App Earthquake Detection and Automatic Mapping of Felt Area

2018 ◽  
Vol 90 (1) ◽  
pp. 305-312 ◽  
Author(s):  
Rémy Bossu ◽  
Robert Steed ◽  
Fréderic Roussel ◽  
Matthieu Landès ◽  
Amaya Fuenzalida ◽  
...  
Keyword(s):  
Earthquake Detection ◽  
Automatic Mapping ◽  
Felt Area

REEVALUATION OF THE JANUARY 2, 1912 NORTHERN ILLINOIS EARTHQUAKE: USING GIS AND COMPREHENSIVE MICROFILM ANALYSIS TO ASSESS FELT AREA, INTENSITY, AND EPICENTER

2017 ◽  
Author(s):  
Alexandre Golden ◽  
◽  
Mallorie Gomez ◽  
Kristin T. Huysken ◽  
Kazuya Fujita ◽  
...  
Keyword(s):  
Felt Area

scholarly journals Evaluation of GRACE/GRACE Follow-On Time-Variable Gravity Field Models for Earthquake Detection above Mw8.0s in Spectral Domain

2021 ◽  
Vol 13 (16) ◽  
pp. 3075
Author(s):  
Ming Xu ◽  
Xiaoyun Wan ◽  
Runjing Chen ◽  
Yunlong Wu ◽  
Wenbing Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Time Variable ◽  
Model Errors ◽  
Signal To Noise ◽  
Time Variable Gravity ◽  
Earthquake Detection ◽  
Gravity Fields ◽  
Variable Gravity ◽  
Gravity Recovery ◽  
Gravity Field Models

This study compares the Gravity Recovery And Climate Experiment (GRACE)/GRACE Follow-On (GFO) errors with the coseismic gravity variations generated by earthquakes above Mw8.0s that occurred during April 2002~June 2017 and evaluates the influence of monthly model errors on the coseismic signal detection. The results show that the precision of GFO monthly models is approximately 38% higher than that of the GRACE monthly model and all the detected earthquakes have signal-to-noise ratio (SNR) larger than 1.8. The study concludes that the precision of the time-variable gravity fields should be improved by at least one order in order to detect all the coseismic gravity signals of earthquakes with M ≥ 8.0. By comparing the spectral intensity distribution of the GFO stack errors and the 2019 Mw8.0 Peru earthquake, it is found that the precision of the current GFO monthly model meets the requirement to detect the coseismic signal of the earthquake. However, due to the limited time length of the observations and the interference of the hydrological signal, the coseismic signals are, in practice, difficult to extract currently.

scholarly journals Graph-partitioning based convolutional neural network for earthquake detection using a seismic array

2020 ◽  
Author(s):  
Keisuke Yano ◽  
Takahiro Shiina ◽  
Sumito Kurata ◽  
Aitaro Kato ◽  
Fumiyasu Komaki ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Graph Partitioning ◽  
Seismic Array ◽  
Earthquake Detection

scholarly journals A Numerical Study of Fluid Structure Interaction of a Flexible Submerged Cylinder Mounted on an Experimental Rig

2015 ◽  
Author(s):  
Erkan Cakir ◽  
Ayhan Akinturk ◽  
Alejandro Allievi
Keyword(s):  
Numerical Study ◽  
Coupled Analysis ◽  
Mapping Algorithms ◽  
Automatic Mapping ◽  
Structural Responses ◽  
Submerged Cylinder ◽  
Physics Model ◽  
System Coupling ◽  
Force Data ◽  
Experimental Rig

The aim of the study is to investigate VIV effects, not only on a test cylinder but also on the experimental rig being towed under water at a prescribed depth and operating speeds. For this purpose, a numerical Multi-Physics model was created using one way coupled analysis simultaneously between the Mechanical and Fluent solvers of ANSYS software package. A system coupling was developed in order to communicate force data alternately between the solvers with the help of automatic mapping algorithms within millesimal time periods of a second. Numerical investigation into the dynamic characteristics of pressure and velocity fields for turbulent viscous fluid flow along with structural responses of the system, stressed the significance of time and space scales for convergence and accuracy of our Finite Volume (FV) CFD calculations.

scholarly journals QuakeMigrate: a Modular, Open-Source Python Package for Automatic Earthquake Detection and Location

2021 ◽  
Author(s):  
Tom Winder ◽  
Conor Bacon ◽  
Jonathan Smith ◽  
Thomas Hudson ◽  
Tim Greenfield ◽  
...  
Keyword(s):  
Open Source ◽  
Earthquake Detection ◽  
Python Package

Property of long term (1990-2009) seismicity in the region of L’Aquila Mw6.1 earthquake revealed from machine learning 

2021 ◽  
Author(s):  
Josipa Majstorović ◽  
Piero Poli
Keyword(s):  
Epicentral Distance ◽  
Large Earthquake ◽  
Historical Seismicity ◽  
Central Apennines ◽  
Small Magnitude ◽  
Earthquake Detection ◽  
Abruzzo Region ◽  
Background Seismic Activity ◽  
The City

<p>On April 6th 2009 (01:32 UTC) strong earthquake of magnitude M<sub>W</sub>6.1 occurred near the city of L’Aquila in the Abruzzo region in the Central Apennines of Italy. Due to the extensional processes the Abruzzo region is characterized by prominent historical seismicity. However, before the 2009 event the background seismic activity is characterised as sparse and mostly clustered in space and time. The general lack of events, especially small magnitude events before the 2009 event motivated us to study the long-term near-fault seismicity before the large earthquake occurrence. To achieve this we first have to extend the existing catalog. We take into consideration the data from the AQU (42.354, 13.405) station that has been recorded in the city of L’Aquila, near Paganica fault responsible for the 2009 event, during an extensive period of 29-years, 19 years before the event itself. The catalog extension is performed by applying the two-stage convolutional neural network pipeline for earthquake detection and characterisation (epicentral distance and magnitude) using three component signal station waveforms. The algorithm allows us to successfully detect ~800 local events (less than 10 km from the AQU station) in the period 1990-2009. We here present a detailed analysis of this catalog including waveforms characterization to derive new insights about the long term preparation processes(es) occuring before the 2009 M<sub>w</sub>6.1 earthquake.</p>

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