Analysis of instrument self-noise and microseismic event detection using power spectral density estimates

Abstract

Apart from the event magnitude, hypocentral distance, and background noise level, the instrument self-noise can also act as a major constraint for the detection of weak microseismic events in particular for deployments in quiet environments such as below 1.5-2km depths. Instrument self-noise levels that are comparable or above background noise levels may not only complicate detection of weak events at larger distances but also challenge methods such as seismic interferometry which aim at analysis of coherent features in the noise wavefields to reveal subsurface structure. In this article we use power spectral densities to estimate the instrument self-noise for a sample dataset and show how it could affect further analysis. We also suggest that the variations of the spectral powers in a timefrequency representation can be used as a new criterion for event detection and therefore propose a new event time picking algorithm. Compared to the common Short-time average and Long-time average (STA/LTA) method, our suggested technique requires easier parameter settings and detects small events with anomalous spectral powers with respect to an estimated background noise spectrum.