9 Search Results

The Redmond Salt Mine (RSM) Monitoring Experiment in Utah was designed to record seismoacoustic data at distances less than 50 km for algorithm testing and development. During the experiment from October 2017 to July 2019, six broadband seismic stations were operating at a time, with three of them having fixed locations for the duration, whereas the three other stations were moved to different locations every one-and-half to two-and-half months. RSM operations consist of nighttime underground blasting several times per week. The RSM is located in proximity to a belt of active seismicity, allowing direct comparison of natural and anthropogenic sources. Usingmore » the recorded data set, we built 1373 events with local magnitude (M_L) of -2.4 and lower to 3.3. For 75 blasts (RMEs) from the Redmond Salt Mine and 206 tectonic earthquakes (EQs), both M_L and the coda duration magnitude (MC) are well constrained. We used these events to test and design discriminants that separate the RMEs from the EQs and are effective at local distances. The discriminants consist of M_L-M_C, low-frequency Sg to high-frequency Sg, Pg/Sg phase-amplitude ratios, and Rg/Sg spectral amplitude ratios, as well as different combinations of two or more of these classifiers. The areas under the receiver operating characteristic curves (AUCs) of 0.92–1.0 for M_L-M_C, low-frequency Sg to high-frequency Sg, and Rg/Sg indicate that these discriminants are very effective. Conversely, the AUC of only 0.57 for Pg/Sg suggests that this discriminant is only slightly better than a random classifier. Among the effective classifiers, Rg/Sg, shows the lowest likelihood of misclassification (4.3%) for the populations. In conclusion, results of joint discriminant analyses suggest that even the arguably ineffective single classifier, like Pg/Sg in this case, can provide some value when used in combination with others.« less

Multimodal, curated data sets and nuisance event catalogs remain rare in the explosion monitoring community relative to curated seismic data sets. The source of this relative absence is the difficultly in deploying multimodal receivers that sense the seismic, acoustic, and other modalities from multiphysics sources. We provide such a data set in this study that delivers seismic, infrasound, and electromagnetic (magnetometer) sensor records collected over a two–week period, within 255 km of a 10 ton buried chemical explosion called DAG–4 that was located at 37.1146°, –116.0693° on 22 June 2019 21:06:19.88 UTC. This catalog includes 485 seismic, seismoacoustic, and infrasound–onlymore » events that an expert analyst manually built by reviewing waveforms from 29 seismic and infrasound sensors. Our data release includes waveforms from these 29 seismic, infrasound, and seismoacoustic stations and two magnetometer stations and their station metadata. We deliver these waveforms in NNSA KB Core CSS.w format (i4) with a corresponding wfdisc table that provides the header information. Here, we expect that this data set will provide a valuable, benchmark resource to develop signal processing algorithms and explosion monitoring methods against manual, human observations.« less

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high–quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment to detect and identify mining blasts for two regions, Wyoming (U.S.A.) and Scandinavia, using waveform templates recorded by multiple International Monitoring System stations of the Preparatory Commission for the Comprehensive Nuclear–Test–Ban Treaty Organization (CTBTO PrepCom) for up to 10 yr prior to the time of interest. We discuss approaches formore » template selection, threshold setting, and event detection that are specialized for characterizing mining blasts using a sparse, global network. We apply the approaches to one week of data for each of the two regions to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be generally applied to operational monitoring systems with a sparse network. Here, we compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to assess potential reduction in analyst workload.« less

Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses amore » trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short-time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal-to-noise ratios (SNRs) by ~5 db, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ~0.80) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ~2-5 improvement in SNR over band-pass filtering and can suppress many types of noise that band-pass filtering cannot. For individual waveforms, the improvement can be as high as ~15 db.« less

Signal arrival-time estimation plays a critical role in a variety of downstream seismic analyses, including location estimation and source characterization. Any arrival-time errors propagate through subsequent data-processing results. In this article, we detail a general framework for refining estimated seismic signal arrival times along with full estimation of their associated uncertainty. Using the standard short-term average/long-term average threshold algorithm to identify a search window, we demonstrate how to refine the pick estimate through two different approaches. In both cases, new waveform realizations are generated through bootstrap algorithms to produce full a posteriori estimates of uncertainty of onset arrival time ofmore » the seismic signal. The onset arrival uncertainty estimates provide additional data-derived information from the signal and have the potential to influence seismic analysis along several fronts.« less

Abstract The capability to discriminate low-magnitude earthquakes from low-yield anthropogenic sources, both detectable only at local distances, is of increasing interest to the event monitoring community. Here, we used a dataset of seismic events in Utah recorded during a 14-day period (1–14 January 2011) by the University of Utah Seismic Stations network to perform a comparative study of event classification at local scale using amplitude ratio (AR) methods and a machine learning (ML) approach. The event catalog consists of 7377 events with magnitudes M_C ranging from –2 and lower up to 5.8. Events were subdivided into six populations based onmore » location and source type: tectonic earthquakes (TEs), mining-induced events (MIEs), and mining blasts from four known mines (WMB, SMB, LMB, and CQB). The AR approach jointly exploits Pg-to-Sg phase ARs and Rg-to-Sg spectral ARs in multivariate quadratic discriminant functions and was able to classify 370 events with high signal quality from the three groups with sufficient size (TE, MIE, and SMB). For that subset of the events, the method achieved success rates between about 80% and 90%. The ML technique used trained convolutional neural network (CNN) models to classify the populations. The CNN approach was able to classify the subset of events with accuracies between about 91% and 98%. Because the neural network approach does not have a minimum signal quality requirement, we applied it to the entire event catalog, including the abundant extremely low-magnitude events, and achieved accuracies of about 94%–100%. Furthermore, we compare the AR and ML methodologies using a broad set of criteria and conclude that a major advantage to ML methods is their robustness to low signal-to-noise ratio data, allowing them to classify significantly smaller events.« less

During the development of new seismic data processing methods, the verification of potential events and associated signals can present a nontrivial obstacle to the assessment of algorithm performance, especially as detection thresholds are lowered, resulting in the inclusion of significantly more anthropogenic signals. Here, we present two 14 day seismic event catalogs, a local–scale catalog developed using data from the University of Utah Seismograph Stations network, and a global–scale catalog developed using data from the International Monitoring System. Each catalog was built manually to comprehensively identify events from all sources that were locatable using phase arrival timing and directional informationmore » from seismic network stations, resulting in significant increases compared to existing catalogs. Here, the new catalogs additionally contain challenging event sequences (prolific aftershocks and small events at the detection and location threshold) and novel event types and sources (e.g., infrasound only events and long–wall mining events) that make them useful for algorithm testing and development, as well as valuable for the unique tectonic and anthropogenic event sequences they contain.« less

Abstract In a traditional data-processing pipeline, waveforms are acquired, a detector makes the signal detections (i.e., arrival times, slownesses, and azimuths) and passes them to an associator. The associator then links the detections to the fitting-event hypotheses to generate an event bulletin. Most of the time, this traditional pipeline requires substantial human-analyst involvement to improve the quality of the resulting event bulletin. For the year 2017, for instance, International Data Center (IDC) analysts rejected about 40% of the events in the automatic bulletin and manually built 30% of the legitimate events. Here, we introduce an iterative processing framework (IPF) thatmore » includes a new data-processing module that incorporates automatic analyst behaviors (auto analyst [AA]) into the event-building pipeline. In the proposed framework, through an iterative process, the AA takes over many of the tasks traditionally performed by human analysts. These tasks can be grouped into two major processes: (1) evaluating small events with a low number of location-defining arrival phases to improve their formation; and (2) scanning for and exploiting unassociated arrivals to form potential events missed by previous association runs. To test the proposed framework, we processed a two-week period (15–28 May 2010) of the signal-detections dataset from the IDC. Comparison with an expert analyst-reviewed bulletin for the same time period suggests that IPF performs better than the traditional pipelines (IDC and baseline pipelines). The majority of the additional events built by the AA are low-magnitude events that were missed by these traditional pipelines. The AA also adds additional signal detections to existing events, which saves analyst time, even if the event locations are not significantly affected.« less

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 phasesmore » are considered. In addition, 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.« less