Characteristic features of geomonitoring during the installation of underground structures in a dense urban setting

1999 ◽  
Vol 36 (4) ◽  
pp. 140-147
Author(s):  
V. A. Il'ichev ◽  
P. A. Konovalov ◽  
N. S. Nikiforova
Keyword(s):  
Urban Setting ◽  
Underground Structures ◽  
Dense Urban Setting ◽  
Characteristic Features

Matching seismic activity with potential sources using machine Learning

2020 ◽  
Author(s):  
Gerrit Hein ◽  
Artemii Novoselov ◽  
Florian Fuchs ◽  
Götz Bokelmann
Keyword(s):  
Machine Learning ◽  
Citizen Science ◽  
Detection Algorithm ◽  
Urban Setting ◽  
Seismic Signals ◽  
Detection And Identification ◽  
Challenging Environment ◽  
Characteristic Features ◽  
Good Signal ◽  
Lower Magnitude

<p>Detecting seismic signals and identifying their origin is more and more used for understanding environmental activity. This usually depends on a good signal/noise ratio (S/N), especially for the more distant sources.</p><p>A test area for detection and identification is the urban setting of the University of Vienna, a challenging environment with more than 4000 strong-acceleration events per day. These repetitive noise events would normally classify the site as "too noisy" for any advanced earthquake research.</p><p>With the real-time open database from Wiener Linien it is possible to attribute many of the repetitive seismic signals (e.g. on a Raspberry Shake Citizen Science Station) to the surrounding trams and train lines. The detection challenge was initiated in a Citizen Science Hackathon, where public interest sparked this research. The available train schedule and more than one year of continuous seismic records is sufficient to train and test a machine learning classifier which finds most characteristic features in the signals of commuter trains and trams, such as the energy in each frequency band.</p><p>The labeled dataset can be used to train our detection algorithm to find similar signals and to help determine whether a certain signal is present or not. An additional second seismic Raspberry Shake sensor is installed in the vicinity, to further constrain the directionality of the trains.</p><p>Studying the vibrations of train signals and solving the classification task of these repetitive patterns first can help develop robust methods<br>for seismically loud environments, and might lead to the detection of lower magnitude events such as regional earthquakes or landslides. </p>

Conclusion: The Mendicants as a Mediterranean Phenomenon

2012 ◽  
Vol 18 (2-3) ◽  
pp. 272-289 ◽  
Author(s):  
Francisco García-Serrano
Keyword(s):  
Close Relationships ◽  
Urban Setting ◽  
Spiritual Needs ◽  
Social Agents ◽  
Dense Urban Setting ◽  
Mediterranean World ◽  
The Mediterranean

Abstract The medieval Mediterranean was an environment in which mendicant friars were able to thrive, attending to the spiritual needs of the populace and benefitting from the support of the urban classes, especially merchants, with whom they established close relationships. The examples of the convents of Barcelona, Ciutat de Mallorca and Florence presented in this volume clearly elucidate the association between the friars, the merchants and the urban aristocracy. Although the friars did not restrict their activities to the Mediterranean world and quickly expanded into other European kingdoms and remote lands, it was in the dense urban setting of this region where they first conducted their preaching and established their roles as active social agents.

Forest fires are associated with elevated mortality in a dense urban setting

2011 ◽  
Vol 69 (3) ◽  
pp. 158-162 ◽  
Author(s):  
Antonis Analitis ◽  
Ioannis Georgiadis ◽  
Klea Katsouyanni
Keyword(s):  
Forest Fires ◽  
Urban Setting ◽  
Dense Urban Setting

Deformations of structures during their erection in the dense urban setting of Saint Petersburg

2006 ◽  
Vol 43 (1) ◽  
pp. 33-36
Author(s):  
A. B. Fadeev ◽  
R. A. Mangushev ◽  
V. A. Lukin ◽  
A. V. Kuznetsov
Keyword(s):  
Urban Setting ◽  
Saint Petersburg ◽  
Dense Urban Setting

Bicycle helmets are highly protective against traumatic brain injury within a dense urban setting

Injury ◽  
2015 ◽  
Vol 46 (12) ◽  
pp. 2483-2490 ◽  
Author(s):  
Monica Sethi ◽  
Jessica Heidenberg ◽  
Stephen P. Wall ◽  
Patricia Ayoung-Chee ◽  
Dekeya Slaughter ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Urban Setting ◽  
Bicycle Helmets ◽  
Dense Urban Setting
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