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Lower Paleocene deposits in the San Juan Basin document one of the best records of mammalian change and turnover following the Cretaceous-Paleogene extinctions and are the type section for the Puercan (Pu) and Torrejonian (To) North America Land Mammal age biozones (NALMA). One of the largest mammalian turnover events in the early Paleocene occurs between the Torrejonian 2 (To2) and Torrejonian 3 (To3) NALMA biozones. The Nacimiento Formation are the only deposits in North America where the To2-To3 mammalian turnover can be constrained, however the precise age and duration of the turnover is poorly understood due to the lack of a precise chronostratigraphic framework. We analyzed paleomagnetic samples, produced a 40Ar/39Ar detrital sanidine age, and developed a detailed lithostratigraphy for four sections of the upper Nacimiento Formation in the San Juan Basin, New Mexico (Kutz Canyon, Escavada Wash, Torreon West and East) to constrain the age and duration of the deposits and the To2-To3 turnover. The polarity stratigraphy for the four sections can be correlated to chrons C27r-C26r of the geomagnetic polarity time scale (GPTS). Using the local polarity stratigraphy for each section, we calculated a mean sediment accumulation rate and developed a precise age model, which allows us to determine the age of important late Torrejonian mammalian localities. Using the assigned ages, we estimate the To2-To3 turnover was relatively rapid and occurred over ~120 kyr (-60/+50 kyr) between 62.59 and 62.47 Ma. This rapid duration of the mammalian turnover suggests that it was driven by external forcing factors, such as environmental change driven by the progradation of the distributive fluvial system across the basin and/or changes in regional or global climate. Additionally, comparisons of the mean sediment accumulation rates between the sections that span from the basin margin to the basin center indicate that sediment accumulation rates equalized across the basin from the end of C27r through the start of C26r, suggesting an accommodation minima in the basin associated with the progradation of a distributive fluvial system into the basin. This accommodation minimum also likely led to the long hiatus of deposition between the Paleocene Nacimiento Formation and the overlying Eocene San Jose Formation.

Detailed analysis of volcanic thermal and gas emissions over time can constrain subsurface processes throughout the pre-and post-eruption phases. Time series analyses are commonly applied to high temporal datasets like the Moderate Resolution Imaging Spectroradiometer (MODIS); however, this is the first study using the entire Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) twenty-plus year archive. The ASTER archive presents a unique opportunity to quantify volcanic precursors and processes. The spatial, spectral, and radiometric resolution of its thermal infrared (TIR) subsystem allows detection of very low-magnitude surface temperature anomalies and passively emitted small gas plumes. We developed a new statistical algorithm to automatically detect these subtle anomalies and applied it to five recently active volcanoes with well-documented eruptions: Taal (Philippians), Popocatépetl (Mexico), Mt. Etna (Italy), Fuego (Guatemala), and Kluichevskoi (Russia). More than 3,300 ASTER level-1 terrain corrected (L1T), registered, radiance-at-sensor images were downloaded from the NASA EARTHDATA website. These were screened for significant summit cloud coverage, which removed approximately 25% of scenes. The remaining were converted to brightness temperature and a median background temperature per scene was determined from an annulus around the active crater to produce the temperature above background. The algorithm creates a rejection criterion value defined by the median absolute deviation to identify the thermal anomalies. The size and intensity of these anomalies as well as the detection, composition, emission rate of small plumes are retrieved one year prior to the known eruptions for each volcano to identify all precursory signals. The results of this study have the dual purpose of constraining volcanological processes that lead to eruptions as well as providing training data for machine learning modeling. Machine learning is an effective and well-established technique that provides rapid classification of volcanic activity such as thermal anomalies that exceed a certain size and/or intensity. The comparison of these two approaches is documented in a companion abstract in this session.

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