An Effective Method for Small Event Detection: Match and Locate (ML) and Its Applications

Abstract:

Detection of low magnitude event is critical and challenging in seismology. Traditional methods of event detection, which rely on phase identification, are usually hindered by low signal to noise ratio (SNR) in small event recordings. We develop a new method, named the match and locate (ML) method, for small event detection. The ML method employs some template events and detects small events through stacking cross-correlograms between waveforms of the template events and potential small event signals in the continuous waveforms over multiple stations and components. Unlike the traditional match filter method which assumes that the template event and slave event are co-located, the ML method scans over potential small event locations around the template, by making relative travel time corrections based on the relative locations of the template event and the potential small event before stacking. It makes event detection more efficient and at the same time relocates the detected event in high-precision. As an example of application and comparison with the matched filter method, we apply the ML and matched filter methods to detect the foreshocks before the 2011 Mw 9.0 Tohoku earthquake. The ML method detects four times more events than the templates and 10% more than the matched filter under the same detection threshold. Up to 42% of the events detected by the ML method are not co-located at the template locations with the largest event separation of 9.4 km. As another example of application, we apply the ML method to search for potential nuclear tests conducted by North Korea in the continuous seismic data recorded in Northeast China, using North Korea’s 2009 and 2013 tests as templates. We report detection of a low-yield nuclear test conducted by North Korea in 2010.