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BEGIN:VEVENT
UID:DSC-21231
DTSTART;TZID=Europe/Berlin:20240912T110000
SEQUENCE:1723542753
TRANSP:OPAQUE
DTEND;TZID=Europe/Berlin:20240912T120000
URL:https://www.dresden-science-calendar.de/calendar/en/detail/21231
LOCATION:Online\,   
SUMMARY:Umlauft: Mapping Glacier Sliding with Machine Learning
CLASS:PUBLIC
DESCRIPTION:Speaker: Dr. Josefine Umlauft\nInstitute of Speaker: ScaDS.AI D
 resden/Leipzig\nTopics:\nUmwelt\n Location:\n  Name: Online (https://tud.l
 ink/i8zf )\n  Street:  \n  City:  \n  Phone: \n  Fax: \nDescription: Glaci
 al ice rapidly responds to changing climatic conditions\, making its proce
 sses strongly intermittent in time and heterogeneous in space. In particul
 ar\, the physical processes controlling glacial basal motion are poorly un
 derstood and remain challenging to observe directly.    This lecture prese
 nts a detailed study of targeted processes within the highly dynamic cryos
 pheric environment. Using a unique seismic dataset from Glacier d’Argent
 ière (French Alps)\, a physics-informed feature space\, and a gradient bo
 osting decision tree model\, glacial sliding can be predicted with high sp
 atio-temporal accuracy. We posit that features of the seismic noise provid
 e direct access to the dominant parameters that drive displacement on the 
 highly variable and unsteady surface of the glacier\, making this a new ap
 proach to studying glacial basal sliding and uncovering its full complexit
 y.
DTSTAMP:20261010T073052Z
CREATED:20240813T095233Z
LAST-MODIFIED:20240813T095233Z
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