Probabilistic Models for Forecasting Earthquakes in the Northeast Region of India

Sil, Arjun; Sitharam, T. G.; Haider, Syed

doi:10.1785/0120140361

Cited by 24 publications

(13 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A well assessed earthquake catalogue of magnitude ≥ 4.0 has been taken from Sil et al [2015], from year 1737 to 2011 (274 years) in the study area within East longitudes 87.590° to 98.461° and North lati-tudes 19.345° to 29.431°. Sil et al [2015] have collected preinstrumental (historic) seismic events from different literatures [Oldham 1883, Basu 1964, Rastogi 1974, Chandra 1977, Dunbar et al 1992, Bilham 2004 and instrumental data from various national and international seismological agencies, such as IMD, BARC, NGRI (National agencies) and USGS, ISC, COSMO Virtual Data Center (International agencies). They have removed all the repeated events (from different seismological agencies) and declustered the foreshocks and aftershocks using methodology given by Gardner and Knopoff [1974] and modified by Uhrhammer [1986].…”

Section: Earthquake Datamentioning

confidence: 99%

“…The size of space and time window to decluster foreshocks and aftershocks are given as e-1.024+0.804*Mw (km) and e -2.87+1.235*Mw (days) respectively. Sil et al [2015] have converted different types of magnitude scale into a common magnitude scale i.e. moment magnitude (Mw) using relationships proposed by Sitharam and Sil [2014].…”

Section: Earthquake Datamentioning

confidence: 99%

“…In the past, several statistical distri-bution models have been proposed for forecasting of future earthquakes including double exponential [Utsu 1972b], Gaussian [Rikitake 1974], Weibull [Hagiwara 1974, Rikitake 1974, Gamma [Utsu 1984] and Lognormal [Nishenko and Buland 1987]. This type of study has been carried by many researchers for different areas Ram 1997, 1999;Yilmaz et al 2004, Yilmaz and Celik 2008, Yadav et al 2008, Sil et al 2015.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Sil et al [2015] have estimated conditional probabilities for forecasting of future earthquakes (Mw > 6.0) in the northeast region of India using earthquake catalogue from year 1737 to 2011. They have divided their study area (longitudes 86.20°E-97.30°E and latitudes 18.40°N-29.00°N) into six regions based on seismic event distribution pattern and orientation of all seismic sources or faults.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Artificial neural networks (ANN) and stochastic techniques to estimate earthquake occurrences in Northeast region of India

Zarola

Sil²

2017

Annals of Geophysics

Self Cite

View full text Add to dashboard Cite

The paper presents the probability of earthquake occurrences and forecasting of earthquake magnitudes size in northeast India, using four stochastic models (Gamma, Lognormal, Weibull and Log-logistic) and artificial neural networks, respectively considering updated earthquake catalogue of magnitude Mw ≥ 6.0 that occurred from year 1737 to 2015 in the study area. On the basis of past seismicity of the region, the conditional probabilities for the identified seismic source zones (12 sources) have been estimated using their best fit model and respective model parameters for various combinations of elapsed time (T) and time interval (t). It is observed that for elapsed time T=0 years, EBT and Kabaw zone shows highest conditional probability and it reaches 0.7 to 0.91 after about small time interval of 3-6 years (2014-2017; since last earthquake of Mw ≥ 6.0 occurred in the year 2011) for an earthquake magnitude Mw ≥ 6.0.Whereas, Sylhet zone shows lowest value of conditional probability among all twelve seismic source zones and it reaches 0.7 after about large time interval of 48 years (year 2045, since last event of Mw ≥ 6.0 occurred in the year 1999). While for elapsed time up to 2016 from the occurrence of the last earthquake of magnitude Mw ≥ 6.0, the MBT and MCT region shows highest conditional probability among all twelve seismic source zones and it reaches 0.88 to 0.91 after about 6-7 (2022-2023) years and in the same year (2022-2023) Sylhet zone shows lowest conditional probability and it reaches 0.14-0.17. However, we have proposed Artificial Neural Network (ANN) technique, which is based on feedforward backpropagation neural network model with single hidden layer to estimate the possible magnitude of future earthquake in the identified seismic source zones. For conditional probability of earthquake occurrence above 0.8, the neural network gives the probable magnitude of future earthquake as Mw 6.6 in Churachandpur-Mao fault (CMF) region in the years 2014 to 2017, Mw 6.8 in Myanmar Central Basin (MCB) region in the years 2013 and Kabaw region in the years 2015-2018, respectively. The epicentre of recently occurred 4 January 2016 Manipur earthquake (M 6.7), 13 April 2016 Myanmar earthquake (M 6.9) and the 24 August 2016 Myanmar earthquake (M 6.8) are located in Churachandpur-Mao fault (CMF) region Myanmar Central Basin (MCB) region and EBT and Kabaw region, respectively and that are the identified seismic source zones in the study area which show that the ANN model yields good forecasting accuracy.

show abstract

Section: Earthquake Datamentioning

confidence: 99%

Section: Earthquake Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial neural networks (ANN) and stochastic techniques to estimate earthquake occurrences in Northeast region of India

Zarola

Sil²

2017

Annals of Geophysics

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the recent past, researchers have found a seismic gap known as Assam gap (Khattri & Wyss 1978) since 1950 in between the Eastern Himalaya (EH), Shillong Plateau (SP), and IndoBurma Ranges (IBR) with the Eurasian plate for a long time in this belt comparison to the past history. They have estimated the return period and are expecting a big earthquake in this region at any time in future (Kayal 1998;Khattri & Wyss 1978); Yadav et al 2010;Sil et al 2015). In 2011, the state experienced 37 shocks having magnitudes ranging from 2.5 to 6.9.…”

Section: Introductionmentioning

confidence: 99%

Site specific design response spectrum proposed for the capital city of Agartala, Tripura

Sil

Sitharam

2015

Geomatics, Natural Hazards and Risk

Self Cite

View full text Add to dashboard Cite

The design response spectrum is smooth in shape compared to other response spectra. The objective of design spectra is to estimate the possible earthquake lateral loads which may experience on a particular structure during its design life. In this study, an attempt has been made to develop, design response spectra for the Agartala city, Tripura which is one of the North Eastern states in India considered at the highest level of seismic activity in the country having a zone factor 0.36g as per Indian seismic code (BIS 1893(BIS -2002. Based on the present data set collected from 1731-2011, the region is characterized and the seismicity parameters estimated separately for each source zone by Gutenberg and Richter (G-R) relationship. The two ground motion models were used for the hazard prediction. However, hazard curve, and uniform hazard response spectra (UHRS) (2% and 10% probability level for 50 years) has been developed for the Agartala at seismic bedrock level condition. Further, the NEHRP (National Earthquake Hazards Reduction Program) site classes D and E-types have been identified based on the average shear wave velocity at an upper 30 m depth of the subsurface soil profile. The direct shear wave velocity profiles were obtained through multi channel analysis of surface wave tests conducted at 27 locations at Agartala city. The design response spectra have been developed at the surface level for both the site classes. The results could be used as direct inputs for earthquake resistant design of civil engineering infrastructures of the study area.

show abstract

Assessment of Earthquake Recurrence for North India and Surrounding Region Using Probabilistic Models

Nayak¹,

Sitharam

2022

Lecture Notes in Civil Engineering

View full text Add to dashboard Cite

Probabilistic Models for Forecasting Earthquakes in the Northeast Region of India

Cited by 24 publications

References 30 publications

Artificial neural networks (ANN) and stochastic techniques to estimate earthquake occurrences in Northeast region of India

Artificial neural networks (ANN) and stochastic techniques to estimate earthquake occurrences in Northeast region of India

Site specific design response spectrum proposed for the capital city of Agartala, Tripura

Assessment of Earthquake Recurrence for North India and Surrounding Region Using Probabilistic Models

Contact Info

Product

Resources

About