Seismology
Earthquake Hazard
Started in a wintery morning on December 11, 1967 after Koyna M6.3
earthquake, CSIR-NGRI Seismological
Observatory (HYB) has made significant contribution to the study of
Seismology in India by producing very
high-quality seismological data followed by accurate analysis over the years.
The observatory has been
equipped with World Wide Standard Seismograph Network (WWSSN) equipment
comprising three component Benioff
Short Period Seismometer and Press Ewing Long Period Seismometer with
photographic paper recording initially
in the 1967. In addition to it, a Very Broadband seismometer with a digital recorder has been installed in 1989. The
seismological data has been shared with United States Geological Survey,
International Seismological Centre,
Berkshire, United Kingdom, National Centre for Seismology, New Delhi and
other national agencies.
The micro earthquake activity in and around Hyderabad has also been studied with Medchal Earthquake M4.5 (1983) being the largest followed by Gundipet Earthquake Swarm (1982), Saroornagar Earthquake Swarm (1984), Jubilee Hills Earthquake Swarm sequences in 1994, 1995, 1998, 2000 and 2017, Vanasthalipuram Earthquake swarm (2010) and Borabanda Earthquake swarm (2017 and 2020).
(The solid line indicates the mean values and +/− 1 standard deviation is represented by the dashed lines. Stars represent the predicted values and the observed PGA values are represented by circles)
Using core-refracted phases like SKS is popular for estimating azimuthal anisotropy. The parameterization of azimuthal anisotropy using the core refracted phases is done in terms of the polarization of the fast wave and the difference in the travel time between the fast and slow wave, that is delay time (e.g., as shown in Figure). These are used as proxies to decipher the past and present-day deformation patterns. The crustal and mantle deformation in the stable and actively deformed regions of India are extensively studied using the shear wave splitting technique. One such kind of work is carried out in Kumaon-Garhwal Himalaya (KGH) using core-refracted phases (XK(K)S) phases recorded at 53 broadband seismic stations. For this region, the northern part of the lesser Himalaya shows a slightly smaller delay time compared to its southern part, which is attributed to the weakening of azimuthal anisotropy caused by the dipping of the Indian lithosphere. Based on the orientation of fast polarisation azimuths (FPAs), the KGH region can be divided into four subregions. The strong ENE-WSW orientation in the central part could result from a slightly variable anisotropy in the crust to the upper part of the lithosphere or basal topography causing deflection of mantle flow. Also, the NW orientation in the southeastern part of KGH is associated with a shallow source within the lithosphere. Application of the spatial coherency technique to single-layered anisotropic parameters results in a depth of 220-240 km, implying that the dominant source of anisotropy could lie in the upper mantle.
Artificial intelligence (AI), Machine learning (ML) and Deep learning (DL) have gained increasing attention in the field of seismology in recent years. With the growth of digital seismic data and advances in computational power, AI/ML algorithms have been applied to various seismological problems, such as earthquake detection, early warning and seismic hazard assessment. Earthquake seismology uses a sequence of processing steps to monitor seismic activity. These steps were initially performed manually by skilled analysts and later were performed automatically with algorithms designed to detect impulsive phase arrivals. By applying AI/ML/DL algorithms one can automatically detect, locate and calculate magnitudes of earthquakes from seismograms. Motivated by the successful application of Deep-Learning models in seismic monitoring and growing amount of data set, applying the DL techniques to the ongoing Palghar (Maharashtra) earthquake swarm which started in November 2019. The technique could detect more than thousands of events with less magnitudes and compared the results those obtained from traditional techniques. DL algorithms are efficient and accurate tools for data reprocessing in order to better understand the space-time evolution of earthquake sequences. This study demonstrates that machine-learning-based earthquake catalogue development is now feasible and will yield new insights into earthquake behaviour.
A Broadband seismograph has been installed in the vicinity of the dam to monitor the micro earthquakes that occur around the dam. Strong Motion Accelerographs have been installed in the dam structure including dam crest and galleries as well as free field to assess the dynamic behaviour of the dam for near field earthquakes.
In India, the first observation of Reservoir Triggered Seismicity (RTS) was noticed in the vicinity of
Koyna dam just after its impoundment in 1962. This phenomenon successfully explains the observation of intra
plate seismicity which corresponds to the impoundment of the reservoir. Another reservoir, Warna was created
in 1985 and it also contributed to the RTS in the region. The seismicity occurs within a small region of 30
km X 20 km. In order to monitor the seismic activity, initially CSIR-NGRI took initiative to deploy digital
seismic stations, however at present 15 surface broadband and 8 borehole short-period seismic stations are
in operation. Previously, several observations have been made regarding the source processes and nature of
seismicity in the region, such as the correlation between the water level and occurrence of seismicity,
difference between the normal earthquake and RTS (Gupta et al., 1972a,b; Gupta and Rastogi, 1974; Gupta,
1992; Rastogi et al., 1997; Talwani, 1997). Recent analysis on variation in stress pattern and the
segregation of focal mechanisms enabled to derive a tectonic model with alternating cycles of strike-slip
and normal type (Rao and Shashidhar, 2016). Using the waveform inversion, a precise determination of focal
depths has been attempted using local seismic waveforms (Shashidhar et al., 2011) identified the Donachiwada
fault is the causative source for the 1967 Koyna earthquake (Mw6.3). A number of seismological studies have
been carried out in this region to understand the source mechanism and structure, however, the triggering
phenomenon of seismicity is poorly resolved. In this direction, to understand the role of fluid, pore
pressure, loading and unloading of the reservoirs and source mechanism a major initiative were taken by the
MoES to drill deep boreholes in and around the region. The main advantage of borehole observation is the
increased sensitivity due to the rapid decrease in noise wave intensity with depth, since the interference
consists mainly of surface waves. Scientific deep drilling in the region has revealed that the Deccan trap
has 932.5m thick and underlain by the basement rock (Roy et al., 2013). The major science objectives and
feasibility were discussed with the international community through ICDP workshops (Gupta and Nayak, 2011;
Shashidhar et al., 2016). The deployment of borehole seismic sensors is first of its kind in India. The high
signal to noise ratio waveforms has the potential not only to detect the sub M1.0 seismic events but also
can provide structural information with unprecedented resolution. The results show that the absolute errors
in locations of earthquake based on the borehole data ranges from 800 to 300m (Shashidhar et al., 2016;
2020). We aim for a detailed study of earthquake mechanism; to map the distribution of the faults based on
the micro-seismicity and also to achieve the accurate velocity structure associated with the fault zones in
this region.
Earthquakes of M≥4.0 in the Koyna-Warna region since 1967. The star (in
red) denotes the largest
earthquake
of M 6.3 of 10 December 1967. The open circles in white are the locations of
events without focal
mechanism
solutions, while the yellow ones are the recent ones with mechanisms. The
lines are the faults /
lineaments
inferred from LANDSAT images (Langston 1981). Also seen are the Koyna and
Warna reservoirs situated
about 25 km
apart, just east of the Western Ghat escarpment. (Rao and Shashidhar 2016)
Focal mechanism solutions in the Koyna–Warna region plotted as a function
of time from 1967 to 2017
show a
periodic variation of predominantly strike-slip (SS), normal type(N)
mechanisms over the years. The
dotted line
indicates the lower latitude limit of the M ≥ 4 earthquakes so far except for
the earthquake of Mw 4.0
of
2017. (Shashidhar et al. 2019).
Comparison of event locations obtained by combining surface and borehole
networks using the
‘HYPOCENTRE’ location algorithm (squares), event locations retrieved using
borehole network data and
a grid search algorithm (triangles) and event locations attained employing
borehole network data and a
cross-correlation relative location algorithm (circles). Western Ghats
escarpment as well as water
reservoirs
close to Kandavan and Paraleninai marked by arrows. Dashed line:
deep-reaching lineament taken from
Arora et al.
(2018). UDG: Udgiri surface station. (Shashidhar et al. 2020).
Individual splitting measurements (lines) made using the local S phases
plotted at the stations
(circles). Rose
diagram of the fast polarization azimuths (grey shaded) is shown for the
surface stations within the
rectangles
and at the respective stations (circle). At the respective stations, the
stereographic projection of the
fast
polarization azimuths and delay times (lines) are plotted as a function of
backazimuth and incidence
angle. In
each stereograph, the circles represent the incidence angle from 10° to 50°
with a 10° increment.
The orientation and length of the lines correspond to the fast polarization
azimuth and delay time,
respectively. The blue and magenta lines correspond to the surface and
borehole stations. The grey arrow
represents the absolute plate motion direction in a no-net rotation frame.
(Roy and Shashidhar 2023).
Seismological Imaging
Based on the earthquake dataset would be obtained from the seismic network and array of broadband stations stated below, we want to conduct the following research projects:
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By analysing earthquake data collected from the Ladakh, Himachal, and Rajasthan broadband seismic networks/arrays, we will create a comprehensive model of the three-dimensional structure of the Earth's crust and mantle. This will be achieved by the use of P-receiver function (PRF) modelling, joint inversion of PRF and surface wave dispersion data, Conversion Common Point (CCP) as well as H-k Stacking of radial PRFs, and SKS splitting studies.
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The study focuses on studying the seismo-tectonics of the indicated seismically active locations in India. This will be accomplished by simulating the source characteristics and moment tensors of local and regional earthquakes.
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Modelling ground motions and site responses in seismically active regions of India for assessing seismic hazards. This will be backed by available estimates of near-surface shear wave velocity (Vs30m).
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Using teleseismic earthquake velocity tomography, we will try to outline the fine 3-D P-wave velocity structure all the way to the mantle depths beneath the Ladakh Himalaya.
Over the past ten years, our group has been responsible for operating seismic networks in the Kachchh rift zone, Singhbhum craton, Uttarakhand Himalaya, and Dharwar Craton. With the help of existing earthquake data from the past and present seismic networks of NGRI, we have already obtained finer crustal and lithospheric thickness models of the Uttarakhand Himalaya, Rajasthan, Singhbhum and Dharwar cratons through joint inversion of P-RFs and surface wave dispersion data, CCP as well as H-k Stacking of radial PRFs, and SKS splitting study. The significant findings from our past research investigations are mentioned in the following:
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Local earthquake seismic tomography was used to identify crustal mafic pluton-induced intraplate earthquakes and swarm activity in Kachchh (Gujarat), Saurashtra (Gujarat), and Palghar (Maharashtra), respectively.
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Detection of crustal and lithospheric thinning beneath the Kachchh rift zone indicates the presence of the 65 Ma Deccan Mantle Plume.
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Identified the varying thicknesses of the Earth's crust and lithosphere in the Dharwar Craton and Deccan Volcanic Province
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Our research shows that vertical tectonics dominated crustal uplift and subduction in the Eastern Indian Craton during the Archean epoch.
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Our research simulated three NNE-SSW trending lithospheric transverse structures to reduce rupture lengths and seismic risk in the Uttarakhand Himalaya.
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Our research on earthquake hazard evaluation in Kachchh (Gujarat) includes 3-D crustal Vp and Vs models, 3D ground motion models, predictive ground motion attenuation relations, pseudo acceleration spectra, site amplification, and sediment thickness maps. These data can be used to design earthquake-resistant buildings.
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Our research on upper mantle anisotropy and mantle transition zones in India has helped us better comprehend the geodynamic evolution of the Indian subcontinent.
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Our study of crustal anisotropy in Uttarakhand Himalaya and Kachchh (Gujarat) has greatly improved our understanding of crustal processes.
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Evaluated the site response, attenuation, and seismic source characteristics in the Uttarakhand Himalaya region.
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Identified impact-related deformation at 800 m depth in the Lonar crater in Maharashtra using shallow velocity imaging.
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Construction of 3-D models for seismic velocity and attenuation in the crust of the Koyna region, Maharashtra.
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Conducted research on background noise spectra, site response, and crustal velocity models in Hyderabad, located in the Eastern Dharwar Craton.
Environmental Seismology
Major Inferences from the Study.
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Large flow events can be detected across a seismic network, and can be tracked as they move downstream.
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The NGRI network would have been sufficient for early warning of the 7 Feb event, with upto ~30 minutes warning for Joshimath, 10 minutes for Tapovan, and a few minutes for Rishi Ganga.
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Study suggests that early warning based on regional seismic networks is very much possible. [Rao et al., 2021, Science; Cook et al., 2021, Science]
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We initiated the development of seismic signal based Landslide Early Warning (LEW) system for Uttarakhand.
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Designed the machine learning based Automatic Seismic signal classifier in the first phase of LEW system.
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Detected the seismic signals of 15-07-2011 flood and landslides from seismic recordings in CSIR-NGRI Arunachal Pradesh Network.
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The stations at nearly 100 km able to record the landslide signals even in the high background noise.
Head of the group
Group Members
Dr. Suresh Gudapati
Senior Technical Officer(2)
Mr. Venkatesh Vempati
Senior Technical Officer(2)
Dr. Rajesh Rekapalli
Senior Technical Officer(2)
Mrs. Uma Anuradha M
Senior Technical Officer(2)
Mr. Prasad B. N. V
Senior Technical Officer(2)
Dr. Srinivas Dakuri
Senior Technical Officer(2)
Dr. Appala Raju P
Senior Technical Officer(2)
Mr. Borlakunta Laxman
Senior Technical Officer(1)
Mrs. Mallika K
Senior Technical Officer(1)
Mr. Sai Dixith M
Senior Technical Officer(1)
Mr. G Narsing Rao
Technician(1)
Mr. Sarma K R
Mrs. B. Annapurna