USAP-DC

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Models from "Applying Machine Learning to Characterize and Extrapolate the Relationship Between Seismic Structure and Surface Heat Flow" Data DOI: https://doi.org/10.15784/601943 Cite as Zhang, S. (2025) "Models from "Applying Machine Learning to Characterize and Extrapolate the Relationship Between Seismic Structure and Surface Heat Flow"" U.S. Antarctic Program (USAP) Data Center. doi: https://doi.org/10.15784/601943. Abstract The dataset consists of six pickle files that each contain a machine learning model. The format for the filenames is `chosen_{region}_{model}.pickle`, where `region` denotes the region that the model is trained from (US or Europe), and `model` denotes one of Linear Regression (LR), Decision Tree (DT), and Random Forest (RF). Creator(s): Zhang, Shane Date Created: 2025-06-10 Repository: USAP-DC (current) License: Creative Commons Attribution Only v4.0 Generic [CC BY 4.0] Award(s) NSF 1943112 Version: 1 References Zhang, S. and Ritzwoller, M.H., 2024. Applying machine learning to characterize and extrapolate the relationship between seismic structure and surface heat flow. Geophysical Journal International, 238(3), pp.1201-1222. (doi:10.1093/gji/ggae218) Keywords Antarctica Cryosphere