Publications

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We perform epicentral relocations for a broad area using cross-correlation measurements made on Lg waves recorded at regional distances on a sparse station network. Using a two-step procedure (pairwise locations and cluster locations), we obtain final locations for 5623 events—3689 for all of China from 1985 to 2005 and 1934 for the Wenchuan area from May to August 2008. These high-quality locations comprise 20% of a starting catalog for all of China and 25% of a catalog for Wenchuan. Of the 1934 events located for Wenchuan, 1662 (86%) were newly detected. The final locations explain the residuals 89 times better than the catalog locations for all of China (3.7302–0.0417 s) and 32 times better than the catalog locations for Wenchuan (0.8413–0.0267 s). The average semimajor axes of the 95% confidence ellipses are 420 m for all of China and 370 m for Wenchuan. The average azimuthal gaps are 205° for all of China and 266° for Wenchuan. 98% of the station distances for all of China are over 200 km. The mean and maximum station distances are 898 and 2174 km. The robustness of our location estimates and various trade-offs and sensitivities is explored with different inversion parameters for the location, such as starting locations for iterative solutions and which singular values to include. Our results provide order-of-magnitude improvements in locations for event clusters, using waveforms from a very sparse far-regional network for which data are openly available.

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The standard seismic explosion-monitoring paradigm is based on a sparse, spatially aliased network of stations to monitor either the whole Earth or a region of interest. Under this paradigm, state-of-the-art event-detection methods are based on seismic phase picks, which are associated at multiple stations and located using 3D Earth models. Here, we revisit a concept for event-detection that does not require phase picks or 3D models and fuses detection and association into a single algorithm. Our pickless event detector exploits existing catalog and waveform data to build an empirical stack of the full regional seismic wavefield, which is subsequently used to detect and locate events at a network level using correlation techniques. We apply our detector to seismic data from Utah and evaluate our results by comparing them with the earthquake catalog published by the University of Utah Seismograph Stations. The results demonstrate that our pickless detector is a viable alternative technique for detecting events that likely requires less analyst overhead than do the existing methods.

Using template waveforms from aftershocks of the Wenchuan earthquake (12 May 2008, Ms 7.9) listed in a global bulletin and continuous data from eight regional stations, we detected more than 6000 additional events in the mainshock source region from 1 May to 12 August 2008. These new detections obey Omori’s law, extend the magnitude of completeness downward by 1.1 magnitude units, and lead to a more than fivefold increase in number of known aftershocks compared with the global bulletins published by the International Data Centre and the International Seismological Centre. Moreover, we detected more M >2 events than were listed by the Sichuan Seismograph Network. Several clusters of these detections were then relocated using the double-difference method, yielding locations that reduced travel-time residuals by a factor of 32 compared with the initial bulletin locations. Our results suggest that using waveform correlation on a few regional stations can find aftershock events very effectively and locate them with precision.

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Swarms of earthquakes and/or aftershock sequences can dramatically increase the level of seismicity in a region for a period of time lasting from days to months, depending on the swarm or sequence. Such occurrences can provide a large amount of useful information to seismologists. For those who monitor seismic events for possible nuclear explosions, however, these swarms/sequences are a nuisance. In an explosion monitoring system, each event must be treated as a possible nuclear test until it can be proven, to a high degree of confidence, not to be. Seismic events recorded by the same station with highly correlated waveforms almost certainly have a similar location and source type, so clusters of events within a swarm can quickly be identified as earthquakes. We have developed a number of tools that can be used to exploit the high degree of waveform similarity expected to be associated with swarms/sequences. Dendro Tool measures correlations between known events. The Waveform Correlation Detector is intended to act as a detector, finding events in raw data which correlate with known events. The Self Scanner is used to find all correlated segments within a raw data steam and does not require an event library. All three techniques together provide an opportunity to study the similarities of events in an aftershock sequence in different ways. To comprehensively characterize the benefits and limits of waveform correlation techniques, we studied 3 aftershock sequences, using our 3 tools, at multiple stations. We explored the effects of station distance and event magnitudes on correlation results. Lastly, we show the reduction in detection threshold and analyst workload offered by waveform correlation techniques compared to STA/LTA based detection. We analyzed 4 days of data from each aftershock sequence using all three methods. Most known events clustered in a similar manner across the toolsets. Up to 25% of catalogued events were found to be a member of a cluster. In addition, the Waveform Correlation Detector and Self Scanner identified significant numbers of new events that were not in either the EDR or regional catalogs, showing a lowering of the detection threshold. We extended our analysis to study the effect of distance on correlation results by applying the analysis tools to multiple stations along a transect of nearly constant azimuth when possible. We expected the number of events found via correlation would drop off as roughly 1/r2, where r is the distance from mainshock to station. However, we found that regional geological conditions influenced the performance of a given station more than distance. For example, for one sequence we clustered 25% of events at the nearest station to the mainshock (34 km), while our performance dropped to 2% at a station 550 km distant. However, we matched our best performance (25% clustering) at a station 198 km distant.

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