Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations.
1Information Solutions Unit - Geological Survey of Finland, Finland.
Methodsx
|March 4, 2022
Summary
This study optimizes fuzzy inference systems (FISs) for mineral prospectivity modeling using Monte Carlo simulations. This approach enhances gold exploration targeting by objectively defining parameters and quantifying uncertainties.
Area of Science:
- Geology
- Data Science
- Computational Intelligence
Background:
- Mineral prospectivity modeling traditionally relies on subjective expert knowledge for parameter selection in fuzzy inference systems (FISs).
- Gold mineralization in the Rajapalot project area, Finland, presents complex geological controls requiring advanced modeling techniques.
- Understanding the interplay of geodynamic systems and geological factors is crucial for effective exploration targeting.
Purpose of the Study:
- To estimate parameters of rule-based fuzzy inference systems (FISs) for mineral prospectivity modeling using Monte Carlo simulations.
- To develop and implement Mamdani-type FISs for predicting favorable structural and chemical settings for gold mineralization.
- To objectively define FIS parameters using drill core data statistics and Monte Carlo simulations for optimized prospectivity mapping.
Main Methods:
- Application of Monte Carlo simulations to estimate FIS parameters and quantify uncertainties.
- Development of Mamdani-type fuzzy inference systems (FISs) for predictive modeling of gold mineralization.
- Utilizing drill core data statistics to define fuzzification function parameters, moving beyond subjective expert input.
Main Results:
- Optimized fuzzy inference systems (FISs) for mineral prospectivity modeling, specifically for gold in the Rajapalot project area.
- Quantification of uncertainties associated with Mamdani-type FIS-based prospectivity modeling through Monte Carlo simulations.
- Generation of prospectivity maps at various confidence levels to support informed exploration target selection.
Conclusions:
- Monte Carlo simulations provide an objective method for parameter estimation and uncertainty quantification in FIS-based mineral prospectivity modeling.
- The developed FIS approach effectively captures complex geological processes and their interplay in mineralization.
- This methodology enhances decision-making for exploration target selection by providing reliable prospectivity assessments.
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