Machine learning application to layer counting in speleothems

Abstract

Some speleothems, in particular stalagmites, are laminated at the visible and microscopic scale, with the latter visible using fluorescence microscopy (e.g., confocal laser scanning microscopy). These laminations can be used to supplement speleothem chronologies, although this process is laborious and lateral variations in lamination geometry and quality necessitate a detailed look over an entire scan as opposed to a simple one-dimensional transect. In order to assist this process, we develop a classification-based machine learning algorithm using an open-source machine learning package. This algorithm is optimized for stalagmites growing at 20–100 μm yr−1 and outputs a 2-dimensional layer density map which may aid in quantitatively interpreting past variations in speleothem growth rate. This algorithm requires user supervision and interpretation, as image artefacts and magnification settings may complicate model output.