Ground Truth Events Research Articles

Relocation of the Seismicity of the Caucasus Region

ABSTRACT Our objective is to improve the view of the seismicity in the Caucasus region using instrumental data between 1951 and 2019. To create a comprehensive catalog, we combine the bulletins of local agencies and the International Seismological Centre, and use an advanced single-event location algorithm, iLoc, to obtain better locations. We show that relocations with iLoc, using travel-time predictions from the 3D upper mantle velocity model, Regional Seismic Travel Time, improve the locations. Then, using the iLoc results as initial locations and the ground-truth events identified in the iLoc results as fix points, we apply Bayesloc, a multiple-event location algorithm, to simultaneously relocate the entire seismicity of the Caucasus region. We demonstrate that the simultaneous relocation of the seismicity with Bayesloc clarifies the location and geometry of major active structures accommodating ongoing convergence between the Arabian and Eurasian continents between the Black and Caspian Seas. Among our major findings is the confirmation of widespread seismicity in the mantle beneath the northern flank of the Greater Caucasus and central Caspian, resulting from north-dipping subduction of the Kura and South Caspian basins and the identification of a discrete band of crustal seismicity beneath the southern flank of the Greater Caucasus.

Automatic highlight detection in videos of martial arts tricking

We propose a novel strategy for the automatic detection of highlight events in user-generated tricking videos, to the best of our knowledge, the first one specifically tailored for this complex sport. Most current methods for related sports leverage high-level semantics such as predefined camera angles or common editing practices, or rely on depth cameras to achieve automatic detection. However, our approach only relies on the contents (themselves) in the frames of a given video, and consists in a four stage pipeline. The first stage identifies foreground key points of interest along with an estimation of their motion in the video frames. In the second stage, these points are grouped into regions of interest based on their proximity and motion. Their behavior over time is evaluated in the third stage to generate an attention map indicating the regions participating in the most relevant events. The fourth and final stage provides the extracted video sequences where highlights have been identified. Experimental results attest to the effectiveness of our approach, which shows high recall and precision values at frame level, with detections that fit well the ground truth events.

Unsupervised Gait Event Identification with a Single Wearable Accelerometer and/or Gyroscope: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns.

We evaluated 18 methods capable of identifying initial contact (IC) and terminal contact (TC) gait events during human running using data from a single wearable sensor on the shank or sacrum. We adapted or created code to automatically execute each method, then applied it to identify gait events from 74 runners across different foot strike angles, surfaces, and speeds. To quantify error, estimated gait events were compared to ground truth events from a time-synchronized force plate. Based on our findings, to identify gait events with a wearable on the shank, we recommend the Purcell or Fadillioglu method for IC (biases +17.4 and -24.3 ms; LOAs -96.8 to +131.6 and -137.0 to +88.4 ms) and the Purcell method for TC (bias +3.5 ms; LOAs -143.9 to +150.9 ms). To identify gait events with a wearable on the sacrum, we recommend the Auvinet or Reenalda method for IC (biases -30.4 and +29.0 ms; LOAs -149.2 to +88.5 and -83.3 to +141.3 ms) and the Auvinet method for TC (bias -2.8 ms; LOAs -152.7 to +147.2 ms). Finally, to identify the foot in contact with the ground when using a wearable on the sacrum, we recommend the Lee method (81.9% accuracy).

Evaluating the location capabilities of a regional infrasonic network in Utah, US, using both ray tracing-derived and empirical-derived celerity-range and backazimuth models

SUMMARYMore 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.

Investigation of near-surface chemical explosions effects using seismo-acoustic and synthetic aperture radar analyses

Chemical explosions are ground truth events that provide data, which, in turn, can enhance the understanding of wave propagation, damage assessment, and yield estimation. On 4 August 2020, Beirut, Lebanon was shocked by a catastrophic explosion, which caused devastating damage to the Mediterranean city. A second strong chemical explosion took place at the Xiangshui, China chemical plant on 21 March 2019. Both events generated shock waves that transitioned to infrasound waves, seismic waves, as well as hydroacoustic signals with accompanying T-phases in the case of the Beirut event. In this work, the seismo-acoustic signatures, yields, and associated damage of the two events are investigated. The differentiainterferometry synthetic aperture radar analysis quantified the surface damage and the estimated yield range, equivalent to 2,4,6-trinitrotoluene [C7H5(NO2)3] (TNT), through a "boom" relation of the peak overpressure was evaluated. Infrasound propagation modeling identified a strong duct in the stratosphere with the propagation to the west in the case of the Beirut-Port explosion. In the case of the Xiangshui explosion, the modeling supports the tropospheric propagation toward the Kochi University of Technology (KUT) sensor network in Japan. Although the Beirut yield (202-270 ± 100 tons) was slightly larger than the Xiangshui yield (201 ± 83.5 tons), the near-source damage areas are almost the same based on the distribution of damaged buildings surrounding the explosions.

Central and Eastern European Infrasound Network: contribution to infrasound monitoring

SUMMARY The Central and Eastern European Infrasound Network (CEEIN) has been established in 2018 with the collaboration of four research institutes, the Zentralanstalt für Meteorologie and Geodynamik (ZAMG), Vienna, Austria; the Institute of Atmospheric Physics of the Czech Academy of Sciences (CAS IAP), Prague, Czech Republic; the Research Centre for Astronomy and Earth Sciences of the Eötvös Loránd Research Network (ELKH CSFK), Budapest, Hungary; and the National Institute for Earth Physics (NIEP), Magurele, Romania. The Main Centre of Special Monitoring National Center for Control and Testing of Space Facilities, State Agency of Ukraine joined CEEIN in 2019. We show how the CEEIN infrasound arrays improve the detection capability of the European infrasound network and discuss coherent noise sources observed at CEEIN stations. We present the first CEEIN bulletin (2017–2020) of infrasound-only and seismo-acoustic events and using ground truth events, we demonstrate how adding infrasound observations to seismic data in the location algorithm improves location accuracy.

Video-Based Deep Learning Approach for 3D Human Movement Analysis in Institutional Hallways: A Smart Hallway

New artificial intelligence- (AI) based marker-less motion capture models provide a basis for quantitative movement analysis within healthcare and eldercare institutions, increasing clinician access to quantitative movement data and improving decision making. This research modelled, simulated, designed, and implemented a novel marker-less AI motion-analysis approach for institutional hallways, a Smart Hallway. Computer simulations were used to develop a system configuration with four ceiling-mounted cameras. After implementing camera synchronization and calibration methods, OpenPose was used to generate body keypoints for each frame. OpenPose BODY25 generated 2D keypoints, and 3D keypoints were calculated and postprocessed to extract outcome measures. The system was validated by comparing ground-truth body-segment length measurements to calculated body-segment lengths and ground-truth foot events to foot events detected using the system. Body-segment length measurements were within 1.56 (SD = 2.77) cm and foot-event detection was within four frames (67 ms), with an absolute error of three frames (50 ms) from ground-truth foot event labels. This Smart Hallway delivers stride parameters, limb angles, and limb measurements to aid in clinical decision making, providing relevant information without user intervention for data extraction, thereby increasing access to high-quality gait analysis for healthcare and eldercare institutions.

Identification of Quarry Blasts Aided by Infrasound Data

We present our analysis of the detections from the Piszkés-tető, Hungary infrasound array that has been operational since May 2017. We introduce an automatic search method to associate infrasound detections with a preliminary list of explosions detected by the seismic network. Once infrasound detections are associated with seismic events, we relocate the events using both the seismic arrival time and infrasound azimuth observations. We validate our methodology on ground truth events, i.e., explosions confirmed by the mine operators. We demonstrate that seismo-acoustic locations are able to identify the source of explosions even for closely spaced quarries. The discriminative power of the infrasound azimuth is strongest at near-field distances, where local and regional seismic discrimination methods have the most difficulties.

Evaluating factors influencing infrasonic signal detection and automatic processing performance utilizing a regional network.

Physical and deployment factors that influence infrasound signal detection and assess automatic detection performance for a regional infrasound network of arrays in the Western U.S. are explored using signatures of ground truth (GT) explosions (yields). Despite these repeated known sources, published infrasound event bulletins contain few GT events. Arrays are primarily distributed toward the south-southeast and south-southwest at distances between 84 and 458 km of the source with one array offering azimuthal resolution toward the northeast. Events occurred throughout the spring, summer, and fall of 2012 with the majority occurring during the summer months. Depending upon the array, automatic detection, which utilizes the adaptive F-detector successfully, identifies between 14% and 80% of the GT events, whereas a subsequent analyst review increases successful detection to 24%-90%. Combined background noise quantification, atmospheric propagation analyses, and comparison of spectral amplitudes determine the mechanisms that contribute to missed detections across the network. This analysis provides an estimate of detector performance across the network, as well as a qualitative assessment of conditions that impact infrasound monitoring capabilities. The mechanisms that lead to missed detections at individual arrays contribute to network-level estimates of detection capabilities and provide a basis for deployment decisions for regional infrasound arrays in areas of interest.

Lightning Location System Detections as Evidence: A Unique Bayesian Framework

Modern lightning location systems (LLSs) remotely detect propagated electromagnetic fields and geolocate them for a better understanding of the lightning phenomenon. In recent years, they have become powerful tools for assessing situations where lightning may be the cause of damage. However, due to the random errors that occur in the remote sensing of electromagnetic propagation, such systems have median location accuracies between 50 and 100 m. This means that there is always some uncertainty in reported geolocations of lightning flashes. To date, there is no effective or standardized method for quantifying this uncertainty and using LLS reports as evidence. This article presents a unique solution to this problem by developing a Bayesian framework. In the field of forensic investigation and engineering, the Bayesian approaches to reporting evidence are widely accepted in legal forums. This article describes the necessary prior probability and likelihood functions (Gaussian mixture model, bivariate Gaussian, and Students’ t-distributions, respectively), and the framework is assessed by comparison with ground-truth events—photographed lightning events to a known location, the Brixton Tower in Johannesburg, South Africa. The framework has a true positive rate between 97% and 99% and 66% and 100% and a true negative rate between 98% and 99% using the bivariate Gaussian and Students’ t-likelihood functions, respectively. Utilizing the bivariate Students’ t-likelihood function achieves a much lower false-positive rate (0.05% to 0.2%) than the bivariate Gaussian likelihood function (0.7%–2%).

Identification of possible Δ14C anomalies since 14 ka BP: A computational intelligence approach

Rapid increments in the concentration of the radiocarbon in the atmosphere (Δ14C) have been identified in the years 774–775 CE and 993–994 CE (Miyake events) using annual measurements on known-age tree-rings. The level of cosmic radiation implied by such increases could cause the failure of satellite telecommunication systems, and thus, there is a need to model and predict them. In this work, we investigated several intelligent computational methods to identify similar events in the past. We apply state-of-the-art pattern matching techniques as well as feature representation, a procedure that typically is used in machine learning and classification. To validate our findings, we used as ground truth the two confirmed Miyake events, and several other dates that have been proposed in the literature. We show that some of the methods used in this study successfully identify most of the ground truth events (~1% false positive rate at 75% true positive rate). Our results show that computational methods can be used to identify comparable patterns of interest and hence potentially uncover sudden increments of Δ14C in the past.

The instrumental seismicity of the Barents and Kara sea region: relocated event catalog from early twentieth century to 1989

We have relocated seismic events registered within the Barents and Kara sea region from early twentieth century to 1989 with a view to creating a relocated catalog. For the relocation, we collected all available seismic bulletins from the global network using data from the ISC Bulletin (International Seismological Centre), ISC-GEM project (International Seismological Centre–Global Earthquake Model), EuroSeismos project, and by Soviet seismic stations from Geophysical Survey of the Russian Academy of Sciences. The location was performed by applying a modified method of generalized beamforming. We have considered several travel time models and selected one with the best location accuracy for ground truth events. Verification of the modified method and selection of the travel time model were performed using data on four nuclear explosions that occurred in the area of the Novaya Zemlya Archipelago and in the north of the European part of Russia. The modified method and the Barents travel time model provide sufficient accuracy for event location in the region. The relocation procedure was applied to 31 of 36 seismic events registered within the Barents and Kara sea region.

Use of bio-loggers to characterize red fox behavior with implications for studies of magnetic alignment responses in free-roaming animals

Spontaneous magnetic alignment (SMA), in which animals position their body axis in fixed alignments relative to magnetic field lines, has been shown in several classes of vertebrates and invertebrates. Although these responses appear to be widespread, the functional significance and sensory mechanism(s) underlying SMA remain unclear. An intriguing example comes from observations of wild red foxes (Vulpes vulpes) that show a ~fourfold increase in hunting success when predatory ‘mousing’ attacks are directed toward magnetic north-northeast. This form of SMA is proposed to receive input from a photoreceptor-based magnetoreception mechanism perceived as a ‘visual pattern’ and used as a targeting system to increase the accuracy of mousing attempts targeting hidden prey. However, similar to previous observational studies of magnetic orientation in vertebrates, direct evidence for the use of magnetic cues, and field-based experiments designed to characterize the biophysical mechanisms of SMA are lacking. Here, we develop a new approach for studies of SMA using triaxial accelerometer and magnetometer bio-loggers attached to semidomesticated red foxes. Accelerometer data were recorded from 415 ground-truth events of three behaviors exhibited by an adult red fox. A 5-nearest neighbor classifier was developed for behavioral analysis and performed with an accuracy of 95.7% across all three behaviors. To evaluate the generalizability of the classifier, data from a second fox were tested yielding an accuracy of 66.7%, suggesting the classifier can extract behaviors across multiple foxes. A similar classification approach was used to identify the fox’s magnetic alignment using two 8-way classifiers with differing underlying assumptions to distinguish magnetic headings in eight equally spaced 45° sectors. The magnetic heading classifiers performed with 90.0 and 74.2% accuracy, suggesting a realistic performance range for a classifier based on an independent set of training events equal in size to our sample. We report the development of ‘magnetic ethograms’ in which the behavior and magnetic alignment of foxes can be accurately extracted from raw sensor data. These techniques provide the basis for future studies of SMA where direct observation is not necessary and may allow for more sophisticated experimental designs aimed to characterize the sensory mechanisms mediating SMA behavior.

Less is More

Ensemble learning for anomaly detection has been barely studied, due to difficulty in acquiring ground truth and the lack of inherent objective functions. In contrast, ensemble approaches for classification and clustering have been studied and effectively used for long. Our work taps into this gap and builds a new ensemble approach for anomaly detection, with application to event detection in temporal graphs as well as outlier detection in no-graph settings. It handles and combines multiple heterogeneous detectors to yield improved and robust performance. Importantly, trusting results from all the constituent detectors may deteriorate the overall performance of the ensemble, as some detectors could provide inaccurate results depending on the type of data in hand and the underlying assumptions of a detector. This suggests that combining the detectors selectively is key to building effective anomaly ensembles—hence “less is more”. In this paper we propose a novel ensemble approach called SELECT for anomaly detection, which automatically and systematically selects the results from constituent detectors to combine in a fully unsupervised fashion. We apply our method to event detection in temporal graphs and outlier detection in multi-dimensional point data (no-graph), where SELECT successfully utilizes five base detectors and seven consensus methods under a unified ensemble framework. We provide extensive quantitative evaluation of our approach for event detection on five real-world datasets (four with ground truth events), including Enron email communications, RealityMining SMS and phone call records, New York Times news corpus, and World Cup 2014 Twitter news feed. We also provide results for outlier detection on seven real-world multi-dimensional point datasets from UCI Machine Learning Repository. Thanks to its selection mechanism, SELECT yields superior performance compared to the individual detectors alone, the full ensemble (naively combining all results), an existing diversity-based ensemble, and an existing weighted ensemble approach.

Assessing waveform predictions of recent three‐dimensional velocity models of the Tibetan Plateau

AbstractAccurate velocity models are essential for both the determination of earthquake locations and source moments and the interpretation of Earth structures. With the increasing number of three‐dimensional velocity models, it has become necessary to assess the models for accuracy in predicting seismic observations. Six models of the crustal and uppermost mantle structures in Tibet and surrounding regions are investigated in this study. Regional Rayleigh and Pn (or Pnl) waveforms from two ground truth events, including one nuclear explosion and one natural earthquake located in the study area, are simulated by using a three‐dimensional finite‐difference method. Synthetics are compared to observed waveforms in multiple period bands of 20–75 s for Rayleigh waves and 1–20 s for Pn/Pnl waves. The models are evaluated based on the phase delays and cross‐correlation coefficients between synthetic and observed waveforms. A model generated from full‐wave ambient noise tomography best predicts Rayleigh waves throughout the data set, as well as Pn/Pnl waves traveling from the Tarim Basin to the stations located in central Tibet. In general, the models constructed from P wave tomography are not well suited to predict Rayleigh waves, and vice versa. Possible causes of the differences between observed and synthetic waveforms, and frequency‐dependent variations of the “best matching” models with the smallest prediction errors are discussed. This study suggests that simultaneous prediction for body and surface waves requires an integrated velocity model constructed with multiple seismic waveforms and consideration of other important properties, such as anisotropy.

Local earthquake tomography of Scotland

Scotland is a relatively aseismic region for the use of local earthquake tomography, but 40 yr of earthquakes recorded by a good and growing network make it possible. A careful selection is made from the earthquakes located by the British Geological Survey (BGS) over the last four decades to provide a data set maximising arrival time accuracy and ray path coverage of Scotland. A large number of 1-D velocity models with different layer geometries are considered and differentiated by employing quarry blasts as ground-truth events. Then, SIMULPS14 is used to produce a robust 3-D tomographic P-wave velocity model for Scotland. In areas of high resolution the model shows good agreement with previously published interpretations of seismic refraction and reflection experiments. However, the model shows relatively little lateral variation in seismic velocity except at shallow depths, where sedimentary basins such as the Midland Valley are apparent. At greater depths, higher velocities in the northwest parts of the model suggest that the thickness of crust increases towards the south and east. This observation is also in agreement with previous studies. Quarry blasts used as ground truth events and relocated with the preferred 3-D model are shown to be markedly more accurate than when located with the existing BGS 1-D velocity model.

A framework for estimating stratospheric wind speeds from unknown sources and application to the 2010 December 25 bolide

SUMMARY 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.Becausewefocusoninterarraypropagationeffects,itisnotnecessarytosimulate thefullpropagationpathfromsourcetoreceiver.Thisfeatureallowsustousearelativelysimple 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 undermostscenarios.Theinversionschemeisappliedtoinfrasounddatarecordedfromalarge 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.

Improved location procedures at the International Seismological Centre

SUMMARY The International Seismological Centre (ISC) is a non-governmental, non-profit organization with the primary mission of producing the definitive account of the Earth's seismicity. The ISC Bulletin covers some 50 yr (1960–2011) of seismicity. The recent years have seen a dramatic increase both in the number of reported events and especially in the number of reported phases, owing to the ever-increasing number of stations worldwide. Similar ray paths will produce correlated traveltime prediction errors due to unmodelled heterogeneities in the Earth, resulting in underestimated location uncertainties, and for unfavourable network geometries, location bias. Hence, the denser and more unbalanced the global seismic station coverage becomes, the less defensible is the assumption (that is the observations are independent), which is made by most location algorithms. To address this challenge we have developed a new location algorithm for the ISC that accounts for correlated error structure, and uses all IASPEI standard phases with a valid ak135 traveltime prediction to obtain more accurate event locations. In this paper we describe the new ISC locator, and present validation tests by relocating the ground truth events in the IASPEI Reference Event List, as well as by relocating the entire ISC Bulletin. We show that the new ISC location algorithm provides small, but consistent location improvements, considerable improvements in depth determination and significantly more accurate formal uncertainty estimates. We demonstrate that the new algorithm, through the use of later phases and testing for depth resolution, considerably clusters event locations more tightly, thus providing an improved view of the seismicity of the Earth.

Experimental characterization of seasonal variations in infrasonic traveltimes on the Korean Peninsula with implications for infrasound event location

SUMMARY The dependence of infrasound propagation on the season and path environment was quantified by the analysis of more than 1000 repetitive infrasonic ground-truth events at an active, open-pit mine over two years. Blast-associated infrasonic signals were analysed from two infrasound arrays (CHNAR and ULDAR) located at similar distances of 181 and 169 km, respectively, from the source but in different azimuthal directions and with different path environments. The CHNAR array is located to the NW of the source area with primarily a continental path, whereas the ULDAR is located East of the source with a path dominated by open ocean. As a result, CHNAR observations were dominated by stratospheric phases with characteristic celerities of 260–289 m s–1 and large seasonal variations in the traveltime, whereas data from ULDAR consisted primarily of tropospheric phases with larger celerities from 322 to 361 m s–1 and larger daily than seasonal variation in the traveltime. The interpretation of these observations is verified by ray tracing using atmospheric models incorporating daily weather balloon data that characterizes the shallow atmosphere for the two years of the study. Finally, experimental celerity models that included seasonal path effects were constructed from the long-term data set. These experimental celerity models were used to constrain traveltime variations in infrasonic location algorithms providing improved location estimates as illustrated with the empirical data set.

Social tags as news event detectors

The objective of this study was to investigate the use of tags in iReport to detect breaking news in terms of coverage and immediacy. Coverage refers to the extent to which news reported in mainstream media can also be detected in iReport, while immediacy refers to the promptness of news reported in mainstream media vis-à-vis those detected in iReport. A total of 10 ground truth events were identified from mainstream media between 1 April 2008 and 31 December 2008. Additionally, 481,455 tags from 118,545 postings were drawn from iReport in the same period. Relative frequencies of the top 200 most frequently-used tags were analysed to check for spikes and bursts. Based on the results, four main findings emerged. First, the performance of using spikes and bursts to detect news events was found to be comparable. Next, news events detected via spikes and bursts were found to lag ranging from a few days to more than a week compared to the dates reported by mainstream media. Third, news events deemed to be significant by professional journalists did not always attract a high level of interest from iReport contributors. Finally, even though citizen journalism transcends national boundaries via the internet, news posted to iReport seemed to show a proclivity towards local context.