We search for repeating earthquakes (REs) in the northern San Francisco Bay Area in 1984–2016. By comparing over 670,000 waveforms from ∼75,000 events, we identify candidate clusters of events whose waveforms have high cross-correlation coefficients at multiple stations. A key difference with our approach is that these “multistation clusters” do not require each event in a family be recorded at multiple common stations. We validate these candidate REs by estimating precise relative relocations for the events in each cluster. We identify 59 RE families whose relocated hypocenters are separated by less than one source radius. These are distributed throughout the Maacama fault zone and along the northern Rodgers Creek and central Bartlett Springs faults, implying that widespread, pervasive creep occurs on those faults, at rates of 1–6 mm/year. At either end of the Maacama fault, the RE pattern highlights structural complexity, suggesting that multiple subparallel strands may be active and creeping.

Time series motifs have been in the literature for about fifteen years, but have only recently begun to receive significant attention in the research community. This is perhaps due to the growing realization that they implicitly offer solutions to a host of time series problems, including rule discovery, anomaly detection, density estimation, semantic segmentation, etc. Recent work has improved the scalability to the point where exact motifs can be computed on datasets with up to a million data points in tenable time. However, in some domains, for example seismology, there is an insatiable need to address even larger datasets. In this work we show that a combination of a novel algorithm and a high-performance GPU allows us to significantly improve the scalability of motif discovery. We demonstrate the scalability of our ideas by finding the full set of exact motifs on a dataset with one hundred million subsequences, by far the largest dataset ever mined for time series motifs. Furthermore, we demonstrate that our algorithm can produce actionable insights in seismology and other domains.