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For paleo environmental studies, a key cha … For paleo environmental studies, a key challenge is to partitioning physical signals operated under multiple spatio-temporal scales. For example, paleo relative sea-level (RSL) data record a combined signal from global ice-ocean mass exchange induced global mean sea-level change and gravitational, rotational and deformational effects, along with regional and local RSL change caused by changing ocean density, groundwater storage and sediment redistribution. Here we present an open-sourced spatio-temporal hierarchical model framework (PaleoSTeHM) that is conceptually suitable for investigating this problem by separating the underlying phenomenon of interest and its variability from the noisy mechanisms by which this underlying process is observed. PaleoSTeHM is built upon a modern, scalable machine-learning framework and offers flexible modelling and analytical choices. In this presentation, we will show some of the modelling choices in PaleoSTeHM along with an example application for Holocene sea level change. Also, we will seek inputs from potential users for this framework in order to make this co-develop framework more sustainable and allows a wide range of paleo-sea level and -climate researchers to easily and robustly incorporate spatio-temporal statistical modeling into their work.oral statistical modeling into their work. +
PaleoSTeHM - A modern, scalable Spatio-Temporal Hierarchical Modeling framework for paleo-environmental data +
Piscataway +
United States +
Rutgers University +
Piscataway +
yc.lin@rutgers.edu +
Yucheng +
Rutgers University +
7322539019 +
Image: +
1) Solving the sea level equation +
3) Parameterizing human dynamics in geomorphic models: learning from coastal barrier evolution models +
1) Using Fill-Spill-Merge to understand and analyze landscape depressions +
United States +
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18:31:58, 15 March 2024 +
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18:31:58, 15 March 2024 +
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