Spatial modelling of phosphorite deposits in the Irece Basin, Bahia, Brazil. W. Franca-Rocha 1,2, A. Misi 2

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1 Spatial modelling of phosphorite deposits in the Irece Basin, Bahia, Brazil W. Franca-Rocha 1,2, A. Misi 2 1 Universidade Estadual de Feira de Santana,Km 3, Br 116, Feira de Santana, , Brazil, 2 Universidade Federal da Bahia, Grupo de Metalogênese, Instituto de Geociências, Salvador, , Brazil, wrocha@uefs.br 1. Abstract An innovative exploration approach uses the analysis of a soil P 2 O 5 map, LANDSAT TM satellite images, geological, geophysical and mineral occurrence data to recognize a combination of mapped geological features, spectral characteristics, and geochemical signatures that could be associated with the phosphate mineralization. Although not all the known deposits were predicted using this approach, the results indicated that soil geochemical data are a valuable predictor of the key dolomite unit, and this has application in less well mapped parts of these Proterozoic basins. 2. Introduction The Irece phosphorite ore, Bahia (Brazil), occurs within the Neoproterozoic metasedimentary Una-Bambui Group, which comprises glaciogenic sediments (Bebedouro Formation) and carbonate successions (Salitre Formation) (Figure 1). The Salitre Formation consists of a thick (about 1.2 km) dominantly carbonate sequence, lying over glaciogenic diamictites of the Bebedouro Formation. Sedimentary and structural features of the Salitre Formation indicate that primary phosphate concentrations formed in shallow-water sedimentary facies and are directly related to biogenic structures (columnar and laminar stromatolites). The presence and proximity to a laminated limestone and cherty dolomite (B and B1 units of Salitre Formation) is particularly important to characterize this stratigraphic level (Misi et al, 1999). During an earlier study, Franca-Rocha (2001) conducted a GIS-based spatial analysis in the Irecê carbonate basin. Fuzzy logic and the predictive probabilistic methods of weights of evidence and logistic regression were applied to build maps showing favorable areas for lead-zinc deposits. Phosphorite ore are associated to the same shallow sedimentary environment, but controlled by distinct factors. The purpose of this study is to test the performance of the geological-morphological, geochemical and geophysical variables to predict occurrences of phosphorite in Neoproterozoic dolomitic units of the Salitre Formation at Irecê-Lapão target area. The results were obtained from a weights-of-evidence modeling and analyzed to evaluate their applicability.

2 Fig. 1. Location of Neoproterozoic carbonate basins in Bahia State, NE= Brazil, showing the potential for phosphorite mineral deposits. Yellow polygon is the study area. 3. Methodology The methodology involves the application as data integrator of the weights-of-evidence modeling, a log linear form of Bayes Rule with conditional independence. This method allows the user to explore the spatial relationship between known mineral deposits and exploration datasets from a variety of sources (Bonham-Carter, 1994). Because most studies of this type have a limited number of deposits, it is advantageous to generalize the maps to a small number of classes, often to binary classes. The process of evaluating weights and reclassification gives invaluable insights into the spatial associations present in the data (e.g. separation of background from anomaly in geochemistry, selection of optimal distances for buffering linear features, etc.). Weights-of-evidence procedure consists in the calculation of the posterior logit from the prior logit and sum of weights, one for each map, see Bonham-Carter (1994). The posterior logit is evaluated over the map region and is usually converted to posterior probability for display. The effects of various sources of uncertainty on the final result can be modelled such as the variances of weights and variance due to missing data (incomplete surveys). The seven themes used as evidence of deposits in this study were derived from the raw data sets that consisted of a digitized geological map, DEM, remotely sensed data, airborne radiometric and soil geochemical survey data. The uranium and thorium maps

3 are incomplete (i.e. the surveys have gaps, or missing data) and the P2O5 map is based on uneven sample density, giving rise to sources of uncertainty in the posterior probability map that can be taken into consideration in modeling. Twenty-one P deposits were used as training points. to calculate statistical parameters for weighting the evidence by datadriven modeling. Weights (W+, W-), contrast (C=W+-W-) and confidence calculations guided the data-driven methods modeling. 4. Data Analysis Several statistical procedures can be used for helping to define geochemical and geophysical anomalies. These include the use of moving averages, kriging, probability graphs and other techniques. Most methods employ concentration values only. In order to improve the results of interpretation of geochemical and geophysical data by considering both the frequency distribution of concentration values and their spatial distribution, the concentration-area fractal method (C-A method) was introduced by Cheng, Agterberg, and Ballantyne (1994) for anomaly separation. In this study we used the C-A method to identify both geochemical and geophysical anomalies. The C-A method separates anomalies from background on the basis of the frequency distribution of values, as well as the spatial and geometrical properties of geochemical and geophysical patterns. The geochemical survey shows high concentration values of P2O5 in very small areas, relatively low values in a much larger area and very low concentrations values in all other regions, typical of many trace element distributions. Situations of this type can be often described by using multifractal models (Cheng, Agterberg and Ballantyne, 1994). The study area is considered to have high potential for P mineral deposits. The soil geochemical survey was designed to discover P occurrences related to dolomitic shallowwater facies in the carbonate sequences of the Una-Bambui Group of Proterozoic age. All soil samples, not always regularly spaced, (12,900 samples) were chemically analyzed for P, Pb, and Zn, among others, and the results were stored in a GIS. The C-A method was also applied to the gridded airborne radioelement values. Plots for U and Th were produced, again indicating that power-law relationships were satisfied. As a consequence, breaks in slope were determined giving estimated thresholds of Th=7 cps and U=8 cps. 5. Results Figure.2 shows the binary anomalous maps obtained from theme weights that were used as evidence map: Morphology or topographic relationship (Fig. 2A), Stratigraphic control - unit B1 (Fig. 2B), a map of P 2 O 5 in soil (Fig. 2C), a map of a 250 m buffer around lineaments (Fig. 2D), a thorium and uranium maps (airborne radiometric survey) (Fig. 2E e 2F), and a spectral signature extracted from LANDSAT TM satellite image (Fig. 2G). For each map, threshold selection were guided on a way to maximize as far as possible the spatial association of the deposits with the resulting binary pattern Table 1 summarizes the statistics from the weights of evidence (WOFE) method, as applied to the 21 P 2 O 5 deposits in the Irecê Basin.

4 A B C D E F G Figure 2: Anomaly maps for A. Topography, B. Geology, C. Soil Geochemistry D. Structural, E. Geophysics - Uranium, F. Geophysics - Thorium and G. Spectral feature. Yellow colors are anomalous pattern. Dots are locations of deposits.

5 Table 1 - Weights (W+, W-), contrast (C) values and studentized contrast values or confidence (C/s(C)). Rows are sorted by decreasing C, a measure of spatial association between the deposits and the map. Prior probability is 21/ = (assuming unit cell=1 km2). EVIDENCE MAPS W+ W- C C/S(C) Th 0,9674-1,8839 2,8513 3,8119 P2O5 soil -0,2580 2,1472 2,4053 4,4056 Topograph -0,5986 1,1102 1,7088 3,7466 Unit b1-0,3135 0,8035 1,1169 2,4123 U 0,1112-0,6662 0,7774 1,0377 Lineaments distance 0,4685-0,2670 0,7356 1,6190 Landsat class 0,4606-0,2200 0,6806 1,4746 Deposits 21, ,8300 0,0183 Figure 3 shows the final integrated mineral potential map for phosphorite modeled by weights-of-evidence method. High probability areas in red indicated possible targets to follow-up. Figure 3: Maps of mineral favorability for P2O5 according Weights-of-evidence approach. Red saturation means highest mineral potential. Blue saturation means low favorability areas. Dots are locations of deposits.

6 6. Discussion The geochemical map was gridded by kriging and analyzed by the concentration-area method to determine the threshold between anomalous and background concentrations. Seven binary maps representing diagnostic deposit recognition criteria were combined in weights-of-evidence model to predict the spatial distribution of known mineral occurrences and to calculate a prospective map of further phosphate potential in the Irece Basin. The results indicate that thorium geophysics and soil geochemical data are valuable predictors of the key dolomite unit and can be applied to prospect in less well mapped parts of these Proterozoic basins. Stratigraphic, structural and spectral evidence were important locally. Among the three targets obtained in this modeling, the centralnorth one named Irecê-Lapão (Fig. 3), was the most important, showing concentration of P deposits and the highest posterior probability values. 7. References Agterberg, F.P., Bonham-Carter, G.F. and Wright, D.F., 1990, Statistical pattern integration for mineral exploration, in: Gaal, G. and Merriam, D.F., (eds.), Computer applications in resource estimation, prediction and assessment for minerals and petroleum: Pergamon, Oxford, p Agterberg, F.P., Bonham Carter, G.F., Cheng, Q., and Wright, D.F., 1993, Weights of evidence modeling and weighted logistic regression for mineral potential mapping, in: Davis, J.C. and Herzfeld, U.C., (eds.), Computers in geology 25 years of progress: Oxford University Press, International Association of Mathematical Geology, Studies in Mathematical Geology, v. 5 p Bonham-Carter, G.F. - Geographic Information Systems for Geoscientists: Modelling with GIS, Pergamon, Oxford, 398 p., Cheng Q., Agterberg, F.P., and Ballantyne, S. B., 1994, The separation of geochemical anomalies from background by fractal methods: Journal of Geochemical Exploration, v. 51, p Franca-Rocha, W. J. S., 2001, Modelagem Metalogenetica na Bacia de Irecê (Ba) através de Sistema de Informações Geográficas (SIG): Tese de Doutoramento, Instituto de Geociências, Universidade Federal da Bahia, Salvador, 350 p. Harris, D. P. and Pan, G., 1991, Consistent geological areas for epithermal gold-silver deposits in the Walker Lake Quadrangle of Nevada and California delineated by quantitative methods: Economic Geology, v. 86, no. 1, p Misi, A., Iyer, S.S., Tassinari, C.G., Kyle, J.R., Coelho, C.E.S., Franca-Rocha, W., Gomes, A.S.R, Cunha, I.A., and Carvalho, I.G., 1999, Geological and isotopic constraints on the metallogenic evolution of the Proterozoic sediment-hosted Pb-Zn(Ag) deposits of Brazil: Gondwana Research, v. 2, no. 1, p

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