Reservoir characterization and porosity classification using probabilistic neural network (PNN) based on single and multi-smoothing parameters

A probabilistic neural network (PNN) is a feed-forward neural network using a smoothing parameter. We used the PNN algorithm based on single and multi-smoothing parameters for multi-dimensional data classification. Using multi-smoothing parameters, we implemented an improved probabilistic neural network (PNN) to estimate the porosity distribution of a gas reservoir in the North Sea. Comparing the results of implementing smoothing parameters obtained from model-based optimization and particle swarm optimization (PSO) indicated the efficiency of PNN in characterizing the gas. Also, results showed that while the PSO algorithm was able to specify smoothing parameters with more precision, about 9%, it was very time-consuming. Finally, multi PNN based on PSO was applied to estimate the porosity distribution of the F3 reservoir. The results validated the main fracture or gas chimney of the F3 reservoir with higher porosity. Also, gas-bearing layers were highlighted by energy and similarity attributes

Fractals and implications for mineral favorability maps: the example of iron oxide-copper-gold deposits from Carajás (PA)

Orientadores: Carlos Roberto de Souza Filho, Emmanuel John Muico CarranzaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de GeociênciasResumo: Desde a definição do conceito da geometria fractal na segunda metade do século XX, a importância dos fractais para a descrição e entendimento de feições geológicas gradualmente ganhou importância. Mais recentemente, diversos trabalhos têm sugerido que a distribuição espacial de depósitos minerais apresenta geometria fractal, a qual representaria a complexa interação de processos geológicos necessários para a gênese de uma mineralização. A manifestação da geometria fractal se dá através da invariância escalar, ou seja, a propriedade de uma feição conservar suas características geométricas independente da escala espacial. Esta característica é promissora para o estudo de depósitos minerais, pois sugere a possibilidade de que informações sobre a geometria da mineralização em uma escala possa ser usada para inferir aspectos da geometria em outras escalas. Uma vez que a geometria das mineralizações é consequência dos controles que atuaram durante e após sua formação, a possibilidade de estudos com uma abordagem fractal tem aplicações teóricas e práticas. Considerando o exposto, a presente pesquisa dedicou-se a investigar se de fato a geometria de depósitos minerais apresenta invariância escalar, e em caso positivo, que informações ela permite inferir sobre os controles de mineralização. Para esta investigação foi escolhida como área de estudo a região do depósito Iron Oxide-Copper-Gold (IOCG) de Sossego, na Província Mineral de Carajás (PA). Depósitos IOCG apresentam forte controle estrutural, que somados a farta disponibilidade de dados nas escalas regional, local e microscópica tornam a área da mina de Sossego ideal para a pesquisa proposta. Assim, os dados já disponíveis na literatura foram integrados com novas medidas estruturais e novas lâminas orientadas de amostras coletadas nas cavas da mina. A geometria da mineralização foi avaliada em três diferentes escalas: na escala regional examinou-se a distribuição espacial dos depósitos IOCG conhecidos; na escala local examinou-se a geometria das estruturas e corpos mineralizados no depósito de Sossego; na escala microscópica foi avaliada a geometria da distribuição espacial e da forma dos grãos de minerais de minério. O conjunto de resultados indica que os depósitos IOCG da região de Carajás, e em particular o depósito de Sossego, apresentam geometria fractal, conservando a orientação e anisotropia nas diferentes escalas. A orientação e anisotropia das mineralizações são aspectos geométricos que resultam diretamente do controle exercido pela trama estrutural subjacente. Desta forma, os resultados indicam que o controle estrutural gera a invariância escalar devido à influência que exerce sobre a permeabilidade das rochas, um fator essencial para a geração de depósitos hidrotermais. A permeabilidade é definida em escala microscópica através de planos de foliação, microfraturas e vênulas, as quais se relacionam diretamente com estruturas de escalas maiores, tais como zonas de cisalhamento, falhas e veios, criando uma rede permeável consistente através das escalas. No caso de Carajás, a geometria destas áreas permeáveis reflete a interação entre uma trama dúctil anterior, de permeabilidade difusa, e uma trama rúptil posterior, com permeabilidade focada. Os resultados deste trabalho sugerem que a abordagem fractal para o estudo da gênese de depósitos minerais tem potencial concreto para gerar resultados relevantes, inclusive para a avaliação da favorabilidade mineral de áreas em exploraçãoAbstract: Since the concept of fractal geometry was defined in the second half of the twentieth century, the importance of fractals for the description and understanding of geological features has gradually gained importance. More recent work has suggested that the spatial distribution of mineral deposits presents fractal geometry, which represents the complex interaction of geological processes necessary for the genesis of a mineralization. The manifestation of fractal geometry occurs through scale invariance, i.e. the property of a feature that conserves its geometrical characteristics independent of the spatial scale. This property is promising for the study of mineral deposits because it suggests the possibility that information about the geometry of a mineralization at one scale can be used to infer aspects of its geometry at other scales. Since mineralization geometry is a consequence of controls that acted during and after its formation, studies with a fractal approach have theoretical and practical applications. Considering the above, the present research investigated if the geometry of mineral deposits presents scale invariance, and if so, what information it permits to infer about the mineralization controls. For this investigation the study area chosen was the iron oxide-copper-gold (IOCG) Sossego deposit, in the Carajás Mineral Province (PA). IOCG deposits present strong structural control, which taken in conjunction with data availability at the regional, local and microscopic scales make the Sossego deposit area ideal for the proposed research. Thus, data already available in the literature were integrated with new structural measurements and new oriented thin sections of samples collected in the mine pits. Mineralization geometry was evaluated at three different scales: in the regional scale the spatial distribution of the known IOCG deposits was examined; in the local scale the geometry of the mineralized structures and orebodies at the Sossego deposit was examined; in the microscale the geometry of the spatial distribution and the shape of ore mineral grains were evaluated. The bulk of results indicate that the IOCG deposits of Carajás province, and in particular the Sossego deposit, present fractal geometry, conserving the orientation and anisotropy at the different scales. The orientation and anisotropy of the mineralization are geometric aspects that result directly from the control exerted by the underlying structural framework. As a consequence, the results indicate that the structural control generates the scale invariance due to its influence on rock permeability, an essential factor for the genesis of hydrothermal deposits. Permeability is defined at the microscale through foliation planes, microfractures and veinlets, which are directly related to structures of larger scales, such as shear zones, faults and veins, creating a consistent permeable network throughout the scales. In the case of Carajás, the geometry of these permeable areas reflects the interaction between an older ductile framework with diffuse permeability, and a posterior brittle network with focused permeability. The results of this work suggest that the fractal approach to the study of the genesis of mineral deposits has concrete potential to generate relevant results, including for the evaluation of the mineral favorability on exploration areasMestradoGeologia e Recursos NaturaisMestre em Geociências2015/11186-3401316/2014-9CAPESFAPESPCNP

Self-organizing Maps Applied To Mapping Mineral Potential In The Region Of Eastern Sierra Mineral Province Of Carajás, To [mapas Auto-organizáveis Aplicados Ao Mapeamento Do Potencial Mineral Na Região De Serra Leste, Província Mineral De Carajás, Pará]

A Self-Organizing Map (SOM) was designed with the aim of integrating and searching for patterns in airborne geological and geophysical gammaspectrometric and magnetic data of the Serra Leste region, Carajás Mineral Province. SOM is an unsupervised Artificial Neural Network method that performs a non-linear mapping from a high-dimensional data space to a 2-dimensional grid, whereas preserving the topological relations in the original data. The SOM grid can be efficiently used in an integrated visualization and understanding of the internal relationships in the data. The K-means algorithm is applied to the SOM grid to reduce the number of mapped patterns so as to facilitate interpretation. Unfolding of the clustered SOM grid associates each mapped pattern with the spatial position of each data point. The SOM reclassified map was compared with a classified map obtained with the Fuzzy C-means method for the same input data and with the same number of classes. The results show the potentiality of SOM in producing higher quality integrated maps to support mineral exploration. © 2010 Sociedade Brasileira de Geofísica.283397409Bezdek, J.C., (1981) Pattern Recognition with fuzzy objective function algorithms, p. 256. , Kluwer Academic Publishers, NorwellBriggs, I.C., Machine contouring using minimum curvature (1974) Geophysics, 39, pp. 39-48Cabral, A.R., Lehmann, B., Kwitko, R., Cravo, C.H.C., The Serra Pelada Au-Pd-Pt deposit, Carajás Mineral Province, northern Brazilreconnaissance mineralogy and chemistry of very high grade palladian gold mineralization (2002) Econ. Geol., 97, pp. 1127-1138Cabral, A.R., Lehmann, B., Kwitko, R., Cravo, C.H.C., Palladium and platinum minerals from the Serra Pelada Au-Pd-Pt deposit, Carajás Mineral Province, Northern Brazil (2002) Can. Mineral, 40, pp. 1451-1463Cios, K.J., Pedrycz, W., Swiniarsk, R.M., (1998) Data Mining Methods for Knowledge Discovery, p. 495. , Kluwer Academic Publishers, NorwellCoelho, C.E.S., Rodrigues, O.B., Jazida de manganês do azul, Serra dos Carajás, Pará (1986) Principais depósitos minerais do Brasil, 2, pp. 145-152. , In: SCHOBBENHAUS C & COELHO CES (Eds.), DNPM/CVRDDavies, D., Bouldin, D., A Cluster Separation Measure (1979) IEEE Trans. Pattern Anal. Mach. Intell., 1 (2), pp. 224-227Demicco, R.V., Klir, G.J., (2003) Fuzzy Logic in Geology, p. 347. , Elsevier Science Academic Press, San DiegoRevisão litoestratigr áfica da Província Mineral de Carajás - litoestratigrafia e principais depósitos minerais (1988) Congresso Brasileiro de Geologia, pp. 11-54. , DOCEGEO. Rio Doce Geologia e Mineração S.A., In:, 35., Belém: SBG, Anexo aos AnaisInternational geomagnetic reference field, 1995, revision (1996) Geophys. J. Int., 125, pp. 318-321. , IAGA DIVISION 5, WORKING GROUP 8Klose, C.D., Self-Organizing Maps for Geoscientific Data Analysis: Geological Interpretation of Multidimensional Geophysical Data (2006) Comput. Geosci., 10, pp. 265-277Kohonen, T., (2001) Self-Organizing Map, p. 113. , 3 ed., Springer-Verlag, BerlinLeite, E.P., Souza Filho, C.R., Probabilistic neural networks applied to mineral potential mapping for platinum-group elements in the Serra Leste region, Carajás Mineral Province, Brazil (2009) Comput. Geosci. (UK), 35, pp. 675-689Leite, E.P., Souza Filho, C.R., Artificial neural networks applied to mineral potential mapping for copper-gold mineralizations in the Carajás Mineral Province (2009) Geophys. Prospect., 57 (6), pp. 1049-1065Mansour, Z., Ali, P., Mahdi, Z., A computational optimized extended model for mineral potential mapping based on WofE method (2009) Am. J. Applied Sci., 6 (2), pp. 200-203Marschik, R., Mathur, R., Ruiz, J., Leveille, R.A., Almeida, A.J.D.E., Late Archean Cu-Au-Mo mineralization at Gameleira and Serra Verde, Carajás Mineral Province, Brazil: Constraints from Re-Os molybdenite ages (2005) Miner. Deposita, 39, pp. 983-991Penn, S.B., Using self-organizing maps, histograms, and standard deviation to detect anomalies in hyperspectral imagery data (2002) Proceedings of the Fifth International Airborne Remote Sensing Conference, pp. 22-24. , In:, Miami, FloridaPenn, S.B., Using self-organizing maps to visualize highdimensional data (2004) Comput. Geosci. (UK), 31 (5), pp. 531-544Roest, W.R., Verhoef, J., Pilkington, M., Magnetic interpretation using the 3-D analytical signal (1992) Geophysics, 57, pp. 116-125Saveliev, A.A., Dobrinin, D.V., Hierarchical multispectral image classification based on self organized maps (1999) IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99, 5, pp. 2510-2512. , In:, Piscataway, NJSeber, G.A.F., (2004) Multivariate observations, p. 686. , 2 ed., John Wiley & Sons, Inc., Hoboken, NJSouza Filho, C.R., Nunes, A.R., Leite, E.P., Monteiro, L.V.S., Xavier, R.P., Spatial analysis of airborne geophysical data applied to geological mapping and mineral prospecting in the Serra Leste region, Carajás Mineral Province, Brazil (2007) Surv. Geophys., 28, pp. 377-405Suita, M.T.F., (1988) Geologia da área Luanga com ênfase na petrologia do complexo básico-ultrabásico Luanga e depósitos de cromita associados, Pará, p. 83. , Dissertação de Mestrado, Brasília, UnBUltsch, A., Knowledge acquisition with self-organizing neural networks (1992) Artificial Neural Networks, pp. 735-740. , In: ALEKSANDER I & TAYLOR J (Eds.), Elsevier Science Publishers, B.VUltsch, A., U*-matrix: A tool to visualize clusters in high dimensional data (1993) Technical Report, 36, p. 12. , University of Marburg, Department of Computer ScienceVeneziani, P., Okida, R., (2001) Mapeamento geológico-estrutural da região do Projeto Platina (Serra Pelada) baseado em dados integrados RADARSAT-TM e aerogeofísica, p. 52. , Relatório TécnicoVesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J., SOM Toolbox for Matlab 5 (2000) Technical Report, A57, p. 59. , Helsinki University of TechnologyVillas, R.N., Santos, M.D., Gold deposits of the Carajá's Mineral Province: Deposit types and metallogenesis (2001) Miner. Deposita, 36, pp. 300-33

High-organizing Maps Mapping The Potential Applied To Mineral Region Of Eastern Sierra Mineral Province Of Carajás, To [comparação De Métodos De Estimativa De Profundidades De Fontes Magnéticas Utilizando Dados Aeromagnéticos Da Província Mineral De Carajás, Pará]

With the goal of providing an effective analysis for assessment and interpretation of depth to magnetic sources, depth estimates were yielded from high density, aeromagnetic data collected over the Serra Leste region (Carajás Mineral Province, Pará State, Brazil), by three distinct methods: (i) Euler Deconvolution; (ii) Analytic Signal; and (iii) Local Wavenumber. According to the results obtained by these three methods, most of the magnetic sources in the study area are located above 100 m depth. Statistical analysis showed that the Euler Deconvolution method provides more accurate depth estimates (mean standard error = 6%) and that the solutions are better correlated with geological structures and contacts, especially those related to the mineral deposits. For instance, it was verified that the Serra Pelada (Au, Pt, Pd) deposit is located over a NNE-SWW dipping magnetic trend with depths increasing from 25 to 270 m. At this deposit, the estimated depth is about 205 m. © 2010 Sociedade Brasileira de Geofísica.283411426Aboud, E., Salem, A., Ushijuma, K., Interpretation of aeromagnetic data of Gebel El-Zeit Area, Gulf of Suez, Egypt Using Magnetic Gradient Techniques (2003) Memoirs of the Faculty of Engineering, 63 (3), pp. 139-149. , Kyushu University, SEPAraújo, O.J.B., Maia, R.G.N., Jorge-João, X.S., Costa, J.B.S., A megaestruturação da folha Serra dos Carajás (1988) Proceedings VII Cong. Lat. Amer. Geol., pp. 324-333. , In:Barbosa, V.C.F., Silva, J.B.C., Medeiros, W.E., Stability analysis and improvement of structural index estimation in Euler deconvolution (1999) Geophysics, 64 (1), pp. 48-60Barbosa, V.C.F., Silva, J.B.C., Medeiros, W.E., Making Euler deconvolution applicable to small ground magnetic surveys (2000) J. Appl. Geophys., 43 (1), pp. 55-68Barbosa, V.C.F., Silva, J.B.C., Deconvolução de Euler: Passado, presente e futuro - um tutorial (2005) Revista Brasileira de Geofísica, 23 (3), pp. 243-250Barros, C.E.M., Barbey, P., Significance of garnet-bearing metamorphic rocks in the Archean supracrustal series of the Carajás Mining Province, Northern Brazil (2000) Rev. Bras. Geoc., 30 (3), pp. 367-370Barros, C.E.M., Sardinha, A.S., Barbosa, J.P.O., Krimski, R., McAmbira, M.J.B., Pb and U-Pb zircon ages of Archean syntectonic granites of the Carajás metallogenic Province, northern Brazil (2001) Proceedings III South Amer. Symp. Isot. Geol., pp. 94-97. , In:Barton, C.E., Baldwin, R.T., Barracloughd, D.R., Bushati, S., Chiappini, M., Cohen, Y., Coleman, R., Sabaka, T.J., International Geomagnetic Reference Field, 1995 revision presented by IAGA Division V, Working Group 8 (1996) Phys. Earth Planet. Inter., 97, pp. 23-26Briggs, I.C., Machine contouring using minimum curvature (1974) Geophysics, 39, pp. 39-48Dall'agnol, R., Lafon, J.M., McAmbira, M.J.B., Proterozoic Anorogenic Magmatism in the Central Amazonian Province, Amazonian Craton: Geochronological, Petrological and Geochemical Aspects (1994) Mineral. Petrol., 50, pp. 113-138Dall'agnol, R., Souza, Z.S., Althoff, F.J., Barros, C.E.M., Leite, A.A.S., Jorge-João, X.S., General aspects of the granitogenesis of the Caraj ás metallogenetic province (1997) Proceedings Intern. Symp. Gran. and Assoc. Miner., pp. 135-161. , In:, Salvador, Excursion GuideRevisão litoestratigr áfica da Província Mineral de Carajás - litoestratigrafia e principais depósitos minerais (1988) Congresso Brasileiro de Geologia, pp. 11-54. , DOCEGEO. Rio Doce Geologia e Mineração S.A., In:, 35., Belém: SBG, Anexo aos AnaisGalarza, T.M.A., McAmbira, M.J.B., Moura, C.A.V., Geocronologia Pb-Pb e Sm-Nd das rochas máficas do depósito Igarapé Bahia, Província Mineral de Carajás (PA) (2003) In: VII Simp. Geol. Amaz., , CD-ROM(2006) OASIS Montaj™, 6. , GEOSOFT, 4.1(6G) GEOSOFT, Inc., TorontoHirata, W.K., Rigon, J.C., Kadekaru, K., Cordeiro, A.A.C., Meireles, E.A., Geologia Regional da Província Mineral de Carajás (1982) Anais I Simp. Geol. Amaz., Belém - PA, 1, pp. 100-110. , In:Holdsworth, R., Pinheiro, R., The anatomy of shallow-crustal transpressional structures: Insights from the Archean Carajás fault zone, Amazon, Brazil (2000) J. Struct. Geol., 22, pp. 1105-1123Hsu, S.-K., Imaging magnetic sources using Euler's equation (2002) Geophys. Prospect., 50, pp. 15-25Hsu, S.-K., Sibuet, J.-C., Shyu, C.-T., High-resolution detection of geologic boundaries from potential-field anomalies: An enhanced analytic signal technique (1996) Geophysics, 61 (2), pp. 373-386Huhn, S.R.B., Santos, A.B.S., Amaral, A.F., Ledsham, E.J., Gouvêa, J.L., Martins, L.P.B., Montalvão, R.M.G., Costa, V.G., O terreno "granite-greenstone" da Região de Rio Maria - Sul do Pará (1988) Anais XXXV Congr. Bras. Geol., Belém - PA, 3, pp. 1438-1452. , In:Leite, E.P., Souza Filho, C.R., Probabilistic neural networks applied to mineral potential mapping for platinum-group elements in the Serra Leste region, Carajás Mineral Province, Brazil (2009) Comput. Geosci., 35 (3), pp. 675-687Leite, E.P., Souza Filho, C.R., Artificial Neural Networks Applied to Mineral Potential Mapping for Copper-Gold Mineralizations in the Caraj ás Mineral Province, Brazil (2009) Geophys. Prospect., 57 (6), pp. 1049-1065Li, J., Morozov, I.B., Chubak, G., Potential-field investigations of the Williston Basin basement (2005) Summary of Investigations 2005, 1, p. 11. , In:, Saskatchewan Geological Survey, Misc. Rep. 4.1, CD-ROM, Paper A-5McHado, N., Lindenmayer, D.H., Krough, T.E., Lindenmayer, Z.G., U-Pb geochronology of Archean magmatism and basement reactivation in the Carajás area, Amazon Shield, Brazil (1991) Precambrian Res., 49, pp. 1-26Monteiro, L.V.S., Xavier, R.P., Hitzman, M.W., Carvalho, E.R., Johnson, C.A., Souza Filho, C.R., Torresi, I., Spatial and temporal zoning of hydrothermal alteration and mineralization in the Sossego iron oxide copper gold deposit, Carajás Mineral Province, Brazil: Paragenesis and stable isotope constraints (2007) Miner. Deposita, 43, pp. 129-159Murdie, E.R., Styles, P., Upton, P., Eardley, P., Cassidy, N.J., Euler deconvolution methods used to determine the depth to archaeological features (1999) Geoarchaeologyexploration, environments, resources, 165, pp. 35-40. , In:, Geological Society, Special PublicationsMuszala, S.P., Grindlay, N.R., Bird, R.T., Three-dimensional Euler deconvolution and tectonic interpretation of marine magnetic anomaly data in the Puerto Rico trench (1999) J. Geophys. Res., 104 (B12), pp. 29175-29187Nabighian, M.N., The analytic signal of two-dimensional magnetic bodies with polygonal cross-section: Its properties and use for automated anomaly interpretation (1972) Geophysics, 37, pp. 507-517Nabighian, M.N., Additional comments on the analytic signal of two-dimensional magnetic bodies with polygonal cross-section (1974) Geophysics, 39, pp. 85-92Oliveira, N.V., Endo, I., Oliveira, L.G.S., Geometria do Sinclinal Gandarela baseada na Deconvolução Euler 2D e 3D - Quadrilátero Ferrífero (MG) (2005) Revista Brasileira de Geofísica, 23 (3), pp. 221-232Phillips, J.D., (1997) Potential Field Geophysical Software for the Pc, version 2.2: USGS Open-File Report 97-725, p. 34. , ftp://ftpext.usgs.gov/pub/cr/co/denver/musette/pub/pf/, Disponível em:, Acesso em: 14 jul. 2008Phillips, J.D., (2001) Processing and interpretation of aeromagnetic data for the Santa Cruz basin-Patagonia Mountains area, south-central Arizona - a preliminary report, p. 15. , USGS Open-File Report 02-98Pimentel, M.M., Lindenmayer, Z.G., Laux, J.H., Armstrong, R., Araújo, J.C., Geochronology and Nd isotope geochemistry of the Gameleira Cu-Au deposit, Serra dos Carajás, Brazil: 1.8-1.7 Ga hydrothermal alteration and mineralization (2003) J. South Am. Earth Sci., 15, pp. 803-813Pinheiro, R.V.L., Holdsworth, R.E., Reactivation of Archaean Strike-Slip Fault Systems, Amazon Region, Brazil (1997) J. Geol. Soc. London, 154, pp. 99-103Reid, A.B., Allsop, J.M., Granser, H., Millett, A.J., Somerton, I.W., Magnetic interpretation in three dimensions using Euler deconvolution (1990) Geophysics, 55 (1), pp. 80-91Rodrigues, E.S., Lafon, J.M., Scheller, T., Geocronologia Pb- Pb da Província Mineral de Carajás: Primeiros resultados (1992) Bol. Res. Exp. XXXVII Cong. Bras. Geol., 2, pp. 183-184. , In:Roest, W.R., Verhoef, J., Pilkington, M., Magnetic interpretation using the 3-D analytic signal (1992) Geophysics, 57 (1), pp. 116-125Smith, R.S., Salem, A., Imaging depth, structure, and susceptibility from magnetic data: The advance source-parameter imaging method (2005) Geophysics, 70 (4), pp. L31-L38Souza Filho, C.R., Nunes, A.R., Leite, E.P., Monteiro, L.V.S., Xavier, R.P., Spatial analysis of airborne geophysical data applied to geological mapping and mineral prospecting in the Serra Leste region, Carajás Mineral Province, Brazil (2007) Surv. Geophys., 28, pp. 377-405Suita, M.T.F., Nilson, A.A., Geologia do complexo máfico-ultram áfico Luanga (Província Mineral de Carajás, Pará) e das unidades encaixantes (1988) Anais XXXV Cong. Bras. Geol. Belém, 6, pp. 2813-2823. , In:Tallarico, F.H.B., Coimbra, C.R., Costa, C.H.C., The Serra Leste sediment-hosted Au-(Pd-Pt) mineralization, Carajás Province (2000) Rev. Bras. Geoc., 30 (2), pp. 226-229Tallarico, F.H.B., McNaughton, N.J., Groves, D.I., Fletcher, I.R., Figueiredo, B.R., Carvalho, J.B., Rego, J.L., Nunes, A.R., Geological and SHRIMP II U-Pb constraints on the age and origin of the Breves Cu- Au-(W-Bi-Sn) deposit, Carajás, Brazil (2004) Miner. Deposita, 39, pp. 68-86Thompson, D.T., EULDPH: A new technique for making computerassisted depth estimates from magnetic data (1982) Geophysics, 47 (1), pp. 31-37Thurston, J.B., Smith, R.S., Automatic conversion of magnetic data to depth, dip, and susceptibility contrast using the SPI - method (1997) Geophysics, 62 (3), pp. 807-813Trendall, A.F., Basei, M.A.S., De Laeter, J.R., Nelson, D.R., SHRIMP U-Pb constraints on the age of the Carajás Formation, Grão Pará Group, Amazon Craton (1998) J. South Am. Earth Sci., 11, pp. 265-277Wirth, K.R., Gibbs, A.K., Olszewski, W.J., U-Pb ages of zircons from the Grão Pará Group and Serra dos Carajás granite, Pará, Brazil (1986) Rev. Bras. Geoc., 16, pp. 195-200Yaghoobian, A., Boustead, G.A., Dobush, T.M., (1992) Object delineation using Euler's Homogeneity Equation, pp. 613-632. , Location and Depth Determination of Buried Ferro-Metallic Bodies. Proceedings SAGEEP 9

Targeting Of Gold Deposits In Amazonian Exploration Frontiers Using Knowledge- And Data-driven Spatial Modeling Of Geophysical, Geochemical, And Geological Data

This paper reports the application of weights-of-evidence, artificial neural networks, and fuzzy logic spatial modeling techniques to generate prospectivity maps for gold mineralization in the neighborhood of the Amapari Au mine, Brazil. The study area comprises one of the last Brazilian mineral exploration frontiers. The Amapari mine is located in the Maroni-Itaicaiúnas Province, which regionally hosts important gold, iron, manganese, chromite, diamond, bauxite, kaolinite, and cassiterite deposits. The Amapari Au mine is characterized as of the orogenic gold deposit type. The highest gold grades are associated with highly deformed rocks and are concentrated in sulfide-rich veins mainly composed of pyrrhotite. The data used for the generation of gold prospectivity models include aerogeophysical and geological maps as well as the gold content of stream sediment samples. The prospectivity maps provided by these three methods showed that the Amapari mine stands out as an area of high potential for gold mineralization. The prospectivity maps also highlight new targets for gold exploration. These new targets were validated by means of detailed maps of gold geochemical anomalies in soil and by fieldwork. The identified target areas exhibit good spatial coincidence with the main soil geochemical anomalies and prospects, thus demonstrating that the delineation of exploration targets by analysis and integration of indirect datasets in a geographic information system (GIS) is consistent with direct prospecting. Considering that work of this nature has never been developed in the Amazonian region, this is an important example of the applicability and functionality of geophysical data and prospectivity analysis in regions where geologic and metallogenetic information is scarce. © 2011 Springer Science+Business Media B.V.332211241Agterberg, F.P., Cheng, Q., Conditional independence test for weights-of-evidence modelling (2002) Nat Resour Res, 11 (4), pp. 249-255Agterberg, F.P., Bonham-Carter, G.F., Wright, D.F., Statistical pattern integration for mineral exploration (1990) Computer Applications in Resource Estimation: Prediction and Assessment for Metals and Petroleum, pp. 1-21. , G. Gaal and D. F. 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