The matched filtering technique that uses the cross correlation of a waveform of interest with archived signals from a template library has proven to be a powerful tool for detecting events in regions with repeating seismicity. However, waveform correlation is computationally expensive and therefore impractical for large template sets unless dedicated distributed computing hardware and software are used. In this study, we introduce an approximate nearest neighbor (ANN) approach that enables the use of very large template libraries for waveform correlation. Our method begins with a projection into a reduced dimensionality space, based on correlation with a randomized subset of the full template archive. Searching for a specified number of nearest neighbors for a query waveform is accomplished by iteratively comparing it with the neighbors of its immediate neighbors. We used the approach to search for matches to each of ∼2300 analyst-reviewed signal detections reported in May 2010 for the International Monitoring System station MKAR. The template library in this case consists of a data set of more than 200,000 analyst-reviewed signal detections for the same station from February 2002 to July 2016 (excluding May 2010). Of these signal detections, 73% are teleseismic first P and 17% regional phases (Pn, Pg, Sn, and Lg). The analyses performed on a standard desktop computer show that the proposed ANN approach performs a search of the large template libraries about 25 times faster than the standard full linear search and achieves recall rates greater than 80%, with the recall rate increasing for higher correlation thresholds.
The task of monitoring the Earth for nuclear explosions relies heavily on seismic data to detect, locate, and characterize suspected nuclear tests. Motivated by the need to locate suspected explosions as accurately and precisely as possible, we developed a tomographic model of the compressional wave slowness in the Earth’s mantle with primary focus on the accuracy and precision of travel-time predictions for P and Pn ray paths through the model. Path-dependent travel-time prediction uncertainties are obtained by computing the full 3D model covariance matrix and then integrating slowness variance and covariance along ray paths from source to receiver. Path-dependent travel-time prediction uncertainties reflect the amount of seismic data that was used in tomography with very low values for paths represented by abundant data in the tomographic data set and very high values for paths through portions of the model that were poorly sampled by the tomography data set. The pattern of travel-time prediction uncertainty is a direct result of the off-diagonal terms of the model covariance matrix and underscores the importance of incorporating the full model covariance matrix in the determination of travel-time prediction uncertainty. The computed pattern of uncertainty differs significantly from that of 1D distance-dependent traveltime uncertainties computed using traditional methods, which are only appropriate for use with travel times computed through 1D velocity models.
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.
GeoTess is a model parameterization and software support library that manages the construction, population, storage, and interrogation of data stored in 2D and 3D Earth models. The software is available in Java and C++, with a C interface to the C++ library. The software has been tested on Linux, Mac, Sun, and PC platforms. It is open source and is available online (see Data and Resources).
Given a set of observations within a specified time window, a fitness value is calculated at each grid node by summing station-specific conditional fitness values. Assuming each observation was generated by a refracted P wave, these values are proportional to the conditional probabilities that each observation was generated by a seismic event at the grid node. The node with highest fitness value is accepted as a hypothetical event location, subject to some minimal fitness value, and all arrivals within a longer time window consistent with that event are associated with it. During the association step, a variety of different phases are considered. Once associated with an event, an arrival is removed from further consideration. While unassociated arrivals remain, the search for other events is repeated until none are identified. Results are presented in comparison with analyst-reviewed bulletins for three datasets: a two-week ground-truth period, the Tohoku aftershock sequence, and the entire year of 2010. The probabilistic event detection, association, and location algorithm missed fewer events and generated fewer false events on all datasets compared to the associator used at the International Data Center (51% fewer missed and 52% fewer false events on the ground-truth dataset when using the same predictions).