1) The first concern is the goal of the paper. Indeed, DAS earthquake detectors exists (one of them was cited by the autors, PhaseNet-Das, Zhu et al. 2023, there might be others), and no comparison was made, nor a justification on the benefit of your method against theirs. If the claim is to say that this is a foundation model, and the test on this task is only as a proof of concept, it should be clearer, and then show or justify a future useful application.
2) I think the purpose of a foundation model would be its applicability at a larger scale. Yet, is your method generalizable to other DAS sensors? It is not clear whether it is site and sensor-specific or not; if so it means a new self-training needs to be performed again for any new DAS.
3) The whole idea of this method is that earthquakes are unpredictible. It is clever indeed, but I see 2 major limitations: 1) this foundation model is thus harder to use for other tasks (which could be predictable) 2) in a series of aftershocks (which could maybe be seen as more predictable), how does your measure performs?
4) The comparison with other multi-variate time series are somehow misleading. Indeed, in multi-variate time-series, we suppose that the different time series (or sensors) are not ordered and not equally-spaced: DAS is a very particular type of 'multi-variate time-series'. I don't think it is worth presenting all of these methods (maybe only one), and it should be clearly stated in the paper. Yet, a comparison with image 2D foundation models, or by modifying a video framework from a 2D+t to a 1D+t, would be more relevant.
1) The first concern is the goal of the paper. Indeed, DAS earthquake detectors exists (one of them was cited by the autors, PhaseNet-Das, Zhu et al. 2023, there might be others), and no comparison was made, nor a justification on the benefit of your method against theirs. If the claim is to say that this is a foundation model, and the test on this task is only as a proof of concept, it should be clearer, and then show or justify a future useful application.