Correction of P/S Amplitude Ratios for Low-Magnitude Seismic Events Based on Bayesian Kriging Method

2021 ◽  
Author(s):  
Tingting Wang ◽  
Yinju Bian ◽  
Qianli Yang ◽  
Mengyi Ren
Keyword(s):  
High Frequency ◽  
Amplitude Ratio ◽  
Seismic Events ◽  
Kriging Method ◽  
Amplitude Ratios ◽  
Small Magnitude ◽  
Bayesian Kriging ◽  
Kriging Interpolation Method ◽  
Misclassification Probability ◽  
Low Magnitude

ABSTRACT Classification of low-magnitude seismic events is a challenging issue for the Comprehensive Nuclear-Test-Ban Treaty. Path correction of the P/S amplitude ratio is the key to identifying earthquakes and explosions. In this article, the Bayesian Kriging interpolation method is used to conduct the path correction of P/S amplitude ratios and recognition of low-magnitude seismic events. Based on a total of 5677 small earthquakes and 1769 explosions in Beijing and its adjacent areas, the Bayesian Kriging method is used to establish the path correction surface and uncertainty surface of Pg/Lg amplitude ratios measured within different frequency bands at five seismic stations, and path correction of amplitude ratios is conducted for all events. The results show that the correction surface is consistent with the observed amplitude ratios, which can reflect the differences in their propagation paths to a certain extent. The root mean square variation of the amplitude ratio is reduced by a maximum of 30% and the misclassification probability is reduced by a maximum of 8.5% after the Kriging correction. The high-frequency Pg/Lg amplitude ratios can effectively classify low-magnitude events, and the misclassification probability at each station is less than 15% and 10% based on high-frequency Pg/Lg of >7 and >9  Hz, respectively. Of the five stations, BJT (Baijiatuan, Beijing) has the best recognition, with the misclassification probability being lower than 5% after Kriging correction based on high-frequency Pg/Lg (>9  Hz). The classification ability of high-frequency amplitude ratios (>15  Hz) is weakened due to high-frequency noises. Bayesian Kriging correction can reduce the variance in the amplitude ratio of low-magnitude seismic events and hence effectively improve the ability to classify small-magnitude events, which has an important reference value for regional seismic monitoring and identification.

Applicability of P/S amplitude ratios for the discrimination of low magnitude seismic events

2007 ◽  
Vol 20 (5) ◽  
pp. 553-561 ◽  
Author(s):  
Chang-zhou Pan ◽  
Ping Jin ◽  
Hong-chun Wang
Keyword(s):  
Seismic Events ◽  
Amplitude Ratios ◽  
Low Magnitude

scholarly journals Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a Spectrogram‐Based Machine Learning Approach

2019 ◽  
Vol 109 (6) ◽  
pp. 2532-2544 ◽  
Author(s):  
Rigobert Tibi ◽  
Lisa Linville ◽  
Christopher Young ◽  
Ronald Brogan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Amplitude Ratio ◽  
Anthropogenic Sources ◽  
High Signal ◽  
Seismic Events ◽  
Signal Quality ◽  
Success Rates ◽  
Neural Network Approach ◽  
Low Magnitude

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. 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 MC ranging from −2 and lower up to 5.8. Events were subdivided into six populations based on 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 approach 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%. 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.

A Frequency-Domain-Based Algorithm for Detecting Microseismicity Using Dense Surface Seismic Arrays

2021 ◽  
Author(s):  
Maria Mesimeri ◽  
Kristine L. Pankow ◽  
James Rutledge
Keyword(s):  
Frequency Domain ◽  
Detection Method ◽  
Maximum Amplitude ◽  
High Energy ◽  
Aftershock Sequence ◽  
Anthropogenic Activity ◽  
Small Scale ◽  
Seismic Events ◽  
Small Magnitude ◽  
Seismic Arrays

ABSTRACT We propose a new frequency-domain-based algorithm for detecting small-magnitude seismic events using dense surface seismic arrays. Our proposed method takes advantage of the high energy carried by S waves, and approximate known source locations, which are used to rotate the horizontal components to obtain the maximum amplitude. By surrounding the known source area with surface geophones, we achieve a favorable geometry for locating the detected seismic events with the backprojection method. To test our new detection method, we used a dense circular array, consisting of 151 5 Hz three-component geophones, over a 5 km aperture that was in operation at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) in southcentral Utah. We apply the new detection method during a small-scale test injection phase at FORGE, and during an aftershock sequence of an Mw 4.1 earthquake located ∼30  km north of the geophone array, within the Black Rock volcanic field. We are able to detect and locate microseismic events (Mw<0) during injections, despite the high level of anthropogenic activity, and several aftershocks that are missing from the regional catalog. By comparing our method with known algorithms that operate both in the time and frequency domain, we show that our proposed method performs better in the case of the FORGE injection monitoring, and equally well for the off-array aftershock sequence. Our new method has the potential to improve microseismic event detections even in extremely noisy environments, and the proposed location scheme serves as a direct discriminant between true and false detections.

scholarly journals GroningenNet: Deep Learning for Low-Magnitude Earthquake Detection on a Multi-Level Sensor Network

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8080
Author(s):  
Ahmed Shaheen ◽  
Umair bin Waheed ◽  
Michael Fehler ◽  
Lubos Sokol ◽  
Sherif Hanafy
Keyword(s):  
Deep Learning ◽  
Template Matching ◽  
Carbon Capture And Storage ◽  
Microseismic Monitoring ◽  
Seismic Events ◽  
Learning Approaches ◽  
Monitoring Networks ◽  
Level Sensor ◽  
Multi Level ◽  
Low Magnitude

Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe. The detection of low-magnitude seismic events is essential for microseismic monitoring of hydraulic fracturing, carbon capture and storage, and geothermal operations for hazard detection and mitigation. Moreover, the detection of micro-earthquakes is crucial to understanding the underlying mechanisms of larger earthquakes. Various algorithms, including deep learning methods, have been proposed over the years to detect such low-magnitude events. However, there is still a need for improving the robustness of these methods in discriminating between local sources of noise and weak seismic events. In this study, we propose a convolutional neural network (CNN) to detect seismic events from shallow borehole stations in Groningen, the Netherlands. We train a CNN model to detect low-magnitude earthquakes, harnessing the multi-level sensor configuration of the G-network in Groningen. Each G-network station consists of four geophones at depths of 50, 100, 150, and 200 m. Unlike prior deep learning approaches that use 3-component seismic records only at a single sensor level, we use records from the entire borehole as one training example. This allows us to train the CNN model using moveout patterns of the energy traveling across the borehole sensors to discriminate between events originating in the subsurface and local noise arriving from the surface. We compare the prediction accuracy of our trained CNN model to that of the STA/LTA and template matching algorithms on a two-month continuous record. We demonstrate that the CNN model shows significantly better performance than STA/LTA and template matching in detecting new events missing from the catalog and minimizing false detections. Moreover, we find that using the moveout feature allows us to effectively train our CNN model using only a fraction of the data that would be needed otherwise, saving plenty of manual labor in preparing training labels. The proposed approach can be easily applied to other microseismic monitoring networks with multi-level sensors.

scholarly journals Hybrid broadband ground motion simulation validation of small magnitude earthquakes in Canterbury, New Zealand

2020 ◽  
Vol 36 (2) ◽  
pp. 673-699 ◽  
Author(s):  
Robin L Lee ◽  
Brendon A Bradley ◽  
Peter J Stafford ◽  
Robert W Graves ◽  
Adrian Rodriguez-Marek
Keyword(s):  
New Zealand ◽  
Ground Motion ◽  
High Frequency ◽  
Motion Simulation ◽  
Small Magnitude ◽  
Ground Motion Simulation ◽  
Simulation Methodology ◽  
Simulation Validation ◽  
Broadband Ground Motion Simulation ◽  
Broadband Ground Motion

Ground motion simulation validation is an important and necessary task toward establishing the efficacy of physics-based ground motion simulations for seismic hazard analysis and earthquake engineering applications. This article presents a comprehensive validation of the commonly used Graves and Pitarka hybrid broadband ground motion simulation methodology with a recently developed three-dimensional (3D) Canterbury Velocity Model. This is done through simulation of 148 small magnitude earthquake events in the Canterbury, New Zealand, region in order to supplement prior validation efforts directed at several larger magnitude events. Recent empirical ground motion models are also considered to benchmark the simulation predictive capability, which is examined by partitioning the prediction residuals into the various components of ground motion variability. Biases identified in source, path, and site components suggest that improvements to the predictive capabilities of the simulation methodology can be made by using a longer high-frequency path duration model, reducing empirical V s30-based low-frequency site amplification, and utilizing site-specific velocity models in the high-frequency simulations.

THE HILBERT MARGINAL SPECTRUM ANALYSIS AND FEATURE EXTRACTION OF SHORT-TERM HEART RATE SIGNAL OF SPONTANEOUS VENTRICULAR TACHYARRHYTHMIA

2011 ◽  
Vol 23 (06) ◽  
pp. 467-478 ◽  
Author(s):  
Hong-Sheng Dong ◽  
Ai-Hua Zhang ◽  
Xiao-Hong Hao
Keyword(s):  
High Frequency ◽  
Amplitude Ratio ◽  
Normal Sinus Rhythm ◽  
Ventricular Tachyarrhythmia ◽  
Very High Frequency ◽  
Total Amplitude ◽  
Rr Interval ◽  
Normal Sinus ◽  
Marginal Spectrum ◽  
Very High

The malignant ventricular tachyarrhythmia including ventricular tachycardia (VT) and ventricular fibrillation (VF) is the major cause of triggering sudden cardiac death (SCD) and it is seriously harmful to human. There is a great significance to predict the VT/VF. In this study, the RR interval series preceding the onset of VT/VF events are used as the study objects, and the 135 RR interval series are recorded by implantable cardioverter defibrillators (ICD) from 78 patients. Instantaneous heart rate (IHR) series are obtained after preprocessing the RR interval series. The Hilbert spectrum and the frequency marginal spectrum of IHR series are analyzed based on the traditional Hilbert-Huang transform (HHT) and the improved HHT. Some signal spectrum features are extracted from the marginal spectrum of IHR series: low frequency amplitude, high frequency amplitude, very high frequency amplitude, total amplitude and the low-to-high frequency amplitude ratio. The statistical analysis shows that the performance of improved HHT is better than that of traditional HHT. High frequency amplitude, very high frequency amplitude and total amplitude of IHR series preceding the onset of VT/VF are significantly higher than that of normal sinus rhythm (p < 0.003), and low-to-high frequency amplitude ratio is significantly lower than that of normal sinus rhythm (p < 0.0002).

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