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Related Concept Videos

Noncompartmental Analysis: Statistical Moment Theory00:56

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Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
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Statistical Modeling of the Seismic Moments via Mathai Distribution.

Pedro Vega-Jorquera1, Erick De la Barra1,2, Héctor Torres2

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Entropy (Basel, Switzerland)
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Summary
This summary is machine-generated.

We introduce the Pareto-Mathai distribution to model earthquake magnitudes in mining. This new statistical model helps estimate the completeness magnitude (Mc) for microseism analysis in the mining industry.

Keywords:
completeness magnitudeparametric statisticsseismic momentssuperstatistics

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Area of Science:

  • Statistical modeling
  • Geophysics
  • Data analysis

Background:

  • Mathai's pathway model is a versatile tool for complex systems.
  • Earthquake magnitudes in full catalogues are often modeled by Mathai distributions.
  • Understanding microseisms in mining requires accurate magnitude analysis.

Purpose of the Study:

  • To construct and analyze the novel Pareto-Mathai distribution.
  • To apply this distribution to microseism data from the mining industry.
  • To estimate the completeness magnitude (Mc) for seismic data.

Main Methods:

  • Generalization of existing distributions using the pathway model.
  • Development of the Pareto-Mathai distribution.
  • Fitting the distribution to earthquake magnitude data.
  • Estimation of completeness magnitude (Mc).

Main Results:

  • The Pareto-Mathai distribution effectively models earthquake magnitudes.
  • The model allows for the calculation of completeness magnitude (Mc).
  • Application to a Chilean mine provided sector-specific and overall Mc estimates.

Conclusions:

  • The Pareto-Mathai distribution is a valuable tool for analyzing microseisms in mining.
  • Accurate Mc estimation is crucial for seismic monitoring in industrial settings.
  • This model enhances the understanding of seismic activity related to mining operations.