Himanshu Mittal

Ground motion estimation in Delhi from postulated regional and local earthquakes

Ground motions are estimated at 55 sites in Delhi, the capital of India from four postulated earthquakes (three regional Mw = 7.5, 8.0, and 8.5 and one local). The procedure consists of (1) synthesis of ground motion at a hard reference site (NDI) and (2) estimation of ground motion at other sites in the city via known transfer functions and application of the random vibration theory. This work provides a more extensive coverage than earlier studies (e.g., Singh et al., Bull Seism Soc Am 92:555–569, 2002; Bansal et al., J Seismol 13:89–105, 2009). The Indian code response spectra corresponding to Delhi (zone IV) are found to be conservative at hard soil sites for all postulated earthquakes but found to be deficient for Mw = 8.0 and 8.5 earthquakes at soft soil sites. Spectral acceleration maps at four different natural periods are strongly influenced by the shallow geological and soil conditions. Three pockets of high acceleration values are seen. These pockets seem to coincide with the contacts of (a) Aravalli quartzite and recent Yamuna alluvium (towards the East), (b) Aravalli quartzite and older quaternary alluvium (towards the South), and (c) older quaternary alluvium and recent Yamuna alluvium (towards the North).

Stochastic finite-fault modeling of M w 5.4 earthquake along Uttarakhand–Nepal border

In this work, an attempt has been made to simulate strong ground motion of Mw 5.4 earthquake in Kumaun region of Uttarakhand. The simulation is based on modified stochastic finite modeling technique with dynamic corner frequency (Motazedian and Atkinson in Bull Seismol Soc Am 95:995–1010, 2005). Ground motion is simulated for 24 sites, where a magnitude 5.4 earthquake was recorded. Synthesized ground motion is found in close agreement with recorded ones, when compared in terms of main characteristics such as peak ground acceleration (PGA), Fourier spectra, response spectra and duration. Decay of PGA values with distance is almost same as that of observed ones. Successful modeling of present earthquake gives the confidence to understand and quantify seismic hazard of different parts of Uttarakhand from earthquakes of different magnitudes.

Site Characterization of Strong‐Motion Recording Stations of Delhi Using Joint Inversion of Phase Velocity Dispersion and H/V Curve

Site characterization is one of the most important aspects of any strong‐motion instrumentation. Nowadays it has become common practice to provide the characterization details up to bedrock level. Without proper site characterization, strong‐motion records of any station cannot be fully utilized. In India, strong‐motion instrumentation sites were classified in three categories as per V S 30 values. These V S 30 values were estimated using the Borcherdt (1994) methodology, in which physical properties of visible soil layer are used for V S 30 estimation. Because this methodology does not use any field testing, the probability of getting erroneous results is very high. Hence, to get accurate assessment of site characteristics, we conducted field testing at 19 strong‐motion instrumentation sites in Delhi, India. The site characteristics assessed here are determined using joint inversion of multichannel analysis of surface waves and horizontal‐to‐vertical spectral ratio (HVSR) from ambient noise results simultaneously to estimate shear‐wave velocity profiles. The benefit of using this method is that it provides site characteristics assessed through shear‐wave velocity profiles up to much deeper soil strata. The results obtained from this analysis are further validated using ground response analysis from recorded ground motions. Further, the profiles obtained are studied for uncertainties using the computer program STRATA. Transfer functions obtained from STRATA are then compared with HVSR of ambient vibration records as well as HVSR from weak‐motion earthquake records available for the sites. These curves are found to well matching with each other in this study. Site characterization carried out here will be very useful for studies related to seismic‐hazard assessment of the Delhi region and studies related to attenuation models.

Performance of a Low‐Cost Earthquake Early Warning System (P‐Alert) and Shake Map Production during the 2018 Mw 6.4 Hualien, Taiwan, Earthquake

On 6 February 2018, anMw 6.4 earthquake struck near the city of Hualien, in eastern Taiwan with a focal depth of 10.4 km. The earthquake caused strong shaking and severe damage to many buildings in Hualien. The maximum intensity during this earthquake reached VII (> 0:4g) in the epicentral region, which is extreme in Taiwan and capable of causing damage in built structures. About 17 people died and approximately 285 were injured. Taiwan was one of the first countries to implement an earthquake early warning (EEW) system that is capable of issuing a warning prior to strong shaking. In addition to the official EEW run by the Central Weather Bureau (CWB), a low-cost EEW system (P-alert) has been deployed by National Taiwan University (NTU). The P-alert network is currently operational and is capable of providing on-site EEW as well as a map of expected ground shaking. In the present work, we demonstrate the performance of the P-alert network during the 2018 Hualien earthquake. The shake maps generated by the P-alert network were available within 2 min and are in good agreement with the patterns of observed damage in the area. These shake maps provide insights into rupture directivity that are crucial for earthquake engineering. During this earthquake, individual P-alert stations acted as on-site EEWsystems and provided 2–8 s lead time in the blind zone around the epicenter. The coseismic deformation (Cd) is estimated using the records of P-alert stations. The higher Cd values (Cd > 2) in the epicentral region are very helpful for authorities for the purpose of responding to damage mitigation.

Building Effects on the P‐Alert‐Based Real‐Time Shaking Map Determination

Recently, the P-wave-alert-device (P-alert) network, which is a dense array of microelectromechanical system (MEMS) accelerometers that was developed and installed by National Taiwan University for the purposes of earthquake early warnings, has recorded a large number of strong-motion records for moderate-to-large earthquakes throughout Taiwan. However, many of these stations are mounted on the vertical walls of buildings in ways such that further studies of the sensor-structure interactions on recorded acceleration data are required before the data is used in the production of high-quality shake maps. In this study, we collect the free-field accelerograms recorded by the Taiwan Strong-Motion Instrumentation Program (TSMIP) network that were operated by the Central Weather Bureau (CWB), where MEMS accelerometers were in the vicinity. Then, we compare the peak ground acceleration (PGA) ratio (R-value) between P-alert and TSMIP stations. Finally, we demonstrate how to use the R-value correction on the P-alert data, in order to rapidly produce high-resolution shake maps for relief work to be done soon after major earthquakes. At present, the shake maps produced by the P-alert network are posted automatically in real time on Facebook and are provided to the National Science and Technology Center for Disaster Reduction (NCDR) in order to allow for their relief work. These timely products provide improved information for disaster risk reduction, emergency preparedness, and emergency response.

Strong-Motion Instrumentation: Current Status and Future Scenario

Three chief tectonic sub-regions of India (GSI Seismotectonic Atlas of India and its environs. Kolkata: Geological Survey of India, 2000), are the mighty Himalayas along the north, the plains of the Ganges and other rivers, and the peninsula. The Himalayas consist primarily of sediments accumulated over long geological time in the Tethys. A number of efforts are being made to study seismic hazard from earthquakes originating from Himalaya for which networks of seismic instruments are required. Seismic networks provide crucial data to scientists and the public about recent earthquakes, both large and small. By increasing the density of seismic stations, we can rapidly detect and locate earthquakes to provide an advance alert, improve our understanding of earthquake rupture and the associated seismic hazard, and generate, in real-time, state-of health information. This paper outlines the status of strong-motion instrumentation program in India and its future scenario.

Testing the performance of earthquake early warning system in northern India

The main goal of present study is to test the functionality of an earthquake early warning (EEW) system (a life-saving tool), in India using synthesized data and recorded earthquake data from Taiwan. In recent time, India set up an EEW system in the central seismic gap along the Himalayan Belt, consisting of about 100 low-cost P-Alert instruments. The area, where these instruments are installed, is highly sensitive to the seismic risk with the potential of strong, major and great earthquakes. In the absence of recorded data from the Himalayas required for analysis of such system, we take advantage of recorded waveforms from Taiwan, to test the EEW system. We selected Taiwanese stations in good accordance with the Indian sensor network, to have a best fit in terms of inter station spacing. Finally, the recorded waveforms are passed through Earthworm software using tankplayer module. The system performs very well in terms of earthquake detection, P-wave picking, earthquake magnitude and location (using previously estimated regressions). Pd algorithm has been tested where the peak amplitude of vertical displacement is used for estimating magnitudes using previously regressed empirical relationship data. For the earthquakes located between Main Boundary Thrust and Main Central Thrust along with a matching instrumentation window, a good estimate of location, as well as magnitude is observed. The approach based on Pd for estimating magnitude works perfectly as compared to

Indian Strong Motion Instrumentation Network

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