A study of characteristics of ground motion response spectra from earthquakes recorded in NE Himalayan region: an active plate boundary

Sharma, Babita; Chopra, Sumer; Chingtham, Prasanta; Kumar, Vikas

doi:10.1007/s11069-016-2543-2

Cited by 14 publications

(9 citation statements)

References 16 publications

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“…10 and 160 km distances (Fig.4d;Singh et al, 2003; online strong-motion dataset from the SMA network of Roorkee (India);Raghukanth and Iyengar, 2007). A similar plot for the 2009 M w 6.2 Bhutan earthquake from the NE India (Fig.4e) shows a good correlation between the predicted attenuation curve and observed PGA values(Sharma et al, 2016). The observed PGA values of M w 5.6 Kachchh aftershocks of 7March and 6 April 2006, and 6 February 2017 Uttarakhand earthquake are plotted with our modelled ground-motion attenuation curve for an M w 5.6 earthquake in the Fig.4f, showing a good correlation between observed and predicted PGA values between 10 and 160 km distances(Mandal et al, 2009;Mandal and Asano, 2019;Sharma et al, 2021).…”

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confidence: 62%

Peak Ground Acceleration Prediction using supervised Machine Learning algorithm for earthquakes of Mw5.6-7.9 occurring in India and Nepal

Mandal

2022

Preprint

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The absence of key input parameters and use of least squares technique have resulted in most of the available empirical ground motion prediction models (GMPEs) in India, as a function of epicentral distances and magnitudes, resulting in large uncertainty in the predictive model. Here we apply a supervised Machine Learning technique (XGBoost) to design a robust improved GMPE of peak ground acceleration (PGA) for the Indian sub-continent and Nepal, utilizing independent input parameters viz., moment magnitudes, hypocentral distances, focal depths and site proxy (in terms of average seismic shear-wave velocity from the surface to a depth of 30m (Vs30)). This has been achieved using PGA dataset of nine earthquakes of Mw5.6-7.9 from Nepal Himalaya, Uttarakhand Himalaya, Kachchh (Gujarat), and Koyna (Maharashtra). Our dataset also includes 225 strong-motion records of 30 significant Bhuj aftershocks of Mw3.3-5.0 (during 2002–2008) with epicentral distances ranging from 1.0 to 288 km. We also test the ground motion predictability of our supervised ML model using the whole dataset and randomized test dataset (30% of total data points). The correlation coefficient of observed and predicted PGA values for the whole dataset is found to be ±0.994 while our predictability on the randomized test dataset suggests a correlation coefficient of 0.944. Also, the model predictability of our supervised ML model suggests a good prediction of the observed PGA dataset for twelve Indian and Nepal earthquakes of Mw5.6-7.9 (viz., the 2001 Bhuj (Gujarat) earthquake sequence, the 2015 Nepal earthquake sequence, the 1999 Chamoli earthquake, the 1967 Koyna earthquake, the 2009 Mw6.2 Bhutan event and two Mw5.9 Myanmar-India border earthquakes of September 2009 and January 2013), suggesting that our ML GMPE model would work for earthquakes occurring in India and Nepal, within the range of Mw [3.3–7.9], focal depths [3–94 km], epicentral distances [1-565 km] and Vs30 [177–760 m/s].

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confidence: 62%

Peak Ground Acceleration Prediction using supervised Machine Learning algorithm for earthquakes of Mw5.6-7.9 occurring in India and Nepal

Mandal

2022

Preprint

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“…Most of the stations are situated more or less 100 km away from the source of the 03 January 2016, Tamenglong earthquake. It is to be noted that the Kohima site is situated on rocks, Itanagar site is situated on stiff soil with a fractured rock while Lekhapani and Jorhat sites are situated on alluvium, Mokokchung site is situated on rocks (Tertiary rock) and Tadong site is situated on Quaternary soils (Sharma, 2016). Thus the estimated parameters are a bit different which represent the site characteristics embedded with different site conditions.…”

Section: Discussionmentioning

confidence: 99%

“…The earthquake records used for processing were originated in and around Northeast India and its vicinity. The strong motion records data set are used in this study, where maximum sites in NE India fall in the Quaternary category (Sharma et al 2016, Chopra et al 2018. It is perceived that GMPs (e.g PGA) amplitude varies with site conditions, distance, and site characteristics .…”

Section: Discussionmentioning

confidence: 99%

“…The group of hard rock includes the rocky ground of various strengths, dense clay, pebble, and gravel while the soft rock/ soil sites comprise sand, clay, and alluvium soil. The majority of the studied area is covered by quaternary formation Chopra, 2018;Sharma, 2016).…”

Section: Databasementioning

confidence: 99%

“…List of SMA stations and its details (Source of geology map of India, Chopra, 2017;Sharma, 2016): (http://www.mapsofindia.com/maps/india/geological.htm)…”

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Regression Analysis On Ground Motion Parameters For The Earthquakes (Mw≥4.0) in NE India With Special Emphasis On 3 Jan 2016 M6.7 Tamenglong Earthquake

Gogoi,

Baruah,

Sharma

2021

Preprint

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In the present study, ground motion parameters are estimated by processing 26 number of Earthquake events which contain 128 accelerogram records with 384 components of earthquakes that originated in NE, India and its vicinity with special emphasis on 3 Jan 2016 M6.7, Tamenglong Earthquake. It is observed that the peak ground acceleration (PGA) is dependent on distance, site conditions, and site characteristics. The linear regression relations developed for GMPs with distance can be applied to characterize sites having three kinds of geology- Quaternary, Tertiary, and Precambrian. Attenuation relation curves of ground motion parameters (PGA, PGV, Ia, Ic, CAV, HI, EDA, ASI) of three different magnitudes (Mw=5.4, 5.5, and 5.8) are estimated for NS and EW components. The average response acceleration of horizontal components for all these sites is equivalent to 0.0485g for 5% damping with reference to the average period of 0.234s and for the vertical component is around 0.026g to the average period of 0.18s. January 3, 2016, Mw=6.7, Tamenglong earthquake, Manipur is one of the highest magnitudes in NER used in this study. Estimated Peak Ground Acceleration (PGA) at Itanagar, Jorhat, Kohima, Lekhapani, Mokokchung and Tadong sites are 0.0289g, 0.0145g, 0.053g, 0.00510g, 0.022g, 0.00159g respectively, while the Peak Ground Velocity (PGV) values are 0.0166m/s, 0.0097m/s, 0.050m/s, 0.0022m/s, 0.0195868m/s, 0.0009498m/s respectively. The PGA estimated for the event is found to be maximum at Kohima. Response spectra indicate that response acceleration mostly varies with periods 0.2s and 0.5s, and beyond 2s there is no effective response acceleration. The maximum effective response accelerations for all the sites are observed within the frequency range of 0.5 Hz to 5 Hz. Pseudo response acceleration (0.2802g at 1.72 Hz) along with estimated engineering parameters was observed to be higher in Kohima site situated on hard rock with a low epicentral distance of about 120 km. These relations help to forecast an average value for the GMPs estimated at a particular site at a certain distance from a source which is a prime input for seismic hazard analyses and the development of design ground motions.

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Simulation of strong ground motion due to active Sohna fault in Delhi, National Capital Region (NCR) of India: an implication for imminent plausible seismic hazard

et al. 2020

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A study of characteristics of ground motion response spectra from earthquakes recorded in NE Himalayan region: an active plate boundary

Cited by 14 publications

References 16 publications

Peak Ground Acceleration Prediction using supervised Machine Learning algorithm for earthquakes of Mw5.6-7.9 occurring in India and Nepal

Peak Ground Acceleration Prediction using supervised Machine Learning algorithm for earthquakes of Mw5.6-7.9 occurring in India and Nepal

Regression Analysis On Ground Motion Parameters For The Earthquakes (Mw≥4.0) in NE India With Special Emphasis On 3 Jan 2016 M6.7 Tamenglong Earthquake

Simulation of strong ground motion due to active Sohna fault in Delhi, National Capital Region (NCR) of India: an implication for imminent plausible seismic hazard

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