Ground-Motion Prediction Equations Based on Data from the Himalayan and Zagros Regions

Sharma, M. L.; Douglas, John; Bungum, Hilmar; Kotadia, Jainish

doi:10.1080/13632460902859151

Cited by 138 publications

(56 citation statements)

References 32 publications

(31 reference statements)

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“…In India, due to scarcity of strong motion data, there are few region or site-specific GMPEs. In the present study, we used three well-accepted GMPEs developed for different regions of the world: those developed by Sharma et al [16], Boore and Atkinson [17], and Akkar and Bommer [18]. Sharma et al [16] developed GMPEs for the Himalayan regions of India using data from the Himalayas and the Zagros regions, on the premise that the seismotectonics of the two regions have considerable similarity.…”

Section: B Ground Motion Modelsmentioning

confidence: 99%

“…In the present study, we used three well-accepted GMPEs developed for different regions of the world: those developed by Sharma et al [16], Boore and Atkinson [17], and Akkar and Bommer [18]. Sharma et al [16] developed GMPEs for the Himalayan regions of India using data from the Himalayas and the Zagros regions, on the premise that the seismotectonics of the two regions have considerable similarity. Boore and Atkinson [17] developed GMPEs for active tectonic regions across the world, and Akkar and Bommer [18] developed GMPEs for active tectonic regions of Europe and the Middle East.…”

Section: B Ground Motion Modelsmentioning

confidence: 99%

“…The ground motion prediction equations provide expressions for the peak horizontal acceleration or the spectral acceleration at bedrock level in terms of m, the moment magnitude of the earthquake, and r, the shortest distance from the site to the earthquake [16]. The PHA or Sa at ground level can be obtained by multiplying the PHA or Sa at bedrock level with suitable amplification factors [28], [29].…”

Section: Earthquake Acceleration In Terms Of M and Rmentioning

confidence: 99%

See 2 more Smart Citations

An Investigative Study of Seismic Landslide Hazards

Vasudevan¹,

Sridharan²,

Kolathayar³

et al. 2016

Proceedings of the Conference on Recent Advances in Rock Engineering (RARE 2016)

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Abstract-A decade ago, in September 2006, the European Commission sanctioned Amrita University, Kerala, to design, develop, and deploy a wireless sensor network-based landslide monitoring system. The deployment was completed in June 2009, at the Anthoniar Colony Landslide in Munnar, Kerala, and the system has been in successful operation since then. Recently, the Government of India commissioned Amrita University to deploy a similar landslide monitoring system in the Himalayas. We selected the Chandmari Landslide in Gangtok City, Sikkim, as our deployment site. The deployment has been phased in two stages. The first stage, completed in June 2015, was a pilot deployment in which a 33.5 m deep borehole was drilled into rock, piezometers and inclinometers were installed, and rock samples were extracted and analyzed. Soil samples were extracted from elsewhere on the hill and tested. In the second stage, we will instrument other areas of Chandmari Hill, so that the entire region can be adequately monitored. Chandmari lies in a seismically active zone and has experienced earthquake-induced landslides in the past. In this paper, we present a thorough analysis, considering linear and gridded seismicity models and three sets of ground motion prediction equations, and estimate seismic hazard at Chandmari. In addition, we report, in detail, the results of the Chandmari soil and rock tests. We then describe a method that uses these soil and rock test results and seismic hazard analyses to estimate seismic landslide hazard at the site.

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Section: B Ground Motion Modelsmentioning

confidence: 99%

Section: B Ground Motion Modelsmentioning

confidence: 99%

Section: Earthquake Acceleration In Terms Of M and Rmentioning

confidence: 99%

See 1 more Smart Citation

An Investigative Study of Seismic Landslide Hazards

Vasudevan¹,

Sridharan²,

Kolathayar³

et al. 2016

Proceedings of the Conference on Recent Advances in Rock Engineering (RARE 2016)

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show abstract

“…Then, from the weighted combination of the results obtained from the tree logic, the peak ground acceleration of the Amol and its suburbs is calculated. This logic tree of eight relationships with the weights assigned is expressed in Figure 7 [15][16][17][18][19][20][21][22][23]. Using the above mentioned authentic, new and compatible attenuation relationships selected for the earthquakes occurred in the Amol area, the zoning maps of the horizontal peak ground accelerations are evaluated using the probabilistic seismic hazard analysis and the results are represented in Figures 8 and 9 for a risk of 10% and 2% in a life time 50 years (475 and 2475 years return period, similar to Iranian standard No.…”

Section: Zoning Of Horizontal Peak Ground Acceleration For Amol Areamentioning

confidence: 99%

“…He released his relationship based on 468 three-component strong motion data from five large earthquakes occurred in Iran, namely: Manjil, (1990), Sirach (1981), Golbaf (1980), Tabas (1979) and Naghan (1978). Also, Ghodrati et al in 2007 and [19,20], Sharma et al in 2009 [21], Yazdani and Kowsari in 2013 [22], and Campbell and Bozorgnia in 2014 [23] suggested new attenuation relations for PGA and PGV based on update data from earthquakes in Iran, with different coefficients for different parts of Iran.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Seismic Hazard Analysis Using the New Correlation Relationships for Magnitude Scales

Alizadeh

Pourzeynali

2018

CivileJournal

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Amol is one of the oldest cities located in north of Iran, Mazandaran province, and its history dates back to the pre-Islamic period. Amol is a city with an area about 3000 square kilometers, a population exceeding 370,000, and includes the old and famous neighborhoods that have a religious, commercial, and service with a long history background. Considering the importance of buildings constructed in this city and the need for their preservation and restoration on one hand, and the occurrence of many severe earthquakes in the past centuries, as well as the recent earthquakes of the last century, on the other hand, encourage us to study the seismicity of this city. Therefore, in this paper, by considering the historical and instrumental earthquakes recorded within a radius of 150 km around this city and the seismic mechanism of the faults located in this region, probabilistic seismic hazard analysis of the area is studied. Then, using the probabilistic relations of the seismic hazard analysis of the Kijko 2000 computer program, the seismicity parameters and the return periods of the earthquake magnitudes are obtained for the area, and at the end, the horizontal peak ground acceleration is zoned for this city.

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Ground motion models for regions with limited data: Data‐driven approach

Sreenath,

Basu,

Raghukanth

2024

Earthq Engng Struct Dyn

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The ground motion model (GMM) for response spectra is crucial to hazard studies and structural design. GMM developed for regions with abundant data can capture appropriate spectral attenuation characteristics. However, several seismically active regions, such as the Himalayas, have limited recorded data, and thus, traditionally, simulated, or other regional data are supplemented with the available data for developing a GMM. Despite data augmentation, GMMs developed for such regions cannot appropriately depict attenuation characteristics. Therefore, the paper presents a comprehensive study encompassing the development of data‐driven approaches to obtain GMM for response spectra using the Himalayan crustal data with inputs: magnitude, distance, site class, depth, and a flag corresponding to the horizontal and vertical components. In this regard, GMM using transfer learning is initially developed from a pre‐trained model using global crustal data. Additionally, GMM using the XGBoost approach with monotonic constraints enforced on inputs is developed. These approaches could not capture trends appropriately. Finally, a monotonic constrained transfer learning model is developed to appropriately depict magnitude saturation and scaling, magnitude‐dependent geometric spreading, anelastic attenuation, depth scaling, site amplification, and other desired characteristics.

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Ground-Motion Prediction Equations Based on Data from the Himalayan and Zagros Regions

Cited by 138 publications

References 32 publications

An Investigative Study of Seismic Landslide Hazards

An Investigative Study of Seismic Landslide Hazards

Probabilistic Seismic Hazard Analysis Using the New Correlation Relationships for Magnitude Scales

Ground motion models for regions with limited data: Data‐driven approach

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