Application of artificially intelligent systems for the identification of discrete fossiliferous levels

PeerJ

article

David M. Martín-Perea¹  Lloyd A. Courtenay⁴  M. Soledad Domingo⁵  Jorge Morales¹ 

[1] Palaeobiology Department;Geodynamics, Stratigraphy and Palaeontology Department, Universidad Complutense de Madrid;Institute of Evolution in Africa;Department of Cartographic and Land Engineering, Higher Polytechnic School of Avila, University of Salamanca;Sciences, Social Sciences and Mathematics Department, Universidad Complutense de Madrid

关键词: Machine Learning;    Archaeological site;    Palaeontological site;    Spatial data;    Archaeostratigraphy;    Palaeostratigraphy;    Batallones Butte sites;   

DOI  :  10.7717/peerj.8767

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

The separation of discrete fossiliferous levels within an archaeological or paleontological site with no clear stratigraphic horizons has historically been carried out using qualitative approaches, relying on two-dimensional transversal and longitudinal projection planes. Analyses of this type, however, can often be conditioned by subjectivity based on the perspective of the analyst. This study presents a novel use of Machine Learning algorithms for pattern recognition techniques in the automated separation and identification of fossiliferous levels. This approach can be divided into three main steps including: (1) unsupervised Machine Learning for density based clustering (2) expert-in-the-loop Collaborative Intelligence Learning for the integration of geological data followed by (3) supervised learning for the final fine-tuning of fossiliferous level models. For evaluation of these techniques, this method was tested in two Late Miocene sites of the Batallones Butte paleontological complex (Madrid, Spain). Here we show Machine Learning analyses to be a valuable tool for the processing of spatial data in an efficient and quantitative manner, successfully identifying the presence of discrete fossiliferous levels in both Batallones-3 and Batallones-10. Three discrete fossiliferous levels have been identified in Batallones-3, whereas another three have been differentiated in Batallones-10.

【 授权许可】

CC BY