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Main benefit is a way to remove background noise which is usually low frequency (removed in intercept plot) and high frequency (intercept plot false positives) but small magnitude (removes false positives after multiplication). The combination amplifies gains / discrimination power in high frequency shaking.
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High Pass
Investigating high-pass filtering: 1s period is good for general use, 0.25s period highpass mostly unnecessary, can even detect trains at ASHS.
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Able to pinpoint start of waveform by reversing until <3 standard deviations away from mean and then going further back until standard deviations from mean increases.
TODO:
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Network Consensus
Finding exact trigger points can be difficult. Individual stations can have passing pedestrians/trains, a lawn mower changing background levels etc. Instead of getting perfect trigger detection, we can rely on the overall network of stations.
Below is a simple trigger start time addition with a normal distribution added at each trigger start time for each station. Each earthquake distribution has a peak of 0.4. This isn't even considering the distance between stations yet is able to remove many false triggers. The plots only consider triggers which exceed 50 standard deviations from mean prior to triggering.
Left plot is for the Kaikoura earthquake, right plot is for a small earthquake on the 9th April 2020. Even though the small event is not visible in further stations, it still has a peak > 0.01.
