Megan Elizabeth. Slinkard

Applying Waveform Correlation to Three Aftershock Sequences

Abstract For nuclear explosion seismic monitoring, major aftershock sequences can be a significant problem because each event must be analyzed. Fortunately, the high degree of waveform similarity expected within aftershock sequences offers a way to more quickly and robustly process these events than is possible using traditional methods (e.g., short‐term average/long‐term average detection). We explore how waveform correlation can be incorporated into an automated event detection system to improve both the timeliness and the quality of the resultant bulletin. With our Waveform Correlation Detector we processed three aftershock sequences: the 1994 Northridge earthquake, the 2005 Kashmir earthquake, and the 2008 Wenchuan earthquake. Our system compared incoming waveform data to a library of known master events and identified incoming waveform data that correlated well with a master event as a repeating event. We break down our results to show how many master events found matches, the distribution in family size, and the effect of distance and fault characteristics on the results. Between 24% and 92% of the events in each sequence were recognized as similar events.

Multistation Validation of Waveform Correlation Techniques as Applied to Broad Regional Monitoring

Abstract Waveform correlation is garnering attention as a method for detecting, locating, and characterizing similar seismic events. To explore the opportunities for using waveform correlation in broad regional monitoring, we applied the technique to a large region of central Asia over a three‐year period, monitoring for events at regional distances using three high‐quality stations. We discuss methods for choosing quality templates and introduce a method for choosing correlation detection thresholds, tailored for each template, for a desired false alarm rate. Our SeisCorr software found more than 10,000 detections during the three‐year period using almost 2000 templates. We discuss and evaluate three methods of confirming detections: bulletin confirmation, high correlation with a template, and multistation validation. At each station, 65%–75% of our detections could be confirmed, most by multistation validation. We confirmed over 6500 unique detections. For monitoring applications, it is of interest that a significant portion of the Comprehensive Nuclear‐Test‐Ban Treaty Organization’s Late Event Bulletin (LEB) catalog events was detected and that adding our confirmed detections for the LEB catalog would more than double the catalog size. Waveform correlation also allows for relative magnitude calculation, and we explore the magnitudes of detected events. The results of our study suggest that doing broad regional monitoring using historical and real‐time‐generated templates is feasible and will increase detection capabilities.

Distributed algorithms for small vehicle detection, classification and velocity estimation using unattended ground sensors

This study developed a distributed vehicle target detection and estimation capability using two algorithmic approaches designed to take advantage of the capabilities of networked sensor systems. The primary interest was on small, quiet vehicles, such as personally owned SUVs and light trucks. The first algorithm approach utilized arrayed sensor beamforming techniques. In addition, it demonstrated a capability to find locations of unknown roads by extending code developed by the Army Acoustic Center for Excellence at Picatinny Arsenal. The second approach utilized single (non-array) sensors and employed generalized correlation techniques. Modifications to both techniques were suggested that, if implemented, could yield robust methods for target classification and tracking using two different types of networked sensor systems.

Pickless event detection and location: The waveform correlation event detection system (WCEDS) revisited

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.