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More realistic models for infrasound signal propagation across a region can be used to improve the precision and accuracy of spatial and temporal source localization estimates. Motivated by incomplete infrasound event bulletins in the Western US, the location capabilities of a regional infrasonic network of stations located between 84–458 km from the Utah Test and Training Range, Utah, USA, is assessed using a series of near-surface explosive events with complementary ground truth (GT) information. Signal arrival times and backazimuth estimates are determined with an automatic F-statistic based signal detector and manually refined by an analyst. This study represents the first application of three distinct celerity-range and backazimuth models to an extensive suite of realistic signal detections for event location purposes. A singular celerity and backazimuth deviation model was previously constructed using ray tracing analysis based on an extensive archive of historical atmospheric specifications and is applied within this study to test location capabilities. Similarly, a set of multivariate, season and location specific models for celerity and backazimuth are compared to an empirical model that depends on the observations across the infrasound network and the GT events, which accounts for atmospheric propagation variations from source to receiver. Discrepancies between observed and predicted signal celerities result in locations with poor accuracy. Application of the empirical model improves both spatial localization precision and accuracy; all but one location estimates retain the true GT location within the 90 per cent confidence bounds. Average mislocation of the events is 15.49 km and average 90 per cent error ellipse areas are 4141 km2. The empirical model additionally reduces origin time residuals; origin time residuals from the other location models are in excess of 160 s while residuals produced with the empirical model are within 30 s of the true origin time. We demonstrate that event location accuracy is driven by a combination of signal propagation model and the azimuthal gap of detecting stations. A direct relationship between mislocation, error ellipse area and increased station azimuthal gaps indicate that for sparse networks, detection backazimuths may drive location biases over traveltime estimates. [ABSTRACT FROM AUTHOR]

We report on the source of seismoacoustic pulses that were observed across the state of Oklahoma (OK) during summer of 2019, and the subject of national media coverage and speculation. Seismic network data collected across four U.S. states and interviews with witnesses to the pulse's effect on residential structures demonstrate that they were triggered by routine ammunition disposal operations conducted by McAlester Army Ammunition Plant (McAAP). During these operations, conventional explosives destroy obsolete munitions stored in pits through a controlled sequence of electronically timed shots that occur over tens of minutes. Despite noise-abatement efforts that reduce coupling of acoustic energy with air, some lower frequency, subaudible (infrasonic) sound radiates from these shots as discrete pulses. We use nine months of blast log documents, seismic network records, analyst picks, and physical modeling to demonstrate that seismic stations as far as 640 km from McAAP sample these pulses, which record seasonal patterns in stratospheric and tropospheric winds, as well as the dynamic formation of waveguides and shadow zones. Digital short-term average to long-term average detectors that we augment with dynamic thresholds and time-binning operations identify these pulses with a fair probability, when compared with visual observations. Our analyses thereby provide estimates of observation rates for both partial and full sequences of these pulses, as well as single shots. We suggest that disposal operations can exploit existing, composite seismic networks to predict where residents are likely to witness blasting. Crucially, our data also show that dense seismic networks can record multiscale atmospheric processes in the absence of infrasound arrays. [ABSTRACT FROM AUTHOR]

The International Monitoring System (IMS) infrasound network has been designed to acquire the necessary data to detect and locate explosions in the atmosphere with a yield equivalent to 1 kiloton of TNT anywhere on Earth. A major associated challenge is the task of automatically processing data from all IMS infrasound stations to identify possible nuclear tests for subsequent review by analysts. This paper is the first attempt to quantify the false alarm rate (FAR) of the IMS network, and in particular to assess how the FAR is affected by the numbers and distributions of detections at each infrasound station. To ensure that the results are sufficiently general, and not dependent entirely on one detection algorithm, the assessment is based on two detection algorithms that can be thought of as end members in their approach to the trade-off between missed detections and false alarms. The results show that the FAR for events formed at only two arrays is extremely high (ranging from 10s to 100s of false events per day across the IMS network, depending on the detector tuning). It is further shown that the FAR for events formed at three or more IMS arrays is driven by ocean-generated waves (microbaroms), despite efforts within both detection algorithms for avoiding these signals, indicating that further research into this issue is merited. Overall, the results highlight the challenge of processing data from a globally sparse network of stations to detect and form events. The results suggest that more work is required to reduce false alarms caused by the detection of microbarom signals. [ABSTRACT FROM AUTHOR]

To assess infrasound detector performance, automated detections by the progressive multichannel correlation method (Cansi, 1995) and the adaptive F-detector (AFD; Arrowsmith et al., 2009) are compared with signals identified by five independent analysts. Each detector was applied to a 4-hr time sequence recorded by the Korean seismoacoustic array, CHNAR, composed of small (<100 m) and large (~1000 m) aperture subarrays. Detector effectiveness was estimated for a selection of array elements and detection thresholds under low- and high-noise conditions. Estimated receiver operating characteristic based on events identified by analysts evaluates the change in detection probability (Pd) and false-alarm probability (Pf) for various detector parameters. This empirical study documents that the use of smaller aperture subarrays by both detectors increases Pd with smaller p-values recommended for AFD to minimize Pf. Pd is impacted most by noise level, as shown by an increase in detections for average root mean square amplitudes from 1.2 to 3.2 MPa. Critical to this assessment is the identification of the source of the noise, constrained by signal characteristics, complementary seismic observations, and realistic atmospheric modeling. Based on signal characteristics (correlation value, phase velocity, and detection azimuth) and raytracing using global and local weather datasets, we conclude that during low-noise conditions some detections from local distances (10-50 km) are affected by surface wind direction, and a second set is affected by tropospheric winds. This illustrates the role that surface and higher-atmosphere winds play in array performance when assessing signals from regional infrasound sources in which local detections may be considered as noise or clutter. [ABSTRACT FROM AUTHOR]

This work quantifies the physical characteristics of infrasound signal and noise, assesses their temporal variations, and determines the degree to which these effects can be predicted by time-varying atmospheric models to estimate array and network performance. An automated detector that accounts for both correlated and uncorrelated noise is applied to infrasound data from three seismoacoustic arrays in South Korea (BRDAR, CHNAR, and KSGAR), cooperatively operated by Korea Institute of Geoscience and Mineral Resources (KIGAM) and Southern Methodist University (SMU). Arrays located on an island and near the coast have higher noise power, consistent with both higher wind speeds and seasonably variable ocean wave contributions. On the basis of the adaptive F-detector quantification of time variable environmental effects, the time-dependent scaling variable is shown to be dependent on both weather conditions and local site effects. Significant seasonal variations in infrasound detections including daily time of occurrence, detection numbers, and phase velocity/azimuth estimates are documented. These time-dependent effects are strongly correlated with atmospheric winds and temperatures and are predicted by available atmospheric specifications. This suggests that commonly available atmospheric specifications can be used to predict both station and network detection performance, and an appropriate forward model improves location capabilities as a function of time. [ABSTRACT FROM AUTHOR]

The mathematical framework used in the Bayesian Infrasonic Source Localization (BISL) methodology is examined and simplified providing a generalized method of estimating the source location and time for an infrasonic event. The likelihood function describing an infrasonic detection used in BISL has been redefined to include the von Mises distribution developed in directional statistics and propagation-based, physically derived celerity-range and azimuth deviation models. Frameworks for constructing propagation-based celerity-range and azimuth deviation statistics are presented to demonstrate how stochastic propagation modelling methods can be used to improve the precision and accuracy of the posterior probability density function describing the source localization. Infrasonic signals recorded at a number of arrays in the western United States produced by rocket motor detonations at the Utah Test and Training Range are used to demonstrate the application of the new mathematical framework and to quantify the improvement obtained by using the stochastic propagation modelling methods. Using propagation-based priors, the spatial and temporal confidence bounds of the source decreased by more than 40 per cent in all cases and by as much as 80 per cent in one case. Further, the accuracy of the estimates remained high, keeping the ground truth within the 99 per cent confidence bounds for all cases. [ABSTRACT FROM AUTHOR]

We use the Rayleigh integral (RI) as an approximation to the Helmholtz-Kirchoff integral to model infrasound generation and propagation from underground chemical explosions at distances of 250 m out to 5 km as part of the Source Physics Experiment (SPE). Using a sparse network of surface accelerometers installed above ground zero, we are able to accurately create synthetic acoustic waveforms and compare them to the observed data. Although the underground explosive sources were designed to be symmetric, the resulting seismic wave at the surface shows an asymmetric propagation pattern that is stronger to the northeast of the borehole. This asymmetric bias may be attributed to the subsurface geology and faulting of the area and is observed in the acoustic waveforms. We compare observed and modelled results from two of the underground SPE tests with a sensitivity study to evaluate the asymmetry observed in the data. This work shows that it is possible to model infrasound signals from underground explosive sources using the RI and that asymmetries observed in the data can be modelled with this technique. [ABSTRACT FROM AUTHOR]

We present a methodology for infrasonic remote sensing of winds in the stratosphere that does not require discrete ground-truth events. Our method uses measured time delays between arrays of sensors to provide group velocities (referred to here as celerities) and then minimizes the difference between observed and predicted celerities by perturbing an initial atmospheric specification. Because we focus on interarray propagation effects, it is not necessary to simulate the full propagation path from source to receiver. This feature allows us to use a relatively simple forward model that is applicable over short-regional distances. By focusing on stratospheric returns, we show that our non-linear inversion scheme converges much better if the starting model contains a strong stratospheric duct. Using the Horizontal Wind Model (HWM)/Mass Spectrometer Incoherent Scatter (MSISE) empirical climatology as a starting model, we demonstrate that the inversion scheme is robust to large uncertainties in backazimuth, but that uncertainties in the measured trace velocity and celerity require the use of prior constraints to ensure suitable convergence. The inversion of synthetic data, using realistic estimates of measurement error, shows that our scheme will nevertheless improve upon a starting model under most scenarios. The inversion scheme is applied to infrasound data recorded from a large event on 2010 December 25, which is presumed to be a bolide, using data from a nine-element infrasound network in Utah. We show that our recorded data require a stronger zonal wind speed in the stratosphere than is present in the HWM profile, and are more consistent with the Ground-to-Space (G2S) profile. [ABSTRACT FROM AUTHOR]

A method for acoustic detection of small explosions at local distances is presented combining a matched filter with a p-value representing the conditional probability of detection. Because the physics of signal generation and propagation for small, locally recorded acoustic signals from small explosions is well understood, the single hypothesis to be tested is a signal corrupted by additive noise. A simple analytical signal representation is used where a known signal is assumed with parameters to be determined. The advantage of the approach is that the detector can be combined with other detectors that measure different signal characteristics all under the same unifying hypothesis. [ABSTRACT FROM AUTHOR]

A methodology for the combined acoustic detection and discrimination of explosions, which uses three discriminants, is developed for the purpose of identifying weak explosion signals embedded in complex background noise. By utilizing physical models for simple explosions that are formulated as statistical hypothesis tests, the detection/discrimination approach does not require a model for the background noise, which can be highly complex and variable in practice. Fisher's Combined Probability Test is used to combine the p-values from all multivariate discriminants. This framework is applied to acoustic data from a 400 g explosion conducted at Los Alamos National Laboratory. [ABSTRACT FROM AUTHOR]

SUMMARY We report on a unique set of infrasound observations from a single earthquake, the 2011 January 3 Circleville earthquake ( Mw 4.7, depth of 8 km), which was recorded by nine infrasound arrays in Utah. Based on an analysis of the signal arrival times and backazimuths at each array, we find that the infrasound arrivals at six arrays can be associated to the same source and that the source location is consistent with the earthquake epicentre. Results of propagation modelling indicate that the lack of associated arrivals at the remaining three arrays is due to path effects. Based on these findings we form the working hypothesis that the infrasound is generated by body waves causing the epicentral region to pump the atmosphere, akin to a baffled piston. To test this hypothesis, we have developed a numerical seismoacoustic model to simulate the generation of epicentral infrasound from earthquakes. We model the generation of seismic waves using a 3-D finite difference algorithm that accounts for the earthquake moment tensor, source time function, depth and local geology. The resultant acceleration-time histories on a 2-D grid at the surface then provide the initial conditions for modelling the near-field infrasonic pressure wave using the Rayleigh integral. Finally, we propagate the near-field source pressure through the Ground-to-Space atmospheric model using a time-domain Parabolic Equation technique. By comparing the resultant predictions with the six epicentral infrasound observations from the 2011 January 3, Circleville earthquake, we show that the observations agree well with our predictions. The predicted and observed amplitudes are within a factor of 2 (on average, the synthetic amplitudes are a factor of 1.6 larger than the observed amplitudes). In addition, arrivals are predicted at all six arrays where signals are observed, and importantly not predicted at the remaining three arrays. Durations are typically predicted to within a factor of 2, and in some cases much better. These results suggest that measured infrasound from the Circleville earthquake is consistent with the generation of infrasound from body waves in the epicentral region. [ABSTRACT FROM AUTHOR]

We present a methodology for the detection of small, impulsive signal transients using time-frequency spectrograms closely related to the emerging field of scan statistics. In local monitoring situations, single-channel detection of small explosions can be difficult due to the complicated nature of the local noise field. Small, impulsive signals are manifest as vertical stripes on spectrograms and are enhanced on grayscale representations using vertical detection masks. Bitmap images are formed where pixels above a defined threshold are set to one. A short-duration large bandwidth signal will have a large number of illuminated bits in the column corresponding to its arrival time. We form the marginal distribution of bit counts as a function of time, ni, by summing columnwise over frequency. For each time window we perform a hypothesis test, H0: signal + noise, by defining a probability model expected when a signal is present. This model is Bernoulli for signal versus no signal with probability of signal = ρ1. We assume that ni follows the binomial distribution and compute a probability of detection (represented as a p value) for a given ρ1. We apply the spectrogram detector to 1 hr of single-channel acoustic data containing a signal from a 1 lb chemical surface explosion recorded at 3.1 km distance and compare performance with a short-term average to long-term average (STA/LTA) detector. Both detectors are optimized through grid search and successfully detect the acoustic arrival from the 1 lb explosion. However, 70% more false detections are observed for STA/LTA than for the spectrogram detector. At great range, attenuation properties of the earth reduce the effectiveness of the spectrogram detector relative to STA/LTA. Data fusion techniques using multiple channels from a network are shown to reduce the number of false detections. [ABSTRACT FROM AUTHOR]

We develop a framework for location of infrasound events using backazimuth and infrasonic arrival times from multiple arrays. Bayesian infrasonic source location (BISL) developed here estimates event location and associated credibility regions. BISL accounts for unknown source-to-array path or phase by formulating infrasonic group velocity as random. Differences between observed and predicted source-to-array traveltimes are partitioned into two additive Gaussian sources, measurement error and model error, the second of which accounts for the unknown influence of wind and temperature on path. By applying the technique to both synthetic tests and ground-truth events, we highlight the complementary nature of back azimuths and arrival times for estimating well-constrained event locations. BISL is an extension to methods developed earlier by Arrowsmith et al. that provided simple bounds on location using a grid-search technique. [ABSTRACT FROM AUTHOR]

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]

We extend a time-frequency discrimination algorithm, developed in an earlier article (Arrowsmith et al., 2006), for application to seismic-array data. Spectrograms evaluated at each component of an array are stacked and then converted into binary form for computation of discriminants. Because noise can bias the discriminants, we develop a procedure for removing the effect of noise on the discriminants. The binary spectrograms are randomized where the spectral amplitude of the signal is similar to the mean spectral amplitude of the pre-event noise at that frequency. The formulism of Arrowsmith et al. (2006) is further extended by modifying the objective function used to optimize the values of input parameters and by removing high-frequency and low-frequency spectral content. We apply the method to a dataset of regional recordings of earthquakes and delay-fired mine blasts recorded at the Pinedale seismic array in Wyoming. Our results show that the utilization of array data improves the success rate for source identification. Furthermore, we find that incorporating the noise-correction procedure increases the separation between earthquakes and cast overburden blasts (the largest type of delay-fired mine blasts). In total, the algorithm successfully identifies 97.4% of the events (74 of a total of 76 events, which comprise earthquakes and cast overburden blasts). [ABSTRACT FROM AUTHOR]

Delay-fired mine blasts, which consist of a series of individual shots arranged in a grid pattern and detonated in sequence, can introduce spectral modulations into recorded seismograms. We can exploit spectral modulations to separate delay-fired mine blasts from the remaining event population, which includes single-fired mine blasts and earthquakes. Here, we enhance an existing algorithm (Hedlin, 1998) for the automatic discrimination of delay-fired mine blasts. A total of seven separate discriminants are computed, based on the spectrograms of recorded events. A feature-selection procedure is used to ensure that each discriminant is significant and contributes to the overall performance of the discrimination algorithm. The effect of input parameters on the methodology is explored. The choice of input parameters is made to maximize the mean Mahalanobis distance between the earthquake and delay-fired mine-blast populations. The technique is then applied to a dataset consisting of regional earthquakes and delay-fired mine blasts recorded at a station in Wyoming. The results show that the larger delay-fired mine blasts, the cast blasts, can be identified successfully by using this technique. The smaller mine blasts are not identified with this technique, although such events are of less interest in a nuclear-monitoring perspective. In a drop-one test, 89.5% of the events studied are successfully identified. Of the events that are misclassified, one is a cast blast and seven are earthquakes. The cast blast is misclassified because of noise on one component, which biased the value of a single discriminant. The earthquakes are misclassified because of a greater variance of the seven discriminants for the mine-blast population. The results suggest that this methodology is very successful at identifying cast blasts in Wyoming, and would be an extremely useful method to use as part of an integrated set of discriminants for the identification of small-magnitude regional events. [ABSTRACT FROM AUTHOR]

The Iceland plume has had an important influence on vertical motions in the North Atlantic. The convecting mantle in this region contains a large-scale low-velocity seismic anomaly, which correlates with a long-wavelength gravity high and bathymetric feature. This suggests that an arm of plume material has extended, or is extending, from Iceland, in a direction perpendicular to the Mid-Atlantic Ridge. Here we present the results of a detailed teleseismic traveltime study that reveals the high-resolution morphology of this low-velocity anomaly beneath the British Isles. Our images provide insights into the nature of plume-lithosphere interactions. The low-velocity anomaly imaged in this study correlates geographically with a region of high gravity anomalies and high topography that was associated with Paleogene magmatism and phases of epeirogenic uplift during the Cenozoic Era. There is evidence that the distribution of British earthquakes is also related to the low-velocity anomaly. The low-velocity anomaly is interpreted to represent hot material from the original Iceland plume head that became trapped beneath thinned regions of lithosphere ca. 60 Ma. [ABSTRACT FROM AUTHOR]