Search

This short article explores and extends the adaptive detection algorithm recently developed by Arrowsmith, Whitaker, et al. (2008). In particular, this article highlights its application for seismic data, compares results for colocated seismic and infrasonic data, and assesses detector performance through comparison with analyst picks. We assess the adaptive detector by generating receiver-operating characteristic (ROC) curves, illustrating the trade-off between detection probability and false-alarm probability, and comparing the results with the conventional F-detector. The results show that the adaptive detector performs much better than the conventional detector for both seismic and infrasound data by maintaining high detection probabilities while significantly decreasing false-alarm probabilities, illustrating that correlated noise is ubiquitous for both types of data. The effect of the adaptation window is illustrated and shown to be especially important for infrasound data where diurnal variations in ambient noise levels are pronounced. A window choice of 1 hr (i.e., significantly less than 24 hr) is shown to be adequate for representing variations in ambient noise levels. [ABSTRACT FROM AUTHOR]

In this paper, we present an integrated set of algorithms for the automatic detection, association, and location of low-frequency acoustic events using regional networks of infrasound arrays. Here, low-frequency acoustic events are characterized by transient signals, which may arise from a range of natural and anthropogenic sources, examples of which include (but are not limited to) earthquakes, volcanic eruptions, explosions, rockets and bolides. First, we outline a new technique for detecting infrasound signals that works successfully in the presence of correlated noise. We use an F-statistic, sequentially adapted to ambient noise conditions, in order to obtain detections at a given statistical significance while accounting for real background noise. At each array, individual arrivals are then grouped together based on measured delay-times and backazimuths. Each signal is identified as either a first or later arrival. First arrivals at spatially separated arrays are then associated using a grid-search method to form events. Preliminary event locations are calculated from the geographic means and spreads of grid nodes associated with each event. We apply the technique to regional infrasound networks in Utah and Washington State. In Utah, over a period of approximately 1 month, we obtain a total of 276 events recorded at three arrays in a geographic region of 6 × 4°. For four ground-truth explosions in Utah, the automatic algorithm detects, associates, and locates the events within an average offset of 5.4 km to the actual explosion locations. In Washington State, the algorithm locates numerous events that are associated with a large coalmine in Centralia, Washington. An example mining-explosion from Centralia is located within 8.2 km of the mine. The methodology and results presented here provide an initial framework for assessing the capability of infrasound networks for regional infrasound monitoring, in particular by quantifying detection thresholds and localization errors. [ABSTRACT FROM AUTHOR]

We study infrasonic signals from large surface mining explosions in Wyoming. Detections at the Pinedale Infrasound Array (PDIAR) (obtained using a conventional array-processing technique) are automatically associated with ground-truth mining explosions at a range of 368 km. We then focus on four clear signals from mining explosions. By performing a detailed noise study and modeling the propagation of infrasound using a raytracing algorithm and ground-to-space (G2S) atmospheric models, we assess the factors that contribute to the detectability of mining explosions. We find that we can explain most of the observations by propagation and noise effects alone, but that there are at least two notable outliers. Because of high noise levels at the PDIAR array, which places significant constraints on the sizes of mining explosions that can be detected, these results are strongly biased and must be reassessed for lower-noise infrasound arrays. [ABSTRACT FROM AUTHOR]