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Quantitative Matching of Forensic Evidence Fragments Using Fracture Surface Topography and Statistical Learning

NCJ Number
309449
Journal
Nature Communications Volume: 15 Issue: 7852 Dated: Sept 2024
Author(s)
Geoffrey Z. Thompson; Bishoy Dawood; Tianyu Yu; Barbara K. Lograsso; John D. Vanderkolk; Ranjan Maitra; William Q. Meeker; Ashraf F. Bastawros
Date Published
September 2024
Length
12 pages
Annotation

The authors of this paper propose using the fractal nature of fracture surface topography and their transition to non-self-affine properties to define an appropriate comparison scale; they also report on efforts to develop supporting statistical methods for forensic fracture matching through three-dimensional topological imaging of fracture surface details.

Abstract

The complex jagged trajectory of fractured surfaces of two pieces of forensic evidence is used to recognize a “match” by using comparative microscopy and tactile pattern analysis. The material intrinsic properties and microstructures, as well as the exposure history of external forces on a fragment of forensic evidence have the premise of uniqueness at a relevant microscopic length scale (about 2–3 grains for cleavage fracture), wherein the statistics of the fracture surface become non-self-affine. The authors utilize these unique features to quantitatively describe the microscopic aspects of fracture surfaces for forensic comparisons, employing spectral analysis of the topography mapped by three-dimensional microscopy. Multivariate statistical learning tools are used to classify articles and result in near-perfect identification of a “match” and “non-match” among candidate forensic specimens. The framework has the potential for forensic application across a broad range of fractured materials and toolmarks, of diverse texture and mechanical properties. (Published Abstract Provided)