CAREER: Tracking deep-time environmental change through statistical analyses of the sedimentary geochemical record

Stanford University, Stanford CA

Investigators

Abstract

The field of historical geobiology investigates how environments have changed on Earth over time and how life on Earth has responded to these changes. This history is archived in the geochemical record, and this grant utilizes an open international research consortium, the Sedimentary Geochemistry and Paleoenvironments Project (SGP), to both build and analyze this record. Specifically, SGP aims to assemble a large database of sedimentary geochemical records (e.g., hundreds of thousands of samples) using a unique ‘crowd sourcing’ approach. In addition to compiling published data, this grant will conduct new sedimentary geochemistry studies of under-studied time intervals; these projects will involve 16 undergraduate students. The database will then be analyzed to understand how primary productivity and oxygen levels in the ocean have changed through geological time. Activities in the grant will contribute to development of the SGP website, which is currently the portal to the largest publicly available database of sedimentary geochemical measurements. This open access data product is particularly useful for industries working in sedimentary rocks such as hydrocarbon and mineral exploration (base metal deposits, critical metals) as well as groundwater research. This proposal will use SGP data products to address two key questions in Earth history. First, how has organic carbon burial and food supply to the benthos changed through time (as proxied by shale total organic carbon (TOC) contents)? Second, how has the ocean’s redox landscape changed through Earth history (as proxied by redox-sensitive trace metal abundances in anoxic/euxinic black shales)? This proposal will test previous hypotheses generated from literal data analysis and smaller datasets by utilizing the large SGP database and sophisticated analytical methods, including re-weighted bootstrapping techniques to address spatio-temporal sampling heterogeneity and machine learning techniques to de-convolve the influence of multiple geological biases on a geochemical proxy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.