EQcorrscan: Repeating and Near‐Repeating Earthquake Detection and Analysis in Python

2017 ◽  
Vol 89 (1) ◽  
pp. 173-181 ◽  
Author(s):  
Calum J. Chamberlain ◽  
Chet J. Hopp ◽  
Carolin M. Boese ◽  
Emily Warren‐Smith ◽  
Derrick Chambers ◽  
...  
Keyword(s):  
Earthquake Detection ◽  
Repeating Earthquake

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 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>

Artificial neural network-based seismic detector

1995 ◽  
Vol 85 (1) ◽  
pp. 308-319 ◽  
Author(s):  
Jin Wang ◽  
Ta-Liang Teng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Input Data ◽  
Seismic Event ◽  
Signal To Noise Ratio ◽  
Type B ◽  
Earthquake Detection ◽  
Seismic Event Detection ◽  
Artificial Neural ◽  
Better Than

Abstract An artificial neural network-based pattern classification system is applied to seismic event detection. We have designed two types of Artificial Neural Detector (AND) for real-time earthquake detection. Type A artificial neural detector (AND-A) uses the recursive STA/LTA time series as input data, and type B (AND-B) uses moving window spectrograms as input data to detect earthquake signals. The two AND's are trained under supervised learning by using a set of seismic recordings, and then the trained AND's are applied to another set of recordings for testing. Results show that the accuracy of the artificial neural network-based seismic detectors is better than that of the conventional algorithms solely based on the STA/LTA threshold. This is especially true for signals with either low signal-to-noise ratio or spikelike noises.

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