Carbonate mud represents one of the most important geochemical archives for reconstructing ancient climatic, environmental, and evolutionary change from the rock record. Mud also represents a major sink in the global carbon cycle. Yet, there remains no consensus about how and where carbonate mud is formed. Here, we present stable isotope and trace-element data from carbonate constituents in the Bahamas, including ooids, corals, foraminifera, and algae. We use geochemical fingerprinting to demonstrate that carbonate mud cannot be sourced from the abrasion and mixture of any combination of these macroscopic grains. Instead, an inverse Bayesian mixing model requires the presence of an additional aragonite source. We posit that this source represents a direct seawater precipitate. We use geological and geochemical data to show that “whitings” are unlikely to be the dominant source of this precipitate and, instead, present a model for mud precipitation on the bank margins that can explain the geographical distribution, clumped-isotope thermometry, and stable isotope signature of carbonate mud. Next, we address the enigma of why mud and ooids are so abundant in the Bahamas, yet so rare in the rest of the world: Mediterranean outflow feeds the Bahamas with the most alkaline waters in the modern ocean (>99.7th-percentile). Such high alkalinity appears to be a prerequisite for the nonskeletal carbonate factory because, when Mediterranean outflow was reduced in the Miocene, Bahamian carbonate export ceased for 3-million-years. Finally, we show how shutting off and turning on the shallow carbonate factory can send ripples through the global climate system.
Accurately assessing the shape, size, and modality of features in rock samples is a longstanding problem in geology. Recent advances in machine learning have introduced the possibility of performing these tasks through automated image analysis. To leverage these methods for geological and paleontological applications, we first need a way to acquire high-resolution images of polished slabs and thin sections with a field of view large enough to fit samples containing crystals, fossils, bedforms, etc. We describe a new multispectral setup that can acquire images at ~3.76 mm per pixel spatial resolution over a 21 cm 2 field of view, equipped with 8-band (470-940 nm) spectral resolution, plus a band for ultraviolet (365 nm) fluorescence. Additionally, we present a 5-band (470-940 nm) light table with automated rotating polarizers, which allows use of the camera as a high-throughput transmitted light thin section imager. The use of color bands outside the visible spectrum, as well as the registration of multiple cross-polarized rotations, encode rock properties that heighten image contrast and improve the accuracy of machine learning models. Our setup and methods provide an efficient way to (1) build reproducible image archives of rock specimens to complement field observations, (2) classify and segment those images, and (3) quantitatively compare lithofacies and fossil assemblages.
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