Here are a few ways SciQL could help with this seismology use case:
1. The mseed array allows storing and querying the large seismic data in an efficient columnar format.
2. Window-based aggregation with dimensional grouping enables filtering signals by station/LTA ratios over time windows.
3. Views and queries on dimensional groups facilitate removing false positives by comparing signals across nearby stations over time.
4. Further window-based grouping and UDFs can extract signal windows for additional heuristic analysis.
By integrating the array and relational models, SciQL provides a declarative way to analyze large multidimensional scientific datasets like seismic signals interactively.