Keywords: (Fuzzy Logic Integration, Mineral Prospectivity, Urmia-Dokhtar, Kajan, GIS)

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1 Mineral Prospectivity Mapping by Fuzzy Logic Data Integration, Kajan Area in Central Iran Pezhman Rasekh 1 (p.rasekh@ut.ac.ir), Farshid Kiani 1, Hooshang H. Asadi 1, Seyed Hassan Tabatabaei 1 1 Isfahan University of Technology, Department of Mining Engineering, Isfahan, Iran Abstract: Kajan area is located in east of Isfahan, within the Urmia-Dokhtar volcanic belt. The volcanic rocks of the area are mostly associated with the Tertiary volcanic activities. The current study is carried out to identify new promising targets for regional exploration. Multiple data sources (e.g., stream sediment geochemical data, magnetic surveys, faults, geological and satellite data) are processed and then integrated by using Fuzzy Logic modeling to produce a final favorability map for regional copper exploration in the Kajan area. Keywords: (Fuzzy Logic Integration, Mineral Prospectivity, Urmia-Dokhtar, Kajan, GIS)

2 Introduction Predictive prospectivity mapping is used to define favorable areas for mineral exploration (Rasekh et al, 2013). This method can be applied in various scales from global to local scale exploration targeting. Definition of the exploration model is based on mineralization model. This gives the framework for initializing a predictive mineralization targeting. In this research we first processed different exploration data set such as geological map at the scale of 1: , stream sediment geochemistry, airborne magnetics, structural map and Aster satellite data to identify special proxies related to copper mineralization. Then the results were integrated by fuzzy logic data integration method to create a final potential map for regional copper exploration. Figure 1 shows the location map of the Kajan area at the Urumiye-Dokhtar volcanic belt and also the 1: geological map of the area. The term "fuzzy logic" was first introduced in 1965 by Lotfi A. Zadeh. Fuzzy logic has been applied to many fields, from control theory to artificial intelligence. It is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1. It has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false. The binary predictor patterns are then assigned weights based on the optimum spatial association to calculate a copper potential map (Carranza and Hale, 2000). The integration process will be discussed and demonstrated during this study. Fig.1: Kajan exploration area

3 Methodology As it is shown in (Fig. 2), multiple data sources (e.g., stream sediment geochemical data, magnetic surveys, faults, geological and satellite data) are processed and then integrated by using Fuzzy Logic modeling algorithm to produce a final favorability map for regional copper exploration in the Kajan area. Binary predictor patterns for mapping mineral potential assumes a crisp boundary between favorable and unfavorable ground (Bonham-Carter et al, 1989; Carranza and Hale, 2000). Fig.2: Overview of classified outputs of each layer. These classified layers are going to be involved in the final integration process. Raster information layers with assigned crisp numbers between 1-10, or fuzzy memberships (1-10) are considered. These information are produced using fuzzy functions such as linear, large and near (Bonham Carter, 1994; Carranza, 2008), geological, geochemical, aeromagnetic, structural, geological, Aster and ETM+ satellite data. These data are analyzed and interpreted using a range of different methods. In order to prevent missing data in the places where one information layer among overlapping layers does not have a value (nodata), crisp value 1 or a ~0 fuzzy value is assigned to such areas. Coordinate system for all of the maps is UTM (Universal Transfer Mercator) zone 38N. Mineral prospectivity maps are then generated with integrating factor maps using knowledge-driven index overlay and fuzzy logic methods (Bonham Carter, 1994, Nykanen et al., 2008). Different weights are multiplied in each layer based on their relativity and correlation with copper mineralization of the study area. The steps of preparing each layer are presented in following sections. Schematic diagram of the integration procedure is shown in (Fig. 3).

4 Fig.3: Schematic diagram of data processing toward anomaly delineation. Results and discussions The geological dataset consists of an ArcMap shape file of lithologies as well as 1:100,000 bedrock map of the study area. In order to allocate specific weights to the promising rock types, a new column added to attribute table of the digitized lithological shape file in order to specify a certain weight to each rock type. The weights are ranging from 1 to 10 and fuzzy membership of the geological units was produced with multiplying 8/100 by the crisp values (Table. 1). As a result, the final value of the promising host rocks will be higher at the end of the integration process. Considering the copper mineralization system in this area, Eocene felsic and basic to intermediate intrusive rocks got highest value. (Fig. 4). Table 1. Fuzzy membership of the geological units was produced with multiplying 8/100 by the crisp values. Eocene felsic and basic to intermediate intrusive rocks got highest value. Rock Units Crisp Fuzzy Gr E1vt, E2rt, E2vp, md, mdq E2vt, m ap, da, E1t, Emv, h, K1l, mv, plda E1r, E1rt, E1tb, E1vb, E2r, eb, Erd, K1v, K1vl, Mb, plla, Plhd, gd, s E1ad, E1ts, K2ls, plag Hz, mb Ms, mt E1c, E2cts, Mc, Qag, Qag, Qal, Qfbg, Qfg, Qfm, Qsa, Qsc, Qtbg, Qtg

Fig.4: Exported map of weighted rock types Traditionally, geochemical exploration based on stream sediment data, is a useful method to identify anomalous areas, especially in the preliminary stages

5 Fig.4: Exported map of weighted rock types Traditionally, geochemical exploration based on stream sediment data, is a useful method to identify anomalous areas, especially in the preliminary stages of prospecting for concealed mineral deposits (Yousefi et al., 2013). Therefore, many researchers have created maps of mineral potential using stream sediment geochemical data (Abdolmaleki et al., 2014; Yousefi et al., 2012; Carranza, 2010; Carranza and Hale, 1997). Geochemical dataset consists of 512 stream sediment samples which have been collected from the whole parts of the Kajan 1:100,000 scale geological map. All samples were analyzed by inductively coupled plasma-optical emission spectrometry (ICP-OES) for 22 elements. The data is then imported to Minitab statistical software and initial statistical processes is done by means of preparing the data for further investigations. Due to classification of the data using ArcMap software, Bonferroni confidence intervals for the standard deviations is calculated as well as median absolute deviations (MAD). The two mentioned statistical procedures, serve as exploratory approaches to classify background and multi-element anomalies. Although, it is claimed that conducting catchment basin analysis (or SCBs) is the essential part of this step and interpolation methods are not suitable for estimation of stream sediment geochemical data, assuming the limited time, the processed data is then imported to ArcMap software to create

iso-grade contour maps resulting from times function of IDW and kriging geostatistical interpolation methods (Fig.5). Fig.5: Exported map of stream sediment data.

6 iso-grade contour maps resulting from times function of IDW and kriging geostatistical interpolation methods (Fig.5). Fig.5: Exported map of stream sediment data. Aeromagnetic data provide information on high electrical conductivity in the near subsurface relating to concealed geological structure, metalliferous mineral deposits and other electrical infrastructure (Hamidbeygi et al, 2014). Thus, studying EM data can provide us with a lot of useful information toward metal-based mineral exploration. In terms of pre-processing of the raw data, RTP correction has been done using Oasis Montaj geophysical software. The results then were imported to ArcMap for further geophysical processing. Resulted map of geophysical layer is clearly visible (Fig. 6).

7 Fig.6: Exported map of aeromagnetic data Many prospectors will notice a relationship between faults and mineralization. A large percentage of precious metal mineral deposits (not all) are formed by the circulation of heated water through the hosted formation which is caused due to laminate structures (Rasekh et al, 2013). Faults of the study area were mapped according to laminate structures of Kajan geological map and then involved in the final fuzzy integration with consideration of a buffer of maximum 500 meters (Fig. 7).

Fig.7: Exported map of weighted laminated structures Numerous remote sensing investigations for mineral exploration and lithological mapping have been conducted in arid and semi-arid

2012a, Pour and Hashim, 2012b, Pour and Hashim, 2013, Sabins, 1999, Bonham-Carter, 1989, Porwal, 2003).

8 Fig.7: Exported map of weighted laminated structures Numerous remote sensing investigations for mineral exploration and lithological mapping have been conducted in arid and semi-arid terrains, with large exposures of geologic materials, allowing the acquisition of spectral information directly from rock soil assemblages (Pour and Hashim, 2011, Pour and Hashim, 2012a, Pour and Hashim, 2012b, Pour and Hashim, 2013, Sabins, 1999, Bonham-Carter, 1989, Porwal, 2003). Hence, band ratios derived from provided Aster data of Kajan area (5/8 and 4/5) allow identification of propylitic and argilic alterations at regional scale, respectively (Fig. 8). Fig.8: Exported map of aster data. White colors indicates argillic and propylitic alteration.

After producing fuzzy memberships of each of the involved integration layers, fuzzy modeling is done and final fuzzy overlay layer is resulted as (Fig. 9). Fig.

9 After producing fuzzy memberships of each of the involved integration layers, fuzzy modeling is done and final fuzzy overlay layer is resulted as (Fig. 9). Fig.9: Final exported map of fuzzy logic integration procedure Conclusions The main conclusions of this research are summarized as follow: Processing Aster satellite imagery data by Ls-Fit method revealed two important hydrothermal alteration zones of alrgillic and propyllitic, mostly associated with the known copper mineralized zones and kaolinite deposits of the area. Processing stream sediment data of the area showed several copper anomalies, partly associated with the mapped hydrothermal alteration. Final prospectivity map of the area created by fuzzy logic data integration modelling identified several high potential areas for regional exploration.

10 References Rasekh, P., Parvar, K., " Integrating Remote Sensing and Tectonic Studies in order to Identification of Potential Areas for mineralization in Chahmir mine, Bahabad, Yazd, Iran", 9th Iranian Student Mining Engineering Conference, University of Birjand, Iran. Hamidbeygi, M., Rasekh, P., "Integrating Resistivity and Microgravity Data in Detection of Underground Canals", National Conference on Mining Science 2014, University of Mazandaran, Iran. Asadi, H.H., Hale, M., A predictive GIS model for mapping potential gold and base metal mineralization in Takabarea, Iran. Computers & Geosciences 27 (2001) Yousefi, M., Carranza, EJM., Kamkar-Rouhani, A., "Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling", Journal of geochemical exploration, 128, Abdolmaleki, M., Mokhtari, A., Akbar, S., Alipour-Asll, M., Carranza, EJM., "Catchment basin analysis of stream sediment geochemical data: incorporation of slope effect ", Journal of Geochemical Exploration, 140. pp Darehshiri, A., Panji, M., Mokhtari, A., "Identifying geochemical anomalies associated with Cu mineralization in stream sediment samples in Gharachaman area, northwest of Iran ", Journal of geochemical exploration. 110, Bonham-Carter, G.F., Agterberg, F.P., Wright, D.F., Weights-of-evidence modelling: a new approach to mapping mineral potential. In: Agterberg, F.P., Bonham-Carter, G.F. (Eds.), Statistical Applications in the Earth Sciences. Geological Survey of Canada, pp , paper Porwal, A., Carranza, E.J.M., and Hale, M., Knowledge driven and data-driven fuzzy models for predictive mineral potential mapping. Natural Resources Research, v. 12(1), p Pour, A. B. and Hashim, M. (2011a) Identification of hydrothermal alteration minerals for exploring of porphyry copper deposit using ASTER data, SE Iran. J. Asian Earth Sci., 42, Pour, A. B. and Hashim, M. (2012b) Identifying areas of high economic-potential copper mineralization using ASTER data in the Urumieh Dokhtar Volcanic Belt, Iran. Adv. Space Res., 49, Sabins, F.F., Remote sensing for mineral exploration. Ore Geology Reviews 14,

Resource Management through Machine Learning

Resource Management through Machine Learning Justin Granek Eldad Haber* Elliot Holtham University of British Columbia University of British Columbia NEXT Exploration Inc Vancouver, BC V6T 1Z4 Vancouver,