ReefCoreSeg: A Clustering-Based Framework for Multi-Source Data Fusion for Segmentation of Reef Drill Cores

Abstract

Coral reefs are among the most biologically diverse and economically valuable ecosystems on Earth, but they are threatened by climate change. Understanding how reefs developed over geological timescales can provide important information about past environmental changes and their impacts on reef systems. Significant effort and capital have been invested in drilling and analyzing reef cores. Recognizing coral and sediment patterns visually from fossil reefs is a laborious task that demands domain expertise. In this paper, we present a machine learning-based framework that utilizes clustering and classification methods to fuse multiple sources of data for the segmentation and annotation of reef cores. The framework produces an annotated image of a reef core with six lithologies identified; massive corals, encrusted corals, coralline algae, microbialite, sand, and silt. We utilize reef cores recovered from Expedition 325 of the International Ocean Discovery Program (IODP) to the Great Barrier Reef. We use reef core image data and physical properties data to segment reef cores. We evaluate the framework using selected clustering and classification models. The results show that Gaussian mixture models can provide accurate segmentation of reef core image data, with a clear visual distinction between two major classes: massive corals and stromatolitic microbialites. Furthermore, we find that the random forest classifier provides the best annotations for the segmented reef core image data with an accuracy of 96%.

Keywords

• Clustering
• segmentation
• multi-source data
• classification
• reef core analysis
• Gaussian mixture models