Abstract
Modeling geological units such as dykes is a powerful tool in mineral exploration studies. The most significant issue in modeling of uncertainty in the mineral exploration studies is the identification of the geological domains that consist of the zones with significant exploratory factor located in the area under study. The sequential indicator simulation (SIS) algorithm has the ability to model lithological facies. Although the two-point geostatistical simulation approaches are simple, they have some substantial drawbacks. For instance, they do not take into account the complex and nonlinear continuities in geological units like a swarm of the dyke. Therefore, if the area under study has complex geological units, it is necessary to improve the SIS algorithm considering the spatial patterns of complex and nonlinear structures. The pattern recognition method is an effective approach that can improve the two-point geostatistical simulation algorithm. It is based on the informative complex geological spatial patterns that contain the connectivity and nonlinear structures. This paper proposes a hybrid approach (PR-SIS) for simulation of geological units in the presence of several nonlinear structures. To perform hybrid approach, the SIS and pattern recognition methods were used in a combined form. To this end, the results of two methods were combined by the Dempster–Shafer theory (DST) approach. The main purpose of this paper is to investigate the impact of the proposed hybrid approach for modeling the dykes of the Sungun porphyry copper deposit (Iran). To this end, two evidences have been used including the sequential indicator simulation (SIS) method and the pattern recognition approach. These evidences were then applied for using the DST to obtain the final accuracy of the model. Final modeling of this approach shows the continuity of dykes in each location and optimal check with respect to the DST approach based on the geological map. The overall accuracy (OA) and the kappa index (KI) in different dimensions of the search windows showed that the dykes modeling using the hybrid approach by the DST provide a better accuracy. In this regard, the values of OA and KI were 0.889 and 0.792 and 0.901 and 0.805, as well as 0.982 and 0.891 for the SIS method, the pattern recognition method using 2.5 × 2.5 search window, and the proposed hybrid approach by the DST, respectively.
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Abbreviations
- ANN:
-
artificial neural network
- Bel:
-
Belief function
- BPA:
-
basic probability mass function
- CDF:
-
cumulative distribution function
- DK:
-
non-mineralized dyke
- DST:
-
Dempster–Shafer theory
- IK:
-
indicator kriging
- m:
-
meter
- MLP:
-
multilayer perceptron
- MPS:
-
multiple-point geostatistical simulation
- MSE:
-
mean squared error
- NW:
-
northwestern
- OA:
-
overall accuracy
- PGS:
-
plurigaussian simulation
- PL:
-
Plausibility function
- RBF:
-
radial basis function
- SIS:
-
sequential indicator simulation
- SK:
-
skarn
- SOM:
-
self-organizing map
- SP:
-
Sungun porphyry
- KI:
-
kappa index
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Hosseini, S.T., Asghari, O. & Pahlavani, P. A hybrid approach to model the dykes in Sungun porphyry copper deposit using Dempster–Shafer theory. Arab J Geosci 13, 1279 (2020). https://doi.org/10.1007/s12517-020-06241-6
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DOI: https://doi.org/10.1007/s12517-020-06241-6