Nonrenewable Resources

, Volume 7, Issue 1, pp 7–24 | Cite as

Bayesian and multivariate methods applied to favorability quantification in Recôncavo Basin and Ribeira Belt, Brazil

  • Sidnei Pires Rostirolla
  • Paulo César Soares
  • Hung Kiang Chang


A methology to define favorable areas in petroleum and mineral exploration is applied, which consists in weighting the exploratory variables, in order to characterize their importance as exploration guides. The exploration data are spatially integrated in the selected area to establish the association between variables and deposits, and the relationships among distribution, topology, and indicator pattern of all variables. Two methods of statistical analysis were compared. The first one is the Weights of Evidence Modeling, a conditional probability approach (Agterberg, 1989a), and the second one is the Principal Components Analysis (Pan, 1993). In the conditional method, the favorability estimation is based on the probability of deposit and variable joint occurrence, with the weights being defined as natural logarithms of likelihood ratios. In the multivariate analysis, the cells which contain deposits are selected as control cells and the weights are determined by eigendecomposition, being represented by the coefficients of the eigenvector related to the system’s largest eigenvalue. The two techniques of weighting and complementary procedures were tested on two case studies: 1. Recôncavo Basin, Northeast Brazil (for Petroleum) and 2. Itaiacoca Formation of Ribeira Belt, Southeast Brazil (for Pb-Zn Mississippi Valley Type deposits). The applied methdology proved to be easy to use and of great assistance to predict the favorability in large areas, particularly in the initial phase of exploration programs.

Key Words

Geomathematics deposit modeling mineral prospection petroleum exploration 


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Copyright information

© International Association for Mathematical Geology 1991

Authors and Affiliations

  • Sidnei Pires Rostirolla
    • 1
  • Paulo César Soares
    • 1
  • Hung Kiang Chang
    • 2
  1. 1.Geology DepartmentUniversidade Federal do Paraná (UFPR)CuritibaBrazil
  2. 2.Applied Geology DepartmentUniversidade Estadual Paulista (UNESP)Rio ClaroBrazil

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