Capturing Hidden Geochemical Anomalies in Scarce Data by Fractal Analysis and Stochastic Modeling

Nasser Madani, Behnam Sadeghi

Research output: Contribution to journalArticle

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.
Original languageEnglish
Pages (from-to)1-15
Number of pages16
JournalNatural Resources Research
Publication statusAccepted/In press - 2018

Fingerprint

fractal analysis
anomaly
modeling
gold
spatial distribution
methodology
simulation
distribution

Keywords

  • Fractal modeling
  • Monte Carlo simulation
  • Kernel density function
  • Ushtagan gold deposit

Cite this

Capturing Hidden Geochemical Anomalies in Scarce Data by Fractal Analysis and Stochastic Modeling. / Madani, Nasser; Sadeghi, Behnam.

In: Natural Resources Research, 2018, p. 1-15.

Research output: Contribution to journalArticle

@article{d489678c0e864bffb3b966e03d9450d5,
title = "Capturing Hidden Geochemical Anomalies in Scarce Data by Fractal Analysis and Stochastic Modeling",
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.",
keywords = "Fractal modeling, Monte Carlo simulation, Kernel density function, Ushtagan gold deposit",
author = "Nasser Madani and Behnam Sadeghi",
year = "2018",
language = "English",
pages = "1--15",
journal = "Natural Resources Research",
issn = "1520-7439",
publisher = "Springer Netherlands",

}

TY - JOUR

T1 - Capturing Hidden Geochemical Anomalies in Scarce Data by Fractal Analysis and Stochastic Modeling

AU - Madani, Nasser

AU - Sadeghi, Behnam

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Fractal modeling

KW - Monte Carlo simulation

KW - Kernel density function

KW - Ushtagan gold deposit

M3 - Article

SP - 1

EP - 15

JO - Natural Resources Research

JF - Natural Resources Research

SN - 1520-7439

ER -