A novel multi-scale μCT characterization method to quantify biogenic carbonate production

Abstract

Biogenic carbonate structures such as rhodoliths and foraminiferal-algal nodules are a significant part of marine carbonate production and are being increasingly used as paleoenvironmental indicators for predictive modeling of the global carbon cycle and ocean acidification research. However, traditional methods to characterize and quantify the carbonate production of biogenic nodules are typically limited to two-dimensional analysis using optical and electron microscopy. While micro-computed tomography (µCT) is an excellent tool for 3D analysis of inner structures of geomaterials, the trade-off between sample size and image resolution is often a limiting factor. In this study, we address these challenges by using a novel multi-scale µCT image analysis methodology combined with electron microscopy, to visualize and quantify the carbonate volumes in a biogenic calcareous nodule. We applied our methodology to a foraminiferal-algal nodule collected from the Red Sea along the coast of NEOM, Saudi Arabia. Integrated µCT and SEM image analyses revealed the main biogenic carbonate components of this nodule to be encrusting foraminifera (EF) and crustose coralline algae (CCA). We developed a multi-scale µCT analysis approach for this study, involving a hybrid thresholding and machine-learning based image segmentation. We utilized a high resolution µCT scan from the sample as a ground-truth to improve the segmentation of the lower resolution full volume µCT scan which provided reliable volumetric quantification of the EF and CCA layers. Together, the EF and CCA layers contribute to approximately 65.5 % of the studied FAN volume, corresponding to 69.01 cm3 and 73.32 cm3 respectively, and the rest is comprised of sediment infill, voids and other minor components. Moreover, volumetric quantification results in conjunction with CT density values, indicate that the CCA layers are associated with the highest amount of carbonate production within this foraminiferal-algal nodule. The methodology developed for this study is suitable for analyzing biogenic carbonate structures for a wide array of applications including quantification of carbonate production and studying the impact of ocean acidification on skeletal structures of marine calcifying organisms. In particular, the hybrid µCT image analysis we adopted in this study proved to be advantageous for the analysis of biogenic structures in which the textures and components of the internal layers are distinctly visible despite having an overlap in the range of CT density values.

Keywords

• Crustose coralline algae
• Foraminifera
• µCT
• Image analysis
• Machine learning
• Marine carbonate factory