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    Carbonate factory response and recovery after Ocean Anoxic Event 1a, Pearsall Formation, Central Texas

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    PEDERSEN-THESIS-2020.pdf (9.196Mb)
    Date
    2020-08-13
    Author
    Pedersen, Esben Skjold
    0000-0001-7893-2826
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    Abstract
    Ocean Anoxic Events (OAEs) are major carbon cycle perturbations that occurred several times in the Mesozoic. OAEs are commonly found to have been caused by a combination of climatic warming and increased surface weathering delivering surface nutrients to the oceans. This feedback loop leads to the expansion of the oxygen minimum zone of the waterbody and increased influx of terrigenous material. The resultant dysoxic to euxinic conditions are thought to have played a prominent role in the suppression of the benthic carbonate factory and deposition of organic-rich mudstones. The establishment of these oceanographic conditions are postulated to have imparted a lasting effect on the deposition of stressed-carbonate facies during the recovery phase of OAEs. Major questions regarding OAE events remain, including the degree of variability in the impact that OAEs have on carbonate factories and the drivers for this variability, on both global and regional scales. This study builds upon previous work and further investigates the regional Early Cretaceous (Aptian) OAE-1a signal that is recorded in the Pearsall Formation in Central Texas, with a particular focus on the record of carbonate factory recovery observed in transects from the San Marcos Arch to the Pearsall Arch. Shoreline-proximal data include outcrops and 8 cores with 1530 ft of coverage. Distal cores include 7 subsurface exploration wells (total 1745 ft core) from the San Marcos Arch to the Pearsall Arch, a strike-parallel distance of 210 km. Physical characterization of stratigraphic data was paired with the multivariate statistical analysis of 10 pXRF datasets, involving Principal Component Analysis (PCA) segmentation, which led to the establishment of five end member chemofacies. These chemofacies allow for high-resolution identification of mineralogic variability across OAE-1a, including the documentation of pulses of terrigenous input as well as cycles of dysoxic to euxinic oceanographic conditions at a sub-lithofacies scale. When paired with the development and application of a deep learning neural network trained by a type-pXRF training dataset, this study outlines a new methodology that allows for the direct comparison of pXRF data across core control through a unified chemofacies schema. The oceanographic conditions identified with this workflow are then used to delineate oceanographic variability and pulses of terrigenous enrichment in association with the recovery from OAE-1a. The characterization of these geochemical processes is particularly relevant in the mudrock component of depositional systems, where biologic productivity, bottom-water redox conditions, and any subsequent diagenesis are critical determinants for the ultimate preservation of TOC in organic-rich shales. TOC rich shale intervals then create potential for an economical petroleum source rock and successive charge of either conventional or unconventional reservoirs. The incidence of OAE-1a is found to be a fundamental driver of facies evolution and faunal composition in the three composite sequences studied: the James (Aptian) composite sequence, the Bexar (Aptian-Albian) composite sequence, and the Glen Rose composite sequence (Albian) (cf. Phelps et al., 2014). OAE-1 is coincident with the drowning of the antecedent Sligo reef margin and deposition of the Pine Island Shale. This drowning event was a result of environmental stressors posed by the OAE and the resultant suppression of sedimentation rates on the platform as the carbonate factory was substantially weakened. Partial recovery of the carbonate factory from OAE-1a is expressed in the deposition of the Cow Creek Member before punctuation of deposition due to the subaerial exposure event at the top-James composite sequence boundary. A second phase of recovery is documented in the Bexar and Glen Rose composite sequences, including reef systems in the platform interior that are coeval with transgression and deposition of the Hensel Formation, as well as the progradation of Lower Glen Rose carbonates and the aggradation of microbial-coral-rudist bioherms in highstand depositional sequences of the Glen Rose Formation. Recovery of the carbonate factory was fundamentally different between the San Marcos Arch and Pearsall Arch areas. Earliest recovery fauna in the Cow Creek Member is comprised of monospecific echinoid-mollusk packstones-grainstones in shoreline proximal settings and oyster-oncoid rudstones distally. Combined observations from pXRF data and the heightened prevalence of pyrite in oncoid cortices on the San Marcos Arch compared to the Pearsall area is interpreted to represent a higher degree of dysoxic and/or euxinic conditions on the San Marcos Arch. During later stages of recovery, the Cow Creek in the Pearsall Arch area is shown to have maintained healthier carbonate deposition in comparison to the San Marcos Arch, including the sustained deposition of reefal assemblages, such as the sequence of stromatoporoid boundstone present in the Tenneco Sirianni well. This combined core-outcrop framework demonstrates the superimposed regional variability inherent even in global carbon cycle perturbations such as OAE-1a, driven by the degree of shelf restriction, oceanographic circulation patterns, basin geometry, and the degree of terrigenous influx. The documented differences in oceanographic conditions and carbonate factory recovery on the regional scale of OAE-1a will aid in better understanding the multi-scaled geochemical and environmental evolution associated with these events, and ultimately pushes towards the development of predictive concepts for future studies.
    Department
    Geological Sciences
    Subject
    Carbonate stratigraphy
    Cretaceous
    Sequence stratigraphy
    Oceanic Anoxic Event
    OAE
    Central Texas
    Pearsall
    South Texas
    XRF
    pXRF
    Chemofacies
    Machine learning
    Neural network
    X-ray fluorescence
    URI
    https://hdl.handle.net/2152/83778
    http://dx.doi.org/10.26153/tsw/10773
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