In a recent study, researchers from NORSAR and ITES - University of Strasbourg worked on how to use machine learning to deal with big data coming from DAS-based geological monitoring. This study was led by joint master’s student M. Donnadille was focusing on the use of machine learning to learn how to classify events based on NORSAR’s conventional seismic data and transfer this model to DAS data. Simultaneously, PhD student J. Rimpot was leading efforts on self-supervised learning of classification of seismological records from a submarine volcano. To continue working on this collaboration, we would like to organize two visits between our two organizations. During these two visits, researchers will focus on the following tasks: Task 1 (Q2 2024) - Knowledge sharing through seminars and a small workshop (A. Turquet, T. Stangeland, C. Hibert, J. Rimpot, C. Huynh). This task will focus on the field of DAS data analysis, self-supervised learning, and transfer learning. Task 2 (Q2-Q3 2024) - Self Supervised Learning on DAS data on the NORFOX and the Pyrénées Datasets (A. Turquet, T. Stangeland, C. Hibert, J. Rimpot, C. Huynh) : Task 2 aims at testing self-supervised learning applied to DAS data for which a prior catalog of events exist. Task 3 (Q2-Q3 2024) - Transfer Learning on DAS data on the NORFOX and the Pyérnées datasets (A. Turquet, T. Stangeland, C. Hibert, J. Rimpot, C. Huynh) : Task 3 is focused on transfer learning of models trained on classical seismological data recorded by large band seismometers and applied to DAS data. The objective is to refine, validate and replicate this work on several datasets and to provide guidelines for future application for environmental seismology. Task 4 (Q3 2024) - ERC Consolidator writing (A. Turquet, C. Hibert) : Task 4 centers on the preparation of an ERC (European Research Council) Consolidator Grant proposal. We will work on this project proposal to be submitted to the 2025 ERC consolidator grant call.