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

作　　者：V.Chandra R.Sicat F.Benzoni V.Vahrenkamp V.Bracchi 

机构地区：[1]Physical Sciences and Engineering,King Abdullah University of Science and Technology,Thuwal,Saudi Arabia [2]Visualization Core Lab,King Abdullah University of Science and Technology,Thuwal,Saudi Arabia [3]Biological and Environmental Science and Engineering,King Abdullah University of Science and Technology,Thuwal,Saudi Arabia [4]Department of Earth and Environmental Sciences,University of Milano-Bicocca,Milan,Italy [5]Saudi Aramco,Dhahran,Saudi Arabia

出　　处：《Geoscience Frontiers》2024年第6期43-56,共14页地学前缘（英文版）

基　　金：ANPERC and Vahrenkamp group lab for supporting sample preparation required for petrography and SEM analysis of the FAN sample NTN0035-17A.

摘　　要：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(lCT)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 lCT 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 lCT 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 lCT analysis approach for this study,involving a hybrid thresholding and machine-learning based image segmentation.We utilized a high resolution lCT scan from the sample as a ground-truth to improve the segmentation of the lower resolution full volume lCT 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 car

关 键 词：Crustose coralline algae FORAMINIFERA μCT Image analysis Machine learning Marine carbonate factory 

分 类 号：P618.13[天文地球—矿床学]