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Modeling of the strong ground motion of 25th April 2015 Nepal earthquake using modified semi-empirical technique

Lal S., Joshi A., Sandeep, Tomer M., Kumar P., Kuo C.-H., Lin C.-M., Wen K.-L., Sharma M. L.

Acta Geophysica

2018

Vol. 66, no. 4

461--477

On 25th April, 2015 a hazardous earthquake of moment magnitude 7.9 occurred in Nepal. Accelerographs were used to record the Nepal earthquake which is installed in the Kumaon region in the Himalayan state of Uttrakhand. The distance of the recorded stations in the Kumaon region from the epicenter of the earthquake is about 420–515 km. Modified semiempirical technique of modeling finite faults has been used in this paper to simulate strong earthquake at these stations. Source parameters of the Nepal aftershock have been also calculated using the Brune model in the present study which are used in the modeling of the Nepal main shock. The obtained value of the seismic moment and stress drop is 8.26 9 1025 dyn cm and 10.48 bar, respectively, for the aftershock from the Brune model .The simulated earthquake time series were compared with the observed records of the earthquake. The comparison of full waveform and its response spectra has been made to finalize the rupture parameters and its location. The rupture of the earthquake was propagated in the NE–SW direction from the hypocenter with the rupture velocity 3.0 km/s from a distance of 80 km from Kathmandu in NW direction at a depth of 12 km as per compared results.

Prediction of seismicity cycles in the Himalayas using artificial neural network

Sharma M. L., Arora M. K.

Acta Geophysica Polonica

2005

Vol. 53, nr 3

299-309

In this paper, cyclic behaviour of seismicity cycles in the Himalayas has been exploited to predict the future earthquake activity using Artificial Neural Network (ANN). The Himalayan region has been divided into six seismogenic zones. A feed forward multi-layer ANN has been used to evaluate the seismicity fluctuation in the time series containing data from historical times to 1998 for each zone. The most widely used Back Propagation Algorithm (BPA) is applied to train the neural network. BPA iteratively minimises an error function over the network outputs and a set of target outputs taken from the training data set. The results show that the probability of occurrence of moderate to great earthquake in next 50 years is relatively lower in the Hindukush-Pamirs zone. Since the intense release of energy will take place in the Kashmir-Himachal Pradesh zone, between 2030 to 2055, the probability of occurrence of moderate to great earth-quake is higher. The accumulation of energy stage is still going on in the India -Western Nepal Border zone, and there will be an increase in seismic activity after 2030 for the next 50 years. The hazard parameters could not be estimated for the Nepal-India-Sikkim Border zone because of lesser number of data to capture cyclic behaviour. In NE India, intense release and remnant release will take place up to 2030 due to which there will be an increase in the probability of occurrence of moderate to great earthquake in this zone. In Burma-Andaman Nicobar, the energy accumulation stage for the next cycle has started in 1990 and will continue till 2020.