A geodetic matched filter search for slow slip with application to the Mexico subduction zone

Abstract

Since the discovery of slow slip events, many methods have been successfully applied to model obvious transient events in geodetic time series, such as the widely used network strain filter. Independent seismological observations of tremors or low‐frequency earthquakes and repeating earthquakes provide evidence of low‐amplitude slow deformation but do not always coincide with clear occurrences of transient signals in geodetic time series. Here we aim to extract the signal corresponding to slow slips hidden in the noise of GPS time series, without using information from independent data sets. We first build a library of synthetic slow slip event templates by assembling a source function with Green's functions for a discretized fault. We then correlate the templates with postprocessed GPS time series. Once the events have been detected in time, we estimate their duration T and magnitude Mw by modeling a weighted stack of GPS time series. An analysis of synthetic time series shows that this method is able to resolve the correct timing, location, T, and Mw of events larger than Mw 6 in the context of the Mexico subduction zone. Applied on a real data set of 29 GPS time series in the Guerrero area from 2005 to 2014, this technique allows us to detect 28 transient events from Mw 6.3 to 7.2 with durations that range from 3 to 39 days. These events have a dominant recurrence time of 40 days and are mainly located at the downdip edges of the Mw>7.5 slow slip events.

Seismic Moment, Seismic Energy, and Source Duration of Slow Earthquakes: Application of Brownian slow earthquake model to three major subduction zones

“…These values are comparable with an estimate of 1.4 ± 0.4 × 10 12 Nm/s for 20 Cascadia SSEs (Schmidt & Gao, 2010). In Mexico, the moment rate is higher, being 7.2-16.1 × 10 12 Nm/s for three large Guerrero events (Graham et al, 2016) and 2.5-2.8 × 10 13 Nm/s for many M w~7 events (Rousset et al, 2017). Although the source of this large discrepancy is unclear, it is probably in the assumptions in each method, such as elastic structure, source geometry, and the definition of duration, and not in the original GPS data.…”

“…Although the source of this large discrepancy is unclear, it is probably in the assumptions in each method, such as elastic structure, source geometry, and the definition of duration, and not in the original GPS data. In Mexico, the moment rate is higher, being 7.2-16.1 × 10 12 Nm/s for three large Guerrero events (Graham et al, 2016) and 2.5-2.8 × 10 13 Nm/s for many M w~7 events (Rousset et al, 2017). The results suggest that the moment rates for these SSEs exhibit an increasing trend across the three subduction zones, from Japan (lowest) to Mexico (highest).…”

Spatiotemporal Evolution of Recurrent Slow Slip Events Along the Southern Ryukyu Subduction Zone, Japan, From 2010 to 2013

“…However, SSE magnitudes still vary, ranging from M w 6.3 to 7.0. These SSEs, along with M w ~7 SSEs in other regions (e.g., Ohta et al, ; Rousset et al, ; Voss et al, ; Wallace & Beavan, ), are one of the largest SSEs in the world.…”

Possible slow slip event beneath the Kii Peninsula, southwest Japan, inferred from historical tilt records in 1973

“…SSEs have been detected by dense geodetic observation networks (Szeliga et al 2008;Sekine et al 2010;Nishimura et al 2013;Nishimura 2014;Rousset et al 2017). For example, Sekine et al (2010) analyzed tilt records of the high-sensitivity seismograph network (Hi-net), operated by the National Research Network for Earth Science and Disaster Resilience (Okada et al 2004;Obara et al 2005), to detect 54 SSEs from 2001 to 2008 in southwest Japan.…”

“…They analyzed the GNSS Earth Observation Network System (GEONET) (Nakagawa et al 2009) operated by the Geospatial Information Authority of Japan and found more than 100 SSEs from 1996 to 2012 in southwest Japan. In the Mexico subduction zone, Rousset et al (2017) constructed another systematic detection technique for SSEs, named the geodetic matched filter, to clarify 28 events from 2005 to 2014. These comprehensive studies of SSE detections are largely attributed to dense observation networks such as Hi-net and GEONET, which are available in these few decades.…”