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A robust interpolation-based method for forensic soil provenancing: A Bayesian likelihood ratio approach.

Michael G Aberle1, Patrice de Caritat2, James Robertson1

  • 1National Centre for Forensic Studies, University of Canberra, Bruce, Australian Capital Territory 2617, Australia.

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|November 17, 2023
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Summary
This summary is machine-generated.

Soil provenancing in urban areas can spatially constrain police searches. A new likelihood ratio approach using geochemical data provides robust soil evidence for forensic casework, improving resource allocation and intelligence operations.

Keywords:
Forensic scienceInterpolationLikelihood RatioProvenancingSimilaritySoil forensics

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Area of Science:

  • Forensic Science
  • Geochemistry
  • Geospatial Analysis

Background:

  • Soil is a complex material with significant potential as evidence in forensic casework.
  • Accurate soil provenancing is crucial for spatially constraining police search areas and prioritizing resources.
  • Existing methods for soil provenancing often rely on arbitrary thresholds, limiting their effectiveness.

Purpose of the Study:

  • To document the distribution of chemical elements in an urban/suburban environment for forensic soil provenancing.
  • To develop and evaluate a new likelihood ratio approach for soil provenancing that avoids arbitrary thresholds.
  • To demonstrate a method for upscaling interpolated soil data grids and ranking variables for provenancing.

Main Methods:

  • Conducted a topsoil survey (0-5 cm depth) in Canberra, Australia, collecting 685 samples.
  • Utilized X-Ray Fluorescence (XRF) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) for geochemical analysis.
  • Developed a likelihood ratio approach using Cauchy distributions to assess analytical similarity between soil samples and evidentiary material.

Main Results:

  • The new likelihood ratio approach provides an unconstrained, gradual transition based on analytical similarity, unlike threshold-based methods.
  • A novel method for upscaling fine to coarser interpolated grids (25m to 500m) was successfully evaluated.
  • Objective ranking of variables using Random Match Probabilities improved the consistency and robustness of provenance maps.

Conclusions:

  • The developed soil provenancing model offers a more consistent and robust method for forensic casework.
  • The approach enhances the utility of soil as evidence by providing a more nuanced assessment of similarity.
  • This research provides a valuable tool for law enforcement and intelligence operations requiring spatial analysis of soil evidence.