Seismology

Earthquake Hazard

CSIR-NGRI Seismological Observatory (HYB)

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.

July 10, 2024, 07:14:54.3hours IST, 19.417N, 77.410E, 4.5ML, 28km NNE of Nanded (Maharashtra)

The observatory has made significant contributions in understanding the Intra Plate Earthquakes in Peninsular Shield and Himalayan Region including Koyna Earthquake M6.3 (1967), Bhadrachalam Earthquake M5.7 (1969), Broach Earthquake M5.4 (1970), Mudhol Earthquake Swarm (1984), Killari Earthquake M6.3 (1993), Jabalpur Earthquake M5.8 (1997), Chamoli Earthquake M6.8 (1999), Bhuj Earthquake M7.7 (2001), Nanded Earthquake Swarm (2007), Chakalikoda (Nellore district) Earthquake Swarm (2016-2017), Vijayapura Earthquake swarm (2021) 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).

Strong Ground Motion Seismology

Comparison of the observed and predicted PGA values with GMPEs.

India is one of the most seismically active regions in the world and the Himalayan arc in particular pose a high seismic risk in northern India as well as the adjoining nations. According to the current seismic zone map of India around 59% of the country is prone to earthquakes. The Himalayas has already experienced 3 catastrophic earthquakes with M > 8.0 in past 1897 Shillong (M8.2); 1934 Bihar-Nepal (M8.4); and 1950 Assam-Tibet (M8.7) (Bilham 2011) which resulted in loss of many lives and infrastructures. But if any earthquake with similar size happens in the Himalayan region in the near future, it will adversely affect several millions of lives and monetary loss would be of multi-billion US dollars. Therefore, it is necessary to monitor seismicity continuously in the Himalayan region and delineate the potential zone of future great earthquake and evaluate seismic hazard levels unfailingly. CSIR – NGRI has established a seismic network in Himalayan region and is continuously expanding the network which includes both Broadband seismographs and Strong motion accelerographs. The Seismic Hazard studies in Himalayan region focuses on understanding characteristics of strong ground motions through modeling of strong ground motions, evaluation and development of empirical relations, evaluation of source, medium and site properties. As an example, results from Sharma et. al. 2021 are shown where researchers evaluated ground motion parameters for seismic hazard assessment from two moderate sized earthquakes occurred during 2017 in Uttarakhand region. The observed ground motion parameters (Peak ground accelerations) are compared with results from simulation and attenuation relationships (now known as GMPEs) developed for shallow crustal earthquakes. (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)

Seismic Anisotropy

Map showing Individual shear wave splitting measurements plotted at a depth of 200 km beneath the Kumaon-Garhwal Himalaya

Prolonged deformation due to tectonic activity results in the development of anisotropy in the crust and upper mantle. Different factors generate anisotropy at various depth ranges, such as fluid-filled cracks, stratigraphy or fault-zone fabrics in the upper crust and lattice-preferred orientation (LPO) of minerals in the lower crust and upper mantle. In the upper mantle, the LPO of minerals (mainly olivine) is generally caused by dislocation creep. Shear wave splitting, an elastic analogue of optical birefringence, offers an effective means to measure seismic anisotropy. When a shear wave passes through an anisotropic medium, it splits into fast and slow waves, with the former polarized parallel to the anisotropic fast axis and the latter polarized orthogonal to it. 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.

Applications of AI/ML in seismology
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.

Seismic Monitoring of Dams

CSIR-NGRI carries out Seismic monitoring of 24 Dams including Nagarjuna Sagar Dam, Srisailam Dam and Sriram Sagar Dam in Andhra Pradesh and Telangana states, Dhamni (Surya) Dam and Bhatsa dam in Maharashtra state and Mullai Periyar Dam, Mettur Dam, Sholayar Dam, Upper Kodayar Dam, Lower Kodayar Dam, Servalar Dam, Tamiraparani Dam, Manalar Dam, Ervangalar Dam, Periyar Forebay Dam, Upper Aliyar Dam, Kadamparai Dam, Pillur Dam, Upper Bhavani Dam, Emerald Dam, Avalanche Dam, Porthimund Dam, Pykara Dam, Mukurthy Dam in Tamilnadu state under seven different projects sponsored by respective state government departments. 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.

Seismological studies at Koyna:

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.

A network map of the borehole and broadband seismic stations in the Koyna-Warna region. In the inset, India map indicates the study 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).

The comprehensive set of 50 earthquake focal mechanism solutions in the Koyna-Warna region compiled from previous studies (grey) and from moment tenor inversion of waveform data by the authors in their previous work and the present work (black). (Rao and Shashidhar 2016).

Tectonics of the Koyna region as inferred jointly from the 1967 (M 6.3) and the 2012 (Mw 4.8) earthquakes are plotted with the topography of the study region as indicated in figure1. Focal mechanism solution for the 1967 earthquake is by Chandra (1976)-b while the solution for the 2012 earthquake is from the present study. Circles represent the aftershocks including the largest aftershock of Mw 3.7 located very close to the Mw 4.8 earthquake. KRFZ is the Koyna River Fault Zone and D is the Donachiwada fault. (Shashidhar et al. 2013).

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:

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.
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.
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).
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.

Currently, our team is operating a seismic network consisting of 27 three-component broadband seismographs in the Ladakh Himalayan region (filled blue triangles in Fig. 1a). Further, we are going to install a 10-broadband station array in 2024 (filled green triangles in Fig. 1a). The violet filled squares mark the locations of geothermal spring sites (viz., Panamic, Chumtang, and Puga) and Tso Moriri lake.

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:

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.
Detection of crustal and lithospheric thinning beneath the Kachchh rift zone indicates the presence of the 65 Ma Deccan Mantle Plume.
Identified the varying thicknesses of the Earth's crust and lithosphere in the Dharwar Craton and Deccan Volcanic Province
Our research shows that vertical tectonics dominated crustal uplift and subduction in the Eastern Indian Craton during the Archean epoch.
Our research simulated three NNE-SSW trending lithospheric transverse structures to reduce rupture lengths and seismic risk in the Uttarakhand Himalaya.
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.
Our research on upper mantle anisotropy and mantle transition zones in India has helped us better comprehend the geodynamic evolution of the Indian subcontinent.
Our study of crustal anisotropy in Uttarakhand Himalaya and Kachchh (Gujarat) has greatly improved our understanding of crustal processes.
Evaluated the site response, attenuation, and seismic source characteristics in the Uttarakhand Himalaya region.
Identified impact-related deformation at 800 m depth in the Lonar crater in Maharashtra using shallow velocity imaging.
Construction of 3-D models for seismic velocity and attenuation in the crust of the Koyna region, Maharashtra.
Conducted research on background noise spectra, site response, and crustal velocity models in Hyderabad, located in the Eastern Dharwar Craton.

Furthermore, we are now upkeeping a broadband array consisting of 24 stations in the Himachal Pradesh (red filled inverted triangles in Fig. 1b).

Environmental Seismology

The mountainous region usually witness Natural disasters such as landslides, GLOFs, and flash floods, triggered by factors like river erosion, extreme weathering, heavy rainfall, permafrost thawing, human activities and earthquakes. Extreme weather variations heighten slope destabilization risk in high-altitude areas. Glacial retreat and permafrost melting expand glacial lakes, destabilizing slopes and increasing landslide and flood risks downstream.

The seismic signals can effectively be utilized to understand environmental processes beyond earthquakes. It investigates anthropogenic noise from human activities that affect the accuracy of seismic monitoring. Research related to E.S explores how natural processes like weather patterns, hydrological cycles and land use changes influence seismic signals. For instance, heavy rainfall increases soil saturation and alters seismic wave propagation. Melting glaciers induce seismic activity and isostatic rebound. Environmental seismology monitors landslides, volcanic eruptions, and glacial dynamics, detecting precursory signals for early warning and hazard mitigation. Technological advancements enable precise seismic data collection and analysis, fostering interdisciplinary collaboration of experts in geophysics, hydrology, meteorology, and ecology. This multidisciplinary approach aids in understanding complex Earth processes, natural hazard assessment, resource management, and environmental conservation efforts. Environmental seismology remains crucial for safeguarding communities and ecosystems in a changing world.

Realtime Monitoring system near Joshimath Uttarakhand, India

The environmental seismology group of CSIR-NGRI has expertise in understanding the seismic signals generated by Natural disasters, like landslides, glacial lake outburst floods (GLOFs) and flash floods. In addition to the analysis of data from pre-existing networks in the vicinity of the Himalayas, the group is operating two dedicated landslide monitoring networks in Uttarakhand and Arunachal Pradesh to study the precursory subsurface changes associated with the landslide processes. The AI/ML approaches have been used for waveform discrimination and automatic detection of earthquake and landslide signals.

Major Inferences from the Study.

Seismic detection of the 7 February 2021 Dhauli Ganga disaster Chamoli District, Uttarakhand, India towards developing an Early Warning System

Large flow events can be detected across a seismic network, and can be tracked as they move downstream.
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.
Study suggests that early warning based on regional seismic networks is very much possible. [Rao et al., 2021, Science; Cook et al., 2021, Science]

Status of seismic monitoring of Geo Hazards @ CSIR-NGRI

We initiated the development of seismic signal based Landslide Early Warning (LEW) system for Uttarakhand.
Designed the machine learning based Automatic Seismic signal classifier in the first phase of LEW system.
Detected the seismic signals of 15-07-2011 flood and landslides from seismic recordings in CSIR-NGRI Arunachal Pradesh Network.
The stations at nearly 100 km able to record the landslide signals even in the high background noise.