Iceberg Calving in Greenland: Understanding the Dynamics through Seismic Data Analysis and Machine Learning ...
<!--!introduction!--> The Greenland ice sheet is a critical component of the global climate system, and its significant mass loss due to iceberg-calving has greatly contributed to sea-level rise. Through the quantification of the spatio-temporal changes in Greenland’s ice mass loss resulting f...
| Main Authors: | , , , , |
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| Format: | Conference Object |
| Language: | unknown |
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GFZ German Research Centre for Geosciences
2023
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| Online Access: | https://dx.doi.org/10.57757/iugg23-2510 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5018265 |
| Summary: | <!--!introduction!--> The Greenland ice sheet is a critical component of the global climate system, and its significant mass loss due to iceberg-calving has greatly contributed to sea-level rise. Through the quantification of the spatio-temporal changes in Greenland’s ice mass loss resulting from iceberg calving, we gain a deeper understanding of the impact of climate change. The mass loss related to calving icebergs can be estimated by combining mechanical simulation of iceberg calving and inversion of seismic data. Indeed, seismic signals are generated by the time-varying force produced during iceberg calving on marine-terminating glacier termini. Those events, known as glacial earthquakes, are recorded by the Greenland Ice Sheet Monitoring Network at tens of km from the source. However, differentiating these signals from tectonic events, anthropogenic noise, and other natural noise is challenging due to their wide frequency range. To overcome this challenge, we use a detection algorithm based on the ... : The 28th IUGG General Assembly (IUGG2023) (Berlin 2023) ... |
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