Neural Network-Based Classification of XRF Profiles Of Pottery Shards Using Synthetic Data

Ankita Nandy

Independent Researcher, Hooghly, West Bengal, India


Vol: 13, Issue: 3, 2023

Receiving Date: 2023-06-17 Acceptance Date:


Publication Date:


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Across all archaeological sites, pottery fragments have been abundant and insightful resources for understanding the societies which crafted, traded and/or used them. Their chemical profiles generated using X-ray fluorescence (XRF) techniques are used to characterize the raw materials and manufacturing techniques, and thus map them to their potential origins. Numerous researchers have published their XRF analysis results for pottery shards across the world, which exist as isolated small datasets. The collation of these datasets and subsequent usage in training classifiers aids in studying potential migration routes and trade and diplomatic relations which have bridged civilizations. Artificial Neural Networks (ANNs) have gained popularity across all domains considering their versatility in learning non-linear, complex patterns. However, training them requires large datasets which is less common in the field of archaeology. To solve the problem posed by data availability, the classifier ANN is trained with synthetically generated data. The 297 XRF records, when added to synthetically generated data, swelled to over 13k records, and could classify pottery shards across 11 geographies with an accuracy of over 80 per cent.

Keywords: pottery shards; XRF; synthetic data; classification; neural networks


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