ALBERT

Description: The GEOSCOPE observatory provides more than four decades of high-quality continuous broadband data to the scientific community. Started in 1982 with a few stations, the network has grown over the years thanks to numerous international partnerships. The 33 operational GEOSCOPE stations are installed in 18 countries, filling gaps in the global Earth coverage (in Africa, Antarctica, Indian Ocean, Pacific Ocean islands and more). Over the years GEOSCOPE contributed to define today's global seismology standards through the FDSN (data format, data quality level, instrumentation requirements), being the French contribution to the international effort (with GSN, GEOFON and others) towards global seismic observations. The stations are equipped with the best quality seismometers (from the very first STS1 in the early 80's to the last STS-6A and Trilium T360 nowadays) and digitizers (Q330HR and Centaur), in order to record with a high fidelity the ground motions generated by all types of seismic sources. Furthermore, most of the stations are also equipped with accelerometers, pressure and temperature sensors allowing for a wider range of observable events such as the recent Hunga-Tonga eruption. All the data are sent in real-time to IPGP, IRIS-DMC, RESIF, and tsunami warning data centers.In 2022, a workshop has been organized to celebrate the 40th anniversary of GEOSCOPE and illustrate the main scientific achievements made possible by all the global networks. After a look at the history of the network, the recent evolutions of the observatory in terms of instrumentation and data products (near-real time earthquake analyses) will be presented.

Description: 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 STA/LTA method and machine learning (Random Forests) trained on catalogues with known events. This algorithm will be applied to continuous data to detect new and possibly smaller events. As a result, we will present a comprehensive catalogue spanning several years and discuss its relevance and reliability. Finally, we will examine the correlations between events in the catalogue and external factors, such as climatic and meteorological events. The catalogue and machine learning approach can be used in the future to extract properties of the source from the generated seismic signals, such as the volume or the shape of the iceberg.