An Attentional Model for Earthquake Prediction Using Seismic Data

2021 ◽  
pp. 53-64
Author(s):  
Alana de Santana Correia ◽  
Iury Cleveston ◽  
Viviane Bonadia dos Santos ◽  
Sandra Avila ◽  
Esther Luna Colombini
Keyword(s):  
Earthquake Prediction ◽  
Seismic Data ◽  
Attentional Model

Complex System for Earthquake Prediction, Warning and Local Assessment of Seismic Events

2021 ◽  
Vol 69 (1) ◽  
pp. 80-89
Author(s):  
Iosif LINGVAY ◽  
Victorin Emilian TOADER ◽  
Ovidiu CIOGESCU ◽  
Adriana BORȘ ◽  
Andrei MIHAI
Keyword(s):  
Complex System ◽  
High Risk ◽  
Real Time ◽  
Earthquake Prediction ◽  
Seismic Data ◽  
Seismic Waves ◽  
Seismic Events ◽  
Warning Signals ◽  
Specialized Software ◽  
Local Assessment

A complex system for zonal earthquake prediction, warning, and local assessment of seismic events has been designed, performed, implemented, and experimented/validated. The system was designed to ensure simultaneously: the reception of warning signals following earthquakes with the epicentre on a radius of 1000 km; acquisition of local precursor data for a possible prediction of seismic events with the epicentre in the perimeter of the targeted locality and/or improvement of the database in the field of Earth physics purchased and processed centrally at the national seismic dispatcher; acquisition of data on the intensity of local seismic movements, based on which, when a predetermined threshold considered dangerous is exceeded, a real-time action order is issued for the protection of high-risk equipment and installations in operation. The realized system is structured on the national seismic dispatcher DSN (with the role of seismic data acquisition from the territory) connected by a bidirectional communication system with a local dispatcher DL which is provided with a system for acquiring and storing local seismic data (vibration detector 3D and temperature transducer mounted in a 40 m deep drilled well, radon detector and associated parameters: temperature, pressure, and humidity of the air mounted at the mouth of the drilled well). The implemented system is able, through the specialized software implemented, to take over the warning signals received from the national seismic dispatcher, to process the locally acquired data, and after the local validation of the seismic event to issue real-time action command (when exceeding values of pre-established major risk threshold) of the protections of high-risk installations in operation in the targeted perimeter. The experimentation/validation of the system, of the interconnection networks, and of the specialized software of the implemented application was done both by continuously recording the local seismic parameters, verifying the communication between DSN and DL, and by taking two warnings regarding seismic events produced (on 30.10.2020  Mw = 7, Greece and on 22.10.2020, at 20:22 hours, ML = 4 R, Vrancea, RO). By processing the data recorded during these events, the speeds of seismic waves in the respective directions were calculated. Thus, for the event of 30.10.2020 Greece, a speed of seismic waves of 7,418 km/second was determined and for the event from 22.10.2020 Vrancea, at 20:22 hours, it was calculated that the secondary waves are moving with 12,686 km/second and the surface seismic waves with 5,063 km/second. Following the analysis/comparison of acceleration intensities with the pre-set threshold level recorded locally for potentially dangerous events, it was found that these events were felt in Râmnicu Vâlcea at a level below the pre-set danger threshold and consequently, the specialized software of the application did not generate a control signal for actuating the protection of high-risk equipment in operation.

scholarly journals Algorithm and program for earthquake prediction based on the geological, geophysical, geomorphological and seismic data

2016 ◽  
Vol 38 (3) ◽  
Author(s):  
Ngo Thi Lu* ◽  
Rodkin M. V. ◽  
Tran Viet Phuong ◽  
Phung Thi Thu Hang ◽  
Nguyen Quang ◽  
...  
Keyword(s):  
Earthquake Prediction ◽  
Seismic Data

scholarly journals Pattern Recognition on Seismic Data for EarthquakePrediction Purpose

2021 ◽  
Vol 14 ◽  
pp. 45-50
Author(s):  
Adel Moatti ◽  
Mohammad Reza Amin-Nasseri ◽  
Hamid Zafarani
Keyword(s):  
Earthquake Prediction ◽  
Seismic Data ◽  
B Value ◽  
Optimum Number ◽  
Distinctive Pattern ◽  
Qeshm Island ◽  
Main Shocks ◽  
Silhouette Index ◽  
Pattern Of Variation ◽  
Economical Losses

Earthquakes has been known as a destructive natural disaster. Due to high human casualties and economical losses, earthquake prediction appears critical. The b-value of Gutenberg Richter law has been considered as precursor to earthquake prediction. Temporal variation of b-value before earthquakes equal or greater than Mw = 6.0 has been examined in the south of Iran, the Qeshm island and around of this from 1995 to 2012. Clustering method by the k-means algorithm has been performed to find pattern of variation of b-value. Three clusters are obtained as optimum number of clusters by the Silhouette Index. Before all mentioned earthquakes greater than Mw = 6.0, cluster 1, which is known as a decrease in b-value has been seen. so decreasing b-value before main shocks as distinctive pattern has been considered. Also an approximate time of decrease has been determined.

scholarly journals Laboratory earthquake forecasting: A machine learning competition

2021 ◽  
Vol 118 (5) ◽  
pp. e2011362118
Author(s):  
Paul A. Johnson ◽  
Bertrand Rouet-Leduc ◽  
Laura J. Pyrak-Nolte ◽  
Gregory C. Beroza ◽  
Chris J. Marone ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Earthquake Prediction ◽  
Seismic Data ◽  
Earthquake Forecasting ◽  
Seismic Cycle ◽  
Pedagogical Tool ◽  
Failure Times ◽  
Testing Data ◽  
Earthquake Data

Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google’s ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a model for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance.

The Role of Visual Distractors in the Simon Effect

2017 ◽  
Vol 64 (6) ◽  
pp. 387-397 ◽  
Author(s):  
Luisa Lugli ◽  
Stefania D’Ascenzo ◽  
Roberto Nicoletti ◽  
Carlo Umiltà
Keyword(s):  
Simon Effect ◽  
Simon Task ◽  
Automatic Generation ◽  
Spatial Code ◽  
Attentional Model ◽  
Previous Evidence ◽  
Spatial Stimulus ◽  
Attentional Resources ◽  
Code Changes

Abstract. The Simon effect lies on the automatic generation of a stimulus spatial code, which, however, is not relevant for performing the task. Results typically show faster performance when stimulus and response locations correspond, rather than when they do not. Considering reaction time distributions, two types of Simon effect have been individuated, which are thought to depend on different mechanisms: visuomotor activation versus cognitive translation of spatial codes. The present study aimed to investigate whether the presence of a distractor, which affects the allocation of attentional resources and, thus, the time needed to generate the spatial code, changes the nature of the Simon effect. In four experiments, we manipulated the presence and the characteristics of the distractor. Findings extend previous evidence regarding the distinction between visuomotor activation and cognitive translation of spatial stimulus codes in a Simon task. They are discussed with reference to the attentional model of the Simon effect.

Formation geological depth image according to refraction and reflection marine seismic data

2017 ◽  
Vol 39 (6) ◽  
pp. 106-121
Author(s):  
A. O. Verpahovskaya ◽  
V. N. Pilipenko ◽  
Е. V. Pylypenko
Keyword(s):  
Seismic Data ◽  
Depth Image ◽  
Marine Seismic
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