A Hybrid Geometric Morphometric Deep Learning Approach for Cut and Trampling Mark Classification

Abstract

The concept of equifinality is currently one of the largest issues in taphonomy, frequently leading analysts to erroneously interpret the formation and functionality of archaeological and paleontological sites. An example of this equifinality can be found in the differentiation between anthropic cut marks and other traces on bone produced by natural agents, such as that of sedimentary abrasion and trampling. These issues are a key component in the understanding of early human evolution, yet frequently rely on qualitative features for their identification. Unfortunately, qualitative data is commonly susceptible to subjectivity, producing insecurity in research through analyst experience. The present study intends to confront these issues through a hybrid methodological approach. Here, we combine Geometric Morphometric data, 3D digital microscopy, and Deep Learning Neural Networks to provide a means of empirically classifying taphonomic traces on bone. Results obtained are able to reach over 95% classification, providing a possible means of overcoming taphonomic equifinality in the archaeological and paleontological register.