scholarly journals Automatic seismic swarm analyzer system based on template matching algorithms and Master-Cluster relative location methods

2021 ◽  
Author(s):  
Eduardo Diaz Suarez ◽  
Itahiza Dominguez Cerdeña ◽  
Carmen Del Fresno
Keyword(s):  
Template Matching ◽  
Relative Location ◽  
Seismic Swarm ◽  
Location Methods

Comparison of two low magnitude seismic swarms in Calabria (Italy)

2021 ◽  
Author(s):  
Giuseppe Davide Chiappetta ◽  
Anna Gervasi ◽  
Mario La Rocca
Keyword(s):  
Focal Mechanism ◽  
Focal Mechanisms ◽  
P Wave ◽  
Reliable Estimate ◽  
Depth Range ◽  
Relative Location ◽  
S Waves ◽  
Seismic Swarm ◽  
Seismic Swarms ◽  
The Many

<p>We studied two seismic swarms occurred recently in Calabria, one in the Mesima-valley and one near Albi. Earthquakes were located by manually picking P and S waves. A search for clusters of events characterized by similar waveform was done, then the relative location was performed for any clusters. The focal mechanism was computed for as many events as possible, comparing the observed seismograms with synthetic signals for events of M>2.8, and considering the polarity of P and S waves for smaller events. For very small earthquakes we tried an estimation of the focal mechanism by comparison of the few clear signals with the recordings of stronger events. This analysis is aimed at investigating whether the many earthquakes of a swarm are produced by the same fault or by faults characterized by different orientation.</p><p>The Mesima valley area was affected by a seismic swarm that begun with a M3.6 earthquake on May 26, 2019. More than 140 events of smaller magnitude occurred in the same area during the following month. The relative location shows a hypocenter distribution with depth between 16 and 19 km and elongated for about 2 km in the NE-SW direction. The seismogenetic volume estimated from the relative location is of about 12 km<sup>3</sup>. The focal mechanisms computed for the 9 strongest events of the swarm are very similar among them, indicating a dip-slip normal kinematics. The comparative observation of P-wave polarity suggests that the most events of this swarm were likely generated by the same fault. In fact, even very small earthquakes (M<1.5) for which we can't give a reliable estimate of the focal mechanism, are characterized by P wave of the same polarity of stronger events at the stations around the epicenter.</p><p><span>Albi seismic swarm is one of the most interesting occurred in the central-eastern part of Calabria during the last 10 years. It begun on January 16, 2020, with a M3.8 earthquake, followed by more than 120 events in a month, and many others later. Detailed analyses were performed on as many earthquakes as possible, including absolute location, search for clusters of similar events and their relative location, and the estimation of focal mechanism. Results clearly indicate that this swarm was generated by a much greater seismogenetic volume if compared with the Mesima valley swarm. In fact hypocenters are much more spread, forming a cloud in the 6-12 km depth range, with a volume of at least 30-40 km</span><sup><span>3</span></sup><span>, and without any clear shape or direction. The search for clusters gave many families of similar events. Events of different clusters show waveforms quite different among them. Sometimes earthquakes located very near to each other have opposite P-wave polarity at the same station. Focal mechanisms confirm the heterogeneity of this swarm. The only common feature is the normal kinematics, while strike and dip cover wide ranges of values. Therefore we conclude that this swarm was generated by many small faults with different directions, activated by an extensional stress field.</span></p>

Rapid Earthquake Association and Location

2019 ◽  
Vol 90 (6) ◽  
pp. 2276-2284 ◽  
Author(s):  
Miao Zhang ◽  
William L. Ellsworth ◽  
Gregory C. Beroza
Keyword(s):  
Travel Time ◽  
Time Windows ◽  
Grid Point ◽  
Seismic Events ◽  
Relative Location ◽  
Central Apennines ◽  
Location Method ◽  
Event Location ◽  
Seismic Characterization ◽  
Location Methods

ABSTRACT Rapid association of seismic phases and event location are crucial for real‐time seismic monitoring. We propose a new method, named rapid earthquake association and location (REAL), for associating seismic phases and locating seismic events rapidly, simultaneously, and automatically. REAL combines the advantages of both pick‐based and waveform‐based detection and location methods. It associates arrivals of different seismic phases and locates seismic events primarily through counting the number of P and S picks and secondarily from travel‐time residuals. A group of picks are associated with a particular earthquake if there are enough picks within the theoretical travel‐time windows. The location is determined to be at the grid point with the most picks, and if multiple locations have the same maximum number of picks, the grid point among them with smallest travel‐time residuals. We refine seismic locations using a least‐squares location method (VELEST) and a high‐precision relative location method (hypoDD). REAL can be used for rapid seismic characterization due to its computational efficiency. As an example application, we apply REAL to earthquakes in the 2016 central Apennines, Italy, earthquake sequence occurring during a five‐day period in October 2016, midway in time between the two largest earthquakes. We associate and locate more than three times as many events (3341) as are in Italy's National Institute of Geophysics and Volcanology routine catalog (862). The spatial distribution of these relocated earthquakes shows a similar but more concentrated pattern relative to the cataloged events. Our study demonstrates that it is possible to characterize seismicity automatically and quickly using REAL and seismic picks.

Pedestrian Detection by Template Matching Using Gabor Filter Bank on 24GHz UWB Radar

2017 ◽  
Vol E100.A (1) ◽  
pp. 232-235
Author(s):  
Kota IWANAGA ◽  
Keiji JIMI ◽  
Isamu MATSUNAMI
Keyword(s):  
Template Matching ◽  
Filter Bank ◽  
Gabor Filter ◽  
Pedestrian Detection ◽  
Uwb Radar ◽  
Gabor Filter Bank

Crosstalk Minimization Method for Eye-tracking-based 3D Display

2020 ◽  
Author(s):  
Seok Lee ◽  
Juyong Park ◽  
Dongkyung Nam
Keyword(s):  
Image Processing ◽  
Eye Tracking ◽  
Three Dimensional ◽  
Processing Method ◽  
Eye Position ◽  
Relative Location ◽  
3D Display ◽  
Minimization Method ◽  
3D Crosstalk ◽  
Pixel Value

In this article, the authors present an image processing method to reduce three-dimensional (3D) crosstalk for eye-tracking-based 3D display. Specifically, they considered 3D pixel crosstalk and offset crosstalk and applied different approaches based on its characteristics. For 3D pixel crosstalk which depends on the viewer’s relative location, they proposed output pixel value weighting scheme based on viewer’s eye position, and for offset crosstalk they subtracted luminance of crosstalk components according to the measured display crosstalk level in advance. By simulations and experiments using the 3D display prototypes, the authors evaluated the effectiveness of proposed method.

Automated Number Plate Recognition Using Template Matching

2018 ◽  
Vol 6 (12) ◽  
pp. 298-304
Author(s):  
Nancy Aggarwal ◽  
Shilpa Sethi
Keyword(s):  
Template Matching

Implementasi Aplikasi Hijab Berbasis Android dengan Metode Canny Operator dan Template Matching Correlation

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Astri Oktavianti ◽  
Winarno Sugeng ◽  
Aldrian Agusta
Keyword(s):  
Template Matching ◽  
Canny Operator

Rapid speech keyword spotting method based on template matching

2013 ◽  
Vol 33 (11) ◽  
pp. 3138-3140
Author(s):  
Guoteng ZHU ◽  
Wei SUN
Keyword(s):  
Template Matching ◽  
Keyword Spotting

Method based on feature template matching to identify special icons in map

2013 ◽  
Vol 26 (7) ◽  
pp. 605-609 ◽  
Author(s):  
Yang Li ◽  
Xiaodong Zhang ◽  
Yuanlv Bao
Keyword(s):  
Template Matching ◽  
Feature Template

Ensemble Visual Content based Search and Retrieval for Natural Scene Images

2019 ◽  
Vol 12 ◽  
Author(s):  
Pushpendra Singh ◽  
P.N. Hrisheekesha ◽  
Vinai Kumar Singh
Keyword(s):  
Template Matching ◽  
Feature Descriptor ◽  
Natural Scene ◽  
Visual Content ◽  
Other Information ◽  
Similar Images ◽  
State Of Art ◽  
Natural Scene Images ◽  
Hybrid Methodology ◽  
Search And Retrieval

Content based image retrieval (CBIR) is one of the field for information retrieval where similar images are retrieved from database based on the various image descriptive parameters. The image descriptor vector is used by machine learning based systems to store, learn and template matching. These feature descriptor vectors locally or globally demonstrate the visual content present in an image using texture, color, shape, and other information. In past, several algorithms were proposed to fetch the variety of contents from an image based on which the image is retrieved from database. But, the literature suggests that the precision and recall for the gained results using single content descriptor is not significant. The main vision of this paper is to categorize and evaluate those algorithms, which were proposed in the interval of last 10 years. In addition, experiment is performed using a hybrid content descriptors methodology that helps to gain the significant results as compared with state-of-art algorithms. The hybrid methodology decreases the error rate and improves the precision and recall for large natural scene images dataset having more than 20 classes.

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