Provenance Analysis Based on Cluster In‐Betweenness and Support Vector Machines: Identifying Migrant Candidates Using Multi‐Isotope Fingerprints

ABSTRACT Provenance reconstruction using strontium and lead stable isotopes can produce complex multidimensional fingerprints, challenging traditional methods. Identifying nonlocals, who migrated between sites, is a major task. Migrants are identifiable by divergent multi‐isotope fingerprints due to isotopic mixing between origin and destination sites. Detecting early migrants, however, is possible in exceptional cases only. This study used a Support Vector Machine approach to detect migrants as “in‐between instances” between sites. Tested on synthetic data, the model effectively identified IBIs under varying conditions. Applied to archaeological multi‐isotope data, it revealed nonlocal individuals, also early migrants, including the approximated years since migration.

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Publication Details

Journal
Archaeometry
Published
2025-12-17
DOI
https://doi.org/10.1111/arcm.70084
Primary Topic
Archaeology and ancient environmental studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Provenance Analysis Based on Cluster In‐Betweenness and Support Vector Machines: Identifying Migrant Candidates Using Multi‐Isotope Fingerprints

Andrea Göhring, Daniyal Kazempour, Matthias Renz, Claudius Zelenka et al.
Archaeometry
Archaeology and ancient environmental studies
article

Provenance Analysis Based on Cluster In‐Betweenness and Support Vector Machines: Identifying Migrant Candidates Using Multi‐Isotope Fingerprints

Andrea Göhring, Daniyal Kazempour, Matthias Renz, Claudius Zelenka, Gisela Grupe, Sweety Mohanty, Peer Kröger, Amei Lang, Jan Robert Bensien
article en

Abstract

ABSTRACT Provenance reconstruction using strontium and lead stable isotopes can produce complex multidimensional fingerprints, challenging traditional methods. Identifying nonlocals, who migrated between sites, is a major task. Migrants are identifiable by divergent multi‐isotope fingerprints due to isotopic mixing between origin and destination sites. Detecting early migrants, however, is possible in exceptional cases only. This study used a Support Vector Machine approach to detect migrants as “in‐between instances” between sites. Tested on synthetic data, the model effectively identified IBIs under varying conditions. Applied to archaeological multi‐isotope data, it revealed nonlocal individuals, also early migrants, including the approximated years since migration.

Archaeometry
Hochschule für Angewandte Wissenschaften Kiel (DE), Christian-Albrechts-Universität zu Kiel (DE), Institute of Archaeology (CN), Ludwig-Maximilians-Universität München (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 80%
Archaeology and ancient environmental studies
DOIOpen Access PDFOpenAlex
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Provenance Analysis Based on Cluster In‐Betweenness and Support Vector Machines: Identifying Migrant Candidates Using Multi‐Isotope Fingerprints — Andrea Göhring, Daniyal Kazempour, et al. · Archaeometry (2025) | TGRS Research Map | TGRS