Magnitude-scaling relations using period parameters τc and τpmax, for Tehran region, Iran

Heidari, Reza; Shomali, Zaher Hossein; Ghayamghamian, Mohammad Reza

doi:10.1093/gji/ggs012

Cited by 15 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their results indicated that the estimated local magnitude based on the vertical components produces more accurate results with smaller standard deviations for the τ max p approach. In that paper, the following scaling relationship was suggested for a pilot earthquake EWS in the Tehran region in order to provide a quick estimate of the earthquake magnitude with a standard deviation less than about 1:0 unit of magnitude (Heidari et al, 2013):…”

Section: Dataset and Analysismentioning

confidence: 99%

“…In this paper, we present empirical attenuation equations for estimation of strong ground motions (PGV), hypocentral distance and an optimum method for approximation of back azimuth for the Tehran region, assuming that magnitude is already known using an independent period parameter method (τ max p ) as presented by Heidari et al (2013). During 2009-2010, the Tehran disaster mitigation and management organization (TDMMO 1 ), affiliated with the Tehran municipality, installed and operated an online broadband accelerometer network with 10 three-component stations equipped with Guralp CMG-5T sensors, in cooperation with Japan International Cooperation Agency (JICA 2 ; Fig.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rapid Estimation of Peak Ground Velocity and Earthquake Location Using Small Magnitude Earthquakes in the Tehran Region, Iran

Heidari

Shomali

Ghayamghamian

2013

Seismological Research Letters

View full text Add to dashboard Cite

Section: Dataset and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid Estimation of Peak Ground Velocity and Earthquake Location Using Small Magnitude Earthquakes in the Tehran Region, Iran

Heidari

Shomali

Ghayamghamian

2013

Seismological Research Letters

View full text Add to dashboard Cite

“…They then send EEW information to the target sites before destructive seismic waves arrive (Allen and Kanamori, 2003). Over the past few decades, EEWSs have been shown to be an effective earthquake hazard mitigation approach and have been applied in many regions around the world, such as Japan (Hoshiba et al, 2008), Mexico (Aranda et al, 1995), Taiwan (Wu and Teng, 2002;Chen et al, 2015), California (Allen et al, 2009a), southern Italy (Zollo et al, 2009;Colombelli et al, 2020), and Iran (Heidari et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Magnitude Estimation for Earthquake Early Warning Using a Deep Convolutional Neural Network

Zhu

Song

et al. 2021

Front. Earth Sci.

View full text Add to dashboard Cite

Magnitude estimation is a vital task within earthquake early warning (EEW) systems (EEWSs). To improve the magnitude determination accuracy after P-wave arrival, we introduce an advanced magnitude prediction model that uses a deep convolutional neural network for earthquake magnitude estimation (DCNN-M). In this paper, we use the inland strong-motion data obtained from the Japan Kyoshin Network (K-NET) to calculate the input parameters of the DCNN-M model. The DCNN-M model uses 12 parameters extracted from 3 s of seismic data recorded after P-wave arrival as the input, four convolutional layers, four pooling layers, four batch normalization layers, three fully connected layers, the Adam optimizer, and an output. Our results show that the standard deviation of the magnitude estimation error of the DCNN-M model is 0.31, which is significantly less than the values of 1.56 and 0.42 for the τc method and Pd method, respectively. In addition, the magnitude prediction error of the DCNN-M model is not affected by variations in the epicentral distance. The DCNN-M model has considerable potential application in EEWSs in Japan.

show abstract

“…In the history of seismology [3], EEW played an essential role in protecting lifelines and economy in the world. In today's world, the implementations of EEW can be grossly divided into three types: (1) in operation, e.g., Romania [4], Turkey [5], Greece [6], America [7], Mexico [8], Chile [9], Japan [10][11][12]; (2) in progress, e.g., Italy [13][14][15], China (https://www.globaltimes.cn/content/1203521.shtml, accessed on 10 August 2021); (3) in planning, e.g., Iran [16,17] and Kyrgyzstan [18].…”

Section: Introductionmentioning

confidence: 99%

Next-Generation EEW Empowered by NDSHA: From Concept to Implementation

Zhang

Romanelli

et al. 2021

Geosciences

View full text Add to dashboard Cite

In this paper, we discuss a possible combination of Earthquake Early Warning (EEW) and Neo-deterministic Seismic Hazard Assessment (NDSHA), and propose a new warning model, EEW2.0. The aim is to provide a differentiated warning alert to various end-users based on the results of seismic hazard assessment evaluation. The implementation of such a system contains three basic steps: (a) classification of “potential to cause hazard” in terms of magnitude; (b) determination of the source areas and building a hazard database in terms of Modified Mercalli Intensity (MMI) maps, considering all possible earthquake scenarios in the source area, for the whole protected area; (3) equipping unique decision framework for specific end-users. When a damaging earthquake (M ≥ 5.0) is detected, EEW2.0 quickly matches the prepared MMI map by estimated magnitude and epicenter, then directly extracts the MMI value and issues an early warning to the public. With the great attention and resources put into the reduction in seismic and its secondary risk in the 21st century, the proposed EEW2.0 will likely play an active role in protecting lives and reducing economic losses.

show abstract

Magnitude-scaling relations using period parameters τc and τpmax, for Tehran region, Iran

Cited by 15 publications

References 16 publications

Rapid Estimation of Peak Ground Velocity and Earthquake Location Using Small Magnitude Earthquakes in the Tehran Region, Iran

Rapid Estimation of Peak Ground Velocity and Earthquake Location Using Small Magnitude Earthquakes in the Tehran Region, Iran

Magnitude Estimation for Earthquake Early Warning Using a Deep Convolutional Neural Network

Next-Generation EEW Empowered by NDSHA: From Concept to Implementation

Contact Info

Product

Resources

About