Capturing Hidden Geochemical Anomalies in Scarce Data by Fractal Analysis and Stochastic Modeling
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Date
2018
Authors
Madani, Nasser
Sadeghi, Behnam
Journal Title
Journal ISSN
Volume Title
Publisher
Natural Resources Research
Abstract
Fractal/multifractal modeling is a widely used geomathematical approach to capturing different
populations in geochemical mapping. The rationale of this methodology is based on
empirical frequency density functions attained from global or local distributions. This approach
is quite popular because of its simplicity and versatility; it accounts for the frequency
and spatial distribution of geochemical data considering self-similarity across a range of
scales. Using this technique for detection of geochemical anomalies in scarce data, however,
is problematic and can lead to systematic bias in the characterization of the underlying
populations. In this paper, an innovative technique is presented that provides good results
without a priori assumptions. A simulation approach is adopted for fractal analysis by
generating different possible distribution scenarios for the variable under study to reveal the
underlying populations that are frequently hidden due to lack of data. The proposed technique
is called the global simulated size–number method, and it is validated in a case study
with two synthetic datasets and another case study with real dataset from the Ushtagan gold
deposit in northeast Kazakhstan.
Description
Keywords
Fractal modeling, Monte Carlo simulation, Kernel density function, Ushtagan gold deposit
Citation
1) Madani, N., Sadeghi, B. (2018). “Capturing Hidden Geochemical Anomalies in Scarce Data by Fractal Analysis and Stochastic Modeling”. Natural Resources Research, DOI: 10.1007/s11053-018-9421-4. In press.