in that period,likely favored the application of statistical (or data-driven) meth-ods rather than artificial intelligence (or knowledge-driven)techniques. The developments in GIS technology inspired severalresearchers in the late-1980s to early-1990s, including Agterberg,Bonham-Carter, and colleagues, to develop and publish methodsfor GIS-based mapping of exploration targets. The peak of thehistogram for papers published in 1992–1994 (Fig. 2) includesBonham-Carter’s (1994) book, which documents several of hisand his colleagues’ GIS-based works during the preceding decade.However, due to the unaffordability of computer hardware andsoftware or to the absence of spatial geological databases duringthat period, there seemed to be a respite in the development ofGIS-based methods during the mid-1990s (Fig. 3). Nevertheless,the development picked-up again during the first decade of thesecond millennium.
Fig. 3. Stacked histogram of annual numbers of papers about data-driven
methods for modelling of mineral exploration targets (based on list in Table 1).
WOFE¼weights-of-evidence, EBF¼evidential belief, DA¼discriminant analysis,
CA¼characteristic analysis, LORE¼ logistic regression, FA¼favourability analysis,
LIRA¼ likelihood ratio, ANN¼artificial neural networks, BNC¼Bayesian network
classifiers.
Editorial / Computers & Geosciences 37 (2011) 1907–1916 1911
Although the list in Table 1 is not complete, it seems that themost popular GIS-based methods for representing and integratingspatial evidence to map exploration targets are fuzzy logic,weights-of-evidence, logistic regression and artificial neural net-works. This is likely due to the freely-available ArcSDM geocom-puting tools that implement those four methods in ArcView(Kemp et al., 2001) and in ArcGIS (Sawatzky et al., 2004, 2007,2009). The popularity of fuzzy logic and weights-of-evidence was,thus, enhanced by developments in GIS software and by Bonham-Carter’s (1994) book, in which both methods are extensivelydemonstrated. The weights-of-evidence became a popular toolsince the late 1980s because it can easily be implemented in a GIScomputing platform and, even before the ArcSDM geocomputingtools were developed, a user-friendly computer code – Arc-WofE– was developed as an ‘extension’ to the erstwhile popularArcView GIS package (Bonham-Carter and Agterberg, 1999). Incontrast, logistic regression has been used even before the rapiddevelopments in GIS technology in the late 1980s (Table 1, Fig. 3).The most likely reason is that logistic regression is the mostappropriate technique for modelling the relationship between adichotomous variable (e.g., presence/absence of mineral deposits)and several independent variables that do not have to benormally distributed, linearly related or homoscedastic. In addi-tion, the main advantage of logistic regression over weights-of-evidence is that, unlike the latter, the former is not subject toproblems associated with lack of conditional independenceamong predictor maps. Since the late 1990s, artificial neuralnetworks have also become popular for predictive mapping ofexploration targets because they are not affected by the lack ofconditional independence among predictor maps.
The performances of the most popular data-driven methodsand some of the other methods in Table 1 (e.g., evidential belief,discriminant analysis, favorability analysis) are roughly similar(cf. Agterberg et al., 1993a; Wright and Bonham-Carter, 1996;Rostirolla et al., 1998; Raines and Mihalasky, 2002; Chen, 2004;Agterberg and Bonham-Carter, 2005; Nykanen and Ojala, 2007;Porwal et al., 2010a). However, artificial neural networks usually
outperform discriminant analysis, logistic regression, andweights-of-evidence (Harris and Pan, 1999; Singer and Kouda,1999; Harris et al., 2003). However, artificial neural networks, likeall the other multivariate data-driven methods (Table 1), cannotcope with missing evidential data. In contrast, the two bivariatedata-driven methods – weights-of-evidence and evidentialbelief – allow representation of missing evidential data(Carranza, 2008; Agterberg, 2011). In addition, artificial neuralnetworks, like all the other data-driven methods, require a largenumber of known occurrences of mineral deposits of the typesought for training and creating predictive models of explorationtargets. More importantly, unlike the parameters of bivariatedata-driven methods and the other multivariate data-drivenmethods (Table 1), the parameters of artificial neural networksare not interpretable in terms of relative importance of predictormaps. In other words, artificial neural networks do not supple-ment the mineral system approach to delineation of explorationtargets with insights into the inter-play of geologic controls ofmineralization. However, recent applications of genetic program-ming (Lewkowski et al., 2010) and support vector machines(Porwal et al., 2010b) show that machine learning algorithmscan help to overcome the ‘black-box’ limitations of artificialneural networks. In this special issue, Zuo and Carranza (thisissue) describe an application of support vector machines todelineate exploration targets. The highlight of this paper is theset of experiments for choosing the optimum kernel function thatefficiently classifies the training and testing deposit and non-deposit locations. Thus, with a properly selected kernel function,Zuo and Carranza (this issue) show the usefulness of supportvector machines as a new geocomputational tool for predictivemapping of exploration targets.
Predictive models of mineral exploration targets are typicallytwo-dimensional. Such models are usually adequate for small-scale mapping, but as mineral exploration progresses to large-scale mapping, three-dimensional predictive modelling ofexploration targets becomes desirable. In the last five years,significant developments have been made in three-dimensionalmodelling of mineral exploration targets (e.g., Apel, 2006;Caumon et al., 2006; Rawling et al., 2006; Sprague et al., 2006;Bohme and Apel, 2008; Feltrin et al., 2008a; McGaughey et al.,2009; Jackson, 2010; De Kemp et al., 2011). In addition, Feltrinet al. (2003, 2008b) have shown how three-dimensional model-ling can be used to define targeting elements at district- todeposit-scales. However, three-dimensional modelling of mineralexploration targets is still uncommon due to (a) the lack of, ordifficult access to, three-dimensional data at various scales and(b) the lack of freely-available three-dimensional geocomputingsoftware. Three-dimensional mineral exploration data are avail-able mainly on deposit- to mine-scales, whereas existing softwarepackages for three-dimensional modelling of geoscience data aregenerally unaffordable. Nevertheless, in this special issue, Wanget al. (this issue-a) present a case study using three-dimensionalgeological modelling and nonlinear geocomputational methods(fractal, multifractal and probabilistic neural networks) to modeland integrate mineral exploration target evidence in order todelineate three-dimensional regional-scale exploration targets.
6. Organization of the special issue and future outlook
The papers in this special issue can be classified into threegroups, which are arranged in the same order as the precedingthree sections.
The first two papers by Arias et al. and Xue et al. are concernedwith the development and application of geocomputational toolsthat allow deriving insights into mineral systems and identifying
Editorial / Computers & Geosciences 37 (2011) 1907–19161912
targeting elements. Because the mineral system approach todelineation of exploration targets is a relatively new paradigmand because mineral systems represent complex interactionsof various geological processes, further testing of the methodsproposed by either Arias et al. or Xue et al. as well as furtherdevelopment of novel or innovative geocomputational tools forunderstanding of mineral systems are expected in the near future.A specific challenge is the development of geocomputational toolsthat would allow distinction between mineral systems thatproduce large mineral deposits and mineral systems that producesmall mineral deposits (cf. Porwal and Kreuzer, 2010).
The next three papers by Ziaii et al., Wang et al. and Zhao et al.are concerned with the development and application of certaingeocomputational tools for deriving spatial evidence of mineralexploration targets from various types of geo-exploration datasets. These papers exemplify the persistent endeavor in mineralexploration to develop robust geocomputational methods foranalysis and mapping of spatial evidence of mineral depositoccurrence, especially in the subsurface or in greenfields. Geo-computational tools that allow distinction between spatial evi-dence of large mineral deposits and spatial evidence of smallmineral deposits would be desirable in the future. Developmentof such tools would benefit from improved understanding ofmineral systems.
The last two papers by Zuo and Carranza and by Wang et al.are concerned with the development and application, respec-tively, of geocomputation tools for representation and integrationof multiple layers of spatial evidence in order to delineate mineralexploration targets. The fact that, in this special issue, only Zuoand Carranza propose a new geocomputational tool for predictivemapping of exploration targets indicates that the different meth-ods for this purpose are now mostly well-established. Althoughnot shown in this special issue, predictive maps of mineralexploration targets derived by any method can be used toestimate undiscovered mineral endowment and the number ofundiscovered deposits/prospects (McCammon and Kork, 1992;McCammon et al., 1994; Carranza et al., 2009; Carranza andSadeghi, 2010; Carranza, 2011). However, further research isneeded to answer the following logical question regarding theefficacy of a predictive map of exploration targets: ‘‘Which of the
predicted targets for further mineral exploration have the best chance
of leading to ore deposit discovery?’’
Acknowledgements
I thank Eric Grunsky and Jef Caers – erstwhile and presenteditors-in-chief, respectively, of the C&G – for making it possibleto publish this collection of papers in a special issue of the C&G.I acknowledge the technical assistance from Jean Hubay, formerManaging Editor of the C&G, and from Ashley Plummer and DavidVargas—erstwhile and current journal managers, respectively, ofthe C&G. I am grateful to all the authors for their contributions,even to those who withdrew their papers after considering thereviewers’ comments and even to those whose manuscripts werenot acceptable to the reviewers. Therefore, I deeply appreciate thesupport of the following individuals (in alphabetical order), someof who reviewed more than one paper (as indicated in parenth-eses), for the invaluable time they have given to review the qualityof the submitted manuscripts: Siti Abidin, Frits Agterberg (3),Stefano Albanese, Aleksander Astel, Tom Blenkinsop (2), FrancescoCamastra, Jun Deng, Detlef Eberle, Ignacio Gonzalez-Alvarez, PabloGumiel, Jeff Harris (2), Ioannis Kapageridis, Andreas Kohler, ShawnLaffan, Antony Mamuse, Ivan Marroquın, Gary Raines, HojjatRanjbar, Ernst Schetselaar (2), Ebru Sezer, Kevin Sprague, ThomasVillmann, Gorazd Zibret, Renguang Zuo (2).
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Emmanuel John M. Carranza n
Department of Earth Systems Analysis,
Faculty of Geo-Information Science and Earth Observation (ITC),
University of Twente,
Enschede, The Netherlands
E-mail address: [email protected]
n Tel.: þ31 53 4874562; fax: þ31 53 4874336.
