[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119941-en":3,"doc-seo-119941-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119941,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Mapping Glacier Basal Sliding Applying Machine Learning","Research using RESOLVE project observations on Glacier d’Argentière records 35 days of continuous surface displacement together with seismic array measurements. Spatially dense seismic and Global Positioning System (GPS) data are analyzed with machine learning to address limited direct knowledge of processes controlling glacial basal motion, especially for temperate alpine glaciers where dynamics are intermittent in time and heterogeneous in space. Multi bandpass-filtered seismic waveforms yield energy-based features and a matched-field beamforming catalog, combined with meteorological observations to estimate GPS displacements via a gradient boosting decision tree.","RESEARCH ARTICLE  \n10.1029/2023JF007280  \nJosefine Umlauft and Christopher W. Johnson contributed equally to this work.  \nKey Points:  \n• Seismic and Global Positioning System (GPS) data are examined to study physical processes controlling glacial basal motion  \n• Decision tree model uses beamforming catalog and statistical features of time series to constrain correlations with GPS recorded motions  \n• Model features indicate glacial  \non-ice velocity is modulated by basal motions  \nCorrespondence to:  \nJ. Umlauft,  \n[josefine.umlauft@uni-leipzig.de](josefine.umlauft@uni-leipzig.de)  \nCitation:  \nUmlauft, J., Johnson, C. W., Roux, P., Trugman, D. T., Lecointre, A., Walpersdorf, A., et al. (2023) . Mapping glacier basal sliding applying machine learning. Journal of Geophysical Research: Earth Surface, 128, e2023JF007280. [https://doi](https://doi). org/10.1029/2023JF007280  \nReceived 5 JUN 2023 Accepted 23 OCT 2023  \nAuthor Contributions:  \nConceptualization: Josefine Umlauft, Christopher W. Johnson, Philippe Roux, Daniel Taylor Trugman, Bertrand RouetLeduc, Paul A. Johnson  \nData curation: Josefine Umlauft, Christopher W. Johnson, Philippe Roux, Albanne Lecointre, Andrea Walpersdorf, Ugo Nanni  \nFormal analysis: Josefine Umlauft, Christopher W. Johnson, Philippe Roux, Albanne Lecointre, Andrea Walpersdorf, Ugo Nanni, Stefan Lüdtke, Sascha Marton Funding acquisition: Florent Gimbert, Paul A. Johnson  \n© 2023. The Authors.  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs  \nLicense, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.  \nMapping Glacier Basal Sliding Applying Machine Learning  \nJosefine Umlauft1 , Christopher W. Johnson2 , Philippe Roux3 , Daniel Taylor Trugman4 , Albanne Lecointre3 , Andrea Walpersdorf3 , Ugo Nanni5, Florent Gimbert6 , Bertrand Rouet-Leduc7, Claudia Hulbert8 , Stefan Lüdtke9 , Sascha Marton10 , and Paul A. Johnson2   \n1ScaDS.AI – Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany, 2Los Alamos National Laboratory, Los Alamos, NM, USA, 3ISTerre – Institut des Sciences de la Terre, Maison des Geosciences, Grenoble, France, 4Nevada Seismological Laboratory, University of Nevada, Reno, NV, USA, 5Department of Geosciences, University of Oslo, Oslo, Norway, 6IGE-Institut de Geophysique de l’Environnement, Grenoble, France, 7Disaster Prevention Research Center, Kyoto University, Kyoto, Japan, 8Ecole Normale Superieure, Paris, France, 9Institute for Visual & Analytic Computing, University of Rostock, Rostock, Germany, 10Institute for Enterprise Systems, University of Mannheim, Mannheim, Germany  \nAbstract During the RESOLVE project (“High-resolution imaging in subsurface geophysics: development of a multi-instrument platform for interdisciplinary research”), continuous surface displacement and seismic array observations were obtained on Glacier d’Argentière in the French Alps for 35 days in May 2018. The data set is used to perform a detailed study of targeted processes within the highly dynamic cryospheric environment. In particular, the physical processes controlling glacial basal motion are poorly understood and remain challenging to observe directly. Especially in the Alpine region for temperate based glaciers where the ice rapidly responds to changing climatic conditions and thus, processes are strongly intermittent in time and heterogeneous in space. Spatially dense seismic and Global Positioning System (GPS) measurements are analyzed applying machine learning to gain insight into the processes controlling glacial motions of Glacier d’Argentière. Using multiple bandpass-filtered copies of the continuous seismic waveforms, we compute energy-based features, develop a matched field beamforming catalog and include meteorological observations. Features describing the data are analyzed","cbCaiurXwgNVJHRA","https://ap.wps.com/l/cbCaiurXwgNVJHRA","pdf",4218418,1,20,"English","en",105,"# Abstract\n## Methods and modeling approach\n## Results and implications\n# Plain Language Summary\n## Problem motivation\n## Machine learning design and outcome\n# Introduction","[{\"question\":\"What data are used to study Glacier d’Argentière basal motion?\",\"answer\":\"The study uses continuous surface displacement and seismic array observations, complemented by dense Global Positioning System (GPS) measurements, collected for 35 days during May 2018.\"},{\"question\":\"How does the machine learning model connect seismic observations to GPS displacements?\",\"answer\":\"It computes energy-based features from multiple bandpass-filtered seismic waveforms, builds a matched field beamforming catalog, incorporates meteorological observations, and then uses a gradient boosting decision tree to estimate GPS displacements from seismic noise.\"},{\"question\":\"What do the results indicate about glacial motion?\",\"answer\":\"The model infers daily fluctuations and longer-term trends, and shows that on-ice displacement rates are strongly modulated by basal activity at the glacier base.\"}]","Mapping Glacier Basal Sliding Applying Machine Learning | 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data are used to study Glacier d’Argentière basal motion?","Question",{"text":75,"@type":76},"The study uses continuous surface displacement and seismic array observations, complemented by dense Global Positioning System (GPS) measurements, collected for 35 days during May 2018.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the machine learning model connect seismic observations to GPS displacements?",{"text":80,"@type":76},"It computes energy-based features from multiple bandpass-filtered seismic waveforms, builds a matched field beamforming catalog, incorporates meteorological observations, and then uses a gradient boosting decision tree to estimate GPS displacements from seismic noise.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results indicate about glacial motion?",{"text":84,"@type":76},"The model infers daily fluctuations and longer-term trends, and shows that on-ice displacement rates are strongly modulated by basal activity at the glacier 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