A computer vision algorithm for interpreting lacustrine carbonate textures at Searles Valley, USA. (September 2022)

• Record Type: Journal Article
• Authors: Fendrock, Michaela; Chen, Christine Y.; Olson, Kristian J.; Lowenstein, Tim K.; McGee, David
• Is Part Of: Computers & geosciences. Volume 166(2022)
• Publication Date: 2022-09
• Subjects: Machine learning -- Computer vision -- Carbonates -- Paleoclimate; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2022.105142
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Investigations of the paleohydrologies of pluvial lake systems have often employed lake carbonate deposits called "tufa" that grow subaqueously and can be preserved long after the drying of the lake. For this reason, tufa have been used as a proxy for minimum lake level. However, they exhibit a variety of textures that hold the potential to reveal richer paleoclimatological information. With the goal of determining if tufa texture can be used as a proxy for lake environment, this study investigates the textures of tufa at Mono Lake, California in comparison to the fossil tufa in Searles Valley, California. While observations in the last century suggest that the tufa in the Mono basin grew in waters similar to the modern, the tufa at Searles formed during the last glacial period, when the Great Basin contained a system of pluvial lakes on the scale of the modern Great Lakes. The tufa at both basins have been observed to have a range of classifiable textures, and new methods of inspecting visual data could be informative about what factors control these textures. To this end, a t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm is used to project images of the tufa at Searles and Mono into a coordinate space, allowing for simple, quantitative comparisons of the visual similarity of textures. The textures of tufa at Searles are compared to each other, as well as to the tufa at Mono. This study performs a robust assessment of the feasibility of Mono Lake as a modern analogue for Searles Valley. It finds that there is a justifiable basis for the comparison of certain fossil facies at Searles to the tufa at Mono, significant progress towards the goal of using texture as a metric for the environment in which tufa formed. Highlights: Computer vision is used to compare facies of tufa at Searles and Mono Lakes, California, USA. According to this comparison, the tufa at Mono Lake are most visually similar to the "columnar" facies at Searles. This is significant progress towards the goal of using tufa texture as a metric for tufa formation environment. Studies of this kind could be used in future to distinguish between geologic facies including, but not limited to, tufa in other basins.