Session SM7.2

[Programme]

SM7.2 | Seismic hazard assessment and disaster risk reduction through AI/ML based earthquake early warning

Seismic hazard assessment and disaster risk reduction through AI/ML based earthquake early warning

Co-organized by NH4

Convener: Prof. M.L. Sharma | Co-conveners: Rohtash KumarECSECS, Dr. Ranjit Das, Mr. Lalit Arya

Orals

| Fri, 28 Apr, 14:00–15:10 (CEST)

Room -2.47/48

Posters on site

| Attendance Fri, 28 Apr, 10:45–12:30 (CEST)

Hall X2

Posters virtual

| Attendance Fri, 28 Apr, 10:45–12:30 (CEST)

vHall GMPV/G/GD/SM

Damaging earthquakes create massive devastation in two ways: the loss of property and the loss of human life, out of which loss of human life can be easily reduced by interventions from earthquake early warning systems. Therefore, along with hazard, risk, and mitigation planning, earthquake prevention, prediction, early warning, and probability monitoring are crucial. Early warning systems have been deployed and are in operation in many nations including China, Taiwan, Japan, the USA, and Chile etc. Most of the systems work on classical regression equations and due to their inability to produce reliable data, old methodologies are no longer employed in the contemporary hazard assessment, earthquake early waring and monitoring of earthquakes. AI/ML techniques have made in roads for better understanding the nonlinear behavior and are capable of relatively more realistic predictions of the attributes, more so when data paucity has gone. The most recent method for earthquake prediction, probability evaluation and earthquake early warning is machine learning. Machine learning (ML) techniques have been extensively used in recent years to monitor earthquakes and analyze seismic data, including seismic detection, seismic classification, seismic denoising, phase picking, phase association, earthquake location, magnitude estimation, ground motion prediction, earthquake early warning, source inversion, and subsurface imaging. Due to its impressive accuracy and efficiency, ML-based phase picking has received a lot of interest and has been widely used for earthquake monitoring at local and regional scales, although global and regional ML phase pickers have received less attention.
This session appreciates contributions on the recent advancement in seismic hazard and risk assessment and earthquake early warning methods. We also, welcome the contribution on the application of ML in earthquake source dynamics, wave attenuation characterization and Seismic tomography.

Orals: Fri, 28 Apr | Room -2.47/48

Chairpersons: Prof. M.L. Sharma, Rohtash Kumar

14:00–14:10

EGU23-6433

SM7.2

Highlight

On-site presentation

Earthquake Early Warning with 3 seconds of records on a single station

Pablo Lara, Quentin Bletery, Jean-Paul Ampuero, and Inza Adolfo

We introduce the Ensemble Earthquake Early Warning System (E3WS), a set of Machine Learning algorithms designed to detect, locate and estimate the magnitude of an earthquake using 3 seconds (or more) of P waves recorded by a single station. The system is made of 6 Ensemble Machine Learning algorithms trained on attributes computed from ground acceleration time series in the temporal, spectral and cepstral domains. The training set comprises datasets from Peru, Chile, Japan, and the STEAD global dataset. E3WS consists of three sequential stages: detection, P-phase picking and source characterization. The latter involves magnitude, epicentral distance, depth and back-azimuth estimation. E3WS achieves an overall success rate in the discrimination between earthquakes and noise of 99.9%. For P-phase picking, the Mean Absolute Error (MAE) is 0.14s. For source characterization, the MAEs for magnitude, distance, depth and back-azimuth are 0.34 magnitude units, 27 km, 15.7 km and 45.2°, respectively. By updating estimates every second, the approach gives time-dependent magnitude estimates that follow the earthquake source time function. E3WS gives faster estimates than present alert systems, providing additional valuable seconds for potential protective actions.

How to cite: Lara, P., Bletery, Q., Ampuero, J.-P., and Adolfo, I.: Earthquake Early Warning with 3 seconds of records on a single station, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6433, https://doi.org/10.5194/egusphere-egu23-6433, 2023.

14:10–14:20

EGU23-15028

SM7.2

Highlight

On-site presentation

Temporal variation of Qc and its implications in medium characterization

Subhash Chandra Gupta, Mukat Lal Sharma, Sanjay Kumar Jain, and Arup Sen

The heterogeneities in the medium play important role not only in earthquake genesis but also strong ground motion simulation due to its bearing on attenuation characteristics. Observational data is one of the prerequisites for such studies which are acquired through deployment of seismological networks in active seismic regions. The Himalaya is considered as one of the most seismically active region in the world. It is also source of many river valley projects, like Tehri dam and others. The Tehri dam with 260.5 meter height is highest dam in India, located in the Lesser Himalaya of the Garhwal Himalaya that lies in seismic zone IV as per the seismic zoning map of India. Besides this, a number of development activity such as road and railway infrastructures are in progress. Thus, there is need to understand the effect of physical state of media on propagation of seismic waves in the Himalayas. The medium/path characteristics of this region has been measured by determining the seismic wave attenuation of high frequency waves of local earthquakes which is accomplished by estimating the quality factor of coda waves (Qc). In the present study, Qc, has been determined using local earthquakes recorded during last fourteen years from 2008 to 2021. The local earthquakes used in the study have been obtained through deployment of 12 to 18 stations local seismological network around Tehri dam reservoir. The results showed in the study that there is no significant change in Qc is observed in the region during this period after dam impoundment. These results found in agreement with the findings that no reservoir trigger seismicity is observed in the region associated with Tehri dam reservoir during last sixteen years.

How to cite: Gupta, S. C., Sharma, M. L., Jain, S. K., and Sen, A.: Temporal variation of Qc and its implications in medium characterization, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15028, https://doi.org/10.5194/egusphere-egu23-15028, 2023.

14:20–14:30

EGU23-15719

SM7.2

On-site presentation

Development of an hybrid GMPE-less ShakeMap implementation for real-time ground shaking maps reconstruction

Simone Francesco Fornasari, Veronica Pazzi, and Giovanni Costa

Ground shaking maps are an essential tool for seismic monitoring and civil defence operations as they provide information about the area and amplitude of the ground motion relative to a seismic event.

Such maps are developed integrating spatially sparse data recorded by the stations, which also provide a constraint to the process, and theoretical values obtained from ground motion prediction equations (GMPEs), given the magnitude and location of the earthquake, also accounting for local site effects.

One of the problems arising during the development of a real-time implementation of these techniques is the lack of information in real-time needed to compute the GMPE.

One possible solution to the problem is to develop algorithms that can constrain the interpolation process using only the ground motion parameters recorded at the stations (Fornasari et al., 2022).

We propose a hybrid model combining the conditioned multivariate normal distribution (MVN; Worden et al., 2018) technique adopted by ShakeMap and a neural network replacing the GMPE.

The neural network provides a purely data-driven approximation of the GMPE results based only on the spatially sparse data from the stations, with possible correction for the site effects.

The network is trained using a supervised approach with labelled data obtained from GMPEs used for the Italian territory. Moreover, by limiting the use of a neural network to a specific task we improve its explainability with respect to end-to-end models.

This approach is easily integrable into the existing workflow, combines the well-studied interpolation techniques and neural networks in a clearly explainable structure, and provides high-resolution estimates of the ground-shaking fields in real-time with potential relevance in the context of early warning.

References:

Simone Francesco Fornasari, Veronica Pazzi, Giovanni Costa; A Machine‐Learning Approach for the Reconstruction of Ground‐Shaking Fields in Real Time. Bulletin of the Seismological Society of America 2022; 112 (5): 2642–2652. doi: https://doi.org/10.1785/0120220034.

C. Bruce Worden, Eric M. Thompson, Jack W. Baker, Brendon A. Bradley, Nicolas Luco, David J. Wald; Spatial and Spectral Interpolation of Ground‐Motion Intensity Measure Observations. Bulletin of the Seismological Society of America 2018; 108 (2): 866–875. doi: https://doi.org/10.1785/0120170201

How to cite: Fornasari, S. F., Pazzi, V., and Costa, G.: Development of an hybrid GMPE-less ShakeMap implementation for real-time ground shaking maps reconstruction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15719, https://doi.org/10.5194/egusphere-egu23-15719, 2023.

14:30–14:40

EGU23-1428

SM7.2

ECS

Virtual presentation

Source-Characterisation of two moderate-sized earthquakes in Nepal employing detailed Moment Tensor Analysis

Pooja Mahto and Subhash Chandra Gupta

Two moderate-sized earthquakes occurred recently in Nepal on 8th November 2022 (Mw=6.1) and on 12th November 2022 (Mw=6.0). The location of these earthquakes falls at 384km and 329km respectively NW of the 25th April 2015 Gorkha earthquake. Both these earthquakes have been studied to understand the source rupture process employing Moment Tensor analysis by estimating the focal mechanism, and source parameters and relocating their hypocentral parameters using their regional waveforms recorded by the 18-stations broadband seismograph network deployed around Tehri dam in the Garhwal Himalaya Uttarakhand.

The epicentral distance of all the stations was less than 10◦. We employ the Moment Tensor Inversion approach to invert the broadband waveforms for the mechanism and depths and assume a one-dimensional velocity model developed for the adjacent Himalayan Region. Moment tensor solutions of the events were calculated along with the simultaneous calculation of the centroid position. Joint analysis of the hypocenter position, centroid position, and nodal planes produced clear outlines of Himalayan Fault lines. The epicenter of these earthquakes is located south of the MCT.

The obtained source mechanisms are consistent with those reported in the USGS centroid moment tensor (CMT) and NEIC solutions. We evaluated the performance of waveform inversion with just two broadband stations, and the result seems extremely reliable.

Inversion results indicate that the focal mechanism of the 8th Nov 2022 earthquake is a thrust fault type, and the obtained strike (283◦), dip (43◦), and rake (83◦) from the Present study are in accordance with CMT results of the USGS (strike (286◦), dip (58◦), and rake (97◦)) and NEIC (strike (285◦), dip (37◦), and rake (82◦)). The seismic moment is 1.583e+19 and the obtained DC% is 93% hence, the mechanism is considered to be Double-Couple. The identification of the source parameters is significant to the investigation of seismic hazards in this region.

How to cite: Mahto, P. and Gupta, S. C.: Source-Characterisation of two moderate-sized earthquakes in Nepal employing detailed Moment Tensor Analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1428, https://doi.org/10.5194/egusphere-egu23-1428, 2023.

14:40–14:50

EGU23-8376

SM7.2

ECS

On-site presentation

Understanding dynamics of ground movement based on seismic monitoring

Deepak rawat and Mukat lal sharma

Given the unique geology, topography, and hydrology of the Himalayas, it is imperative that a long-term strategy be developed to reduce the destructive potential of landslides in the region. Monitoring and an early warning system for landslides are the best non-structural measures for preventing landslide disasters. The study's overarching objective is to highlight why it's crucial to keep an eye out for landslides and how seismic sensor arrays can be used to issue early warnings. The implications of large mass flows in the study area must be carefully considered for sustainable hydropower and other socio-economic development projects. Seismic data, satellite imagery data, Time-Frequency analysis (TFA), and videos and photos taken by eyewitnesses form the basis of our investigation.

First, we gather precise event data, and then we collect signals from the seismology observatory at the Indian Institute of Technology Roorkee for that time frame. The signals from the collection have been processed with signal processing methods like STA/LTA, Filtering, and TFA. One synthetic signal, two landslide events, and two local earthquakes were analyzed to better comprehend the dynamics and behavior of a natural distractive event.

Seismic records reveal that various types of events have distinctive dynamic properties. There are three distinct stages to a landslide event: (1) the detachment of slope-forming materials, (2) the debris flow, and (3) the flood flow. The P and S waves, the onset and end times, and the duration of an earthquake have all been determined. We have used synthetic signals to learn about TFA and have found the method that works best for interpreting seismic signals. We use the classification method developed by Provost et al. in 2017. Time-domain amplitude levels are a feature that can be easily extracted and classified, but they are also vulnerable to noise. Energy concentration in the time-frequency domain is one such method that, while requiring more complex operations, can lead to more trustworthy feature extraction and accurate classification. The absence of the distinct P- and S-wave arrival time, as is typical of earthquakes, is another feature of the seismic waveform that is indicative of a landslide. The results of the seismic record analysis also shed light on the breadth of monitoring for slope-moving disaster events in the North-West Himalayas.

How to cite: rawat, D. and sharma, M. L.: Understanding dynamics of ground movement based on seismic monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8376, https://doi.org/10.5194/egusphere-egu23-8376, 2023.

14:50–15:00

EGU23-11549

SM7.2

ECS

Virtual presentation

Ground Motion Model For Data Sparse Regions: Machine Learning Approach

Vemula Sreenath, Jahnabi Basu, and Raghukanth stg

Ground motion models (GMMs) to the recorded ground motion time histories are essential input to the hazard analysis. With recent vast array of strong motion instruments to seismically active regions such as Japan, California, and Mexico, large amounts resulted in abundant recorded data huge metadata. Several global and regional GMMs are developed with these strong motion datasets. However, many active regions (e.g., The Himalayas) are in dearth of recorded strong motion data and metadata to develop predictive models. Despite recent instrumentations by different networks to the Himalayan region, the problem of near-field strong-motion records resulting from sparse instrumentation is the key concern. Traditionally, stochastic models are used in developing GMMs, as developing empirical models with limited data is challenging. Additionally, GMMs developed to other data-rich regions with similar tectonics are used in the hazard estimations. Thus, developing predictive models to these data-poor regions is a key concern which needs to be addressed. In the current work, we address this problem from the data-driven approach such as neural network. Neural networks learn the functional form from the data during training making it suitable for our present problem. Magnitude, epicentre distance, hypocentre depth, and shear wave velocity flag are used as inputs to estimate both the horizontal and vertical response spectra. In this regard, we attempt several approaches in developing the GMM using shallow neural network. Initially we develop model with seven neurons in the hidden layer using the available regional Western Himalayan crustal data and as one expects the model scaled poorly at the near-field. The obtained mean squared error (MSE) mean absolute error (MAE), and coefficient of determination (R2) are 0.6858, 0.6504, and 0.7592, respectively. To address this lack of near-field data, we supplement our regional data with records from global near-field strong motion and in developing GMM. This model has seven neurons in the hidden layer and performed better than the previous model but still had scaling issues at the large magnitude near-field. Further, supplementing data from other regions would influence the predictions. The obtained MSE, MAE, and R2 of the combined database are 0.5690, 0.5830, and 0.8659, respectively. However, the MSE, MAE, and R2 of the Western Himalaya data are 0.8006, 0.7057, and 0.7216, respectively. Finally, we use transfer learning technique: we develop GMM to the global crustal data and global near-field data and use it as a base model to develop GMM with six neurons in the hidden layer using the Western Himalayan data. The obtained MSE, MAE, and R2 of the Western Himalayan database are 0.8688, 0.7282, and 0.6970, respectively. Despite large error compared to previous two models, this model could capture large magnitude near-field effects and distance scaling effects and performed better than the previous two models. We conclude that transfer learning could be used to regions with limited strong motion data in developing GMM.

How to cite: Sreenath, V., Basu, J., and stg, R.: Ground Motion Model For Data Sparse Regions: Machine Learning Approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11549, https://doi.org/10.5194/egusphere-egu23-11549, 2023.

15:00–15:10

EGU23-9036

SM7.2

ECS

On-site presentation

Simulation of ground motions with high frequency components obtained from Fourier neural operators

Tariq Anwar Aquib and Paul Martin Mai

Seismic hazards analysis relies on accurate knowledge of ground motions arising from potential earthquakes to assess the risk of damage to buildings and infrastructure. It is necessary to perform ground motion simulations because recorded strong-motion data from specific combinations of earthquake magnitudes, epicentral distances, and site conditions are still limited. Physics-based simulations provide reliable ground motion estimates, but their application in practice is limited to frequency ranges f < 1Hz, largely due to limited computational resources and lack of information regarding earthquake sources and medium. While hybrid ground-motion computations combining deterministic low frequency components with stochastic high frequency components are often used, their stochastic high frequency components fail to correctly account for source and path effects and lead to inconsistent building responses.

The large database of ground motion records from Japan lends itself to develop machine learning approaches to estimate high frequency ground motions. Applying state-of-the-art machine learning techniques, like Fourier neural operators (FNOs) and Generative Adversarial Networks (GANs), we estimate seismograms at higher frequencies from their low frequency counterparts. In our approach, the time and frequency properties of ground motions are estimated using two different FNO models. In the time domain, a relationship is established between normalised low pass filtered and broadband waveforms. Frequency domain analysis involves the learning of high frequency spectrum from low frequency spectrum. Finally, the time and frequency properties are combined to produce broadband ground motions. Source, site, and path aspects are naturally incorporated into the training process.

We use ground motion data collected between 1996 and 2020 at 18 stations in the Ibaraki province of Japan to train our models and validate them on different events (Mw 4 to 7) around Japan. Using goodness of fit measures (GOFs), we show that the resulting ground motions match the recorded observations with good to acceptable GOF values for most of the predictions. To enhance our predictions, we include uncertainty estimation by utilizing a conditioned GAN approach. Lastly, to demonstrate the practicality of the approach, we compute high frequency components for a physics based simulated hypothetical Mw 5.0 earthquake in Japan.

How to cite: Aquib, T. A. and Mai, P. M.: Simulation of ground motions with high frequency components obtained from Fourier neural operators, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9036, https://doi.org/10.5194/egusphere-egu23-9036, 2023.

Posters on site: Fri, 28 Apr, 10:45–12:30 | Hall X2

X2.49

EGU23-19

SM7.2

ECS

Pattern identification of strong earthquakes in North American- Puerto Rico region through Correlation fractal dimension and Coulomb stress

Haritha Chandriyan, Ramakrushna Reddy, Tejaswini Mangalagiri, and Paresh Nath Singha Roy

This study attempts to investigate the patterns of correlation fractal dimension (Dc) prior to the occurrence of strong earthquakes by implementing modified Grassberger and Procaccia (1983) algorithm. The primary input for current research is earthquake epicentre locations. Through this method, dispersed and clustered seismicity can be distinguished by analysing spatiotemporal distribution of earthquake clusters. The low Dc values suggest dense clusters while high Dc values imply a scattered distribution of occurrences. In other words, low Dc represents a highly stressed region. Therefore, by monitoring the variations in Dc, we get valuable insights regarding spatiotemporal clustering of events as well as state of stress. To confirm the high stress brought on by dense clusters prior to the mainshock, we make use of the coulomb failure criterion to measure the Coulomb stress. For testing this hypothesis we have done analysis in southern California (SC), Baja California (BaC), and Puerto Rico Island (PRI).

Major plate movement between the North American plate and the Pacific plate is accommodated by the San Andreas Fault (SAF) and the remaining is by Eastern California Shear Zone (ECSZ). However, the ECSZ has experienced three strong earthquakes in the last thirty years. This indicates an anomalous pattern of seismicity developing in ECSZ. The recent rupture of 2019 Ridgecrest earthquake has caused stress perturbation along Garlock Fault (dormant fault, capable of producing M >7 earthquakes) throws light on the probable future event. We did fractal analysis on 30 years (1990-2020) of data considering 50 earthquakes per each window. Four strong earthquakes have chosen for studying; 2019 Ridgecrest (M_w7.1), 2010 El-Mayor Cucapah (M_w7.2), 1999 Hectormine (M_w7.1), and 1992 Landers (M_w7.3).In general, a relative decrease in Dc before each of the events is observed.

The commencement of 2019 Puerto Rico sequence trailed by the incidence of Mw6.4, 07 January 2020, earthquake highlights the importance of studying seismicity patterns in the PRI. Tectonic setting of the PRI is highly complex; characterized by dynamic seismicity. We have analysed ~32 years of seismicity data (M≥ 2.8). The fractal study of the Puerto Rico earthquake suggests a relative decline in Dc during 2019. It should be noted that the emergence of spatially closed clusters occurred at the same time in the southwestern PRI. When the static stress is calculated, the southwestern clusters indicate a highly stressed crust. This validates the relationship between the stress and low Dc observed prior to the occurrence of Mw6.4 January 2020 event.

Based on our study, it is possible to conclude that a significant drop in the Dc proceeds the mainshock. This pattern is explicit in the five major earthquakes in the study area. So we propose that our approach based on the patterns of correlation fractal dimension is a novel method to identify numerical precursors of strong earthquakes before the rupture.

How to cite: Chandriyan, H., Reddy, R., Mangalagiri, T., and Roy, P. N. S.: Pattern identification of strong earthquakes in North American- Puerto Rico region through Correlation fractal dimension and Coulomb stress, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-19, https://doi.org/10.5194/egusphere-egu23-19, 2023.

X2.50

EGU23-1957

SM7.2

Building Vulnerability Assessment using Artificial Intelligence forLandslide Susceptibility Zone in Champawat District, India

Yaggesh Sharma, Arun Tyagi, Mukat Lal Sharma, Priyanka Sharma, and Ashish Aggarwal

The evaluation of the vulnerability of society aroused to landslide-related tragedy is an enlarged
topic. Few studies talk about this issue and limited research has been carried out on the
relationship between landslides and their potential impact on buildings and infrastructure.
Uttarakhand Province in India is a highly landslide-prone area in the Himalayan region. The
present study focused on assessing the building vulnerability for the landslide susceptibility zone
in the Champawat district of Uttarakhand state. The building footprint areas are identified by
using an image segmentation algorithm in artificial intelligence. Moreover, the landslide-prone
zone was identified based on the historical and recent information collected from various
authenticated sources and the field investigations made on the recent landslides whereas, more
than ten landslide causative parameters/landslide conditioning factors (LCF) have been used to
generate a susceptibility map. The frequency ratio method has been applied to carry out the
susceptibility zone in the entire study area. Most buildings are found in dangerous areas that are
highly correlated by using published and in-situ datasets.
Keywords: Landslide Susceptibility Zone, Artificial Intelligence, Vulnerability, Building Footprints

How to cite: Sharma, Y., Tyagi, A., Sharma, M. L., Sharma, P., and Aggarwal, A.: Building Vulnerability Assessment using Artificial Intelligence forLandslide Susceptibility Zone in Champawat District, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1957, https://doi.org/10.5194/egusphere-egu23-1957, 2023.

X2.51

EGU23-2581

SM7.2

ECS

Highlight

Combining convolutional and recurrent neural networks for ShakeMap prediction

(withdrawn)

Yi-Shan Chen, Ljegay Rupeljengan, and I-Ting Su

X2.52

EGU23-4165

SM7.2

ECS

Exploring the physical state of Colombian lithosphere using body wave attenuation using ML

(withdrawn)

Rohtash Kumar, Utkarsh Pratap Singh, Amritansh Rai, Ankit Singh, Satya Prakash Maurya, and Raghav Singh

X2.53

EGU23-4709

SM7.2

A study on input ground motion processing platform for evaluating seismic fragilities using Deep Learning Phase Determination Model

Jin Koo Lee and JeongBeom Seo

The 9.12 Gyeongju earthquake(Sep. 12, 2016, Ml 5.8) and the Pohang earthquake(Nov. 15, 2017, Ml 5.4) have occurred in the Korean Penisula, resulting in emphasizing the stability of nuclear power plants. For safety evaluation, it is necessary to study the earthquake vulnerability caused by input ground motion. The input ground motion can be obtained from the earthquakes, and it is essential to acquire good quality and many samples input ground motion database for accurate evaluation. In this study, we tried to develop a platform that can automatically generate a ground motion database from past or real-time waveforms. To determine the detailed time window for data processing, deep learning-based earthquake detection, and phase-picking models were used. A voting method was conducted on these models to increase reliability in various environments. The platform produces a RotD50 5% damped pseudo-spectral acceleration, peak ground acceleration, and meta information related to site, hypocenter, and path. It also provides a web service to confirm generated data and meta information. The database generated by the platform could be used as input ground motion data to evaluate the safety of operating power plants and could be applied as fundamental data for the seismic design of planned nuclear power plants.

How to cite: Lee, J. K. and Seo, J.: A study on input ground motion processing platform for evaluating seismic fragilities using Deep Learning Phase Determination Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4709, https://doi.org/10.5194/egusphere-egu23-4709, 2023.

X2.54

EGU23-5710

SM7.2

ECS

Highlight

Rapid source characterization of the Maule earthquake using Prompt Elasto-Gravity Signals

Gabriela Arias, Quentin Bletery, Andrea Licciardi, Kevin Juhel, Jean-Paul Ampuero, and Bertrand Rouet-Leduc

The recently identified Prompt Elasto-Gravity Signals (PEGS), generated by large earthquakes, propagate at the speed of light and are sensitive to the earthquake magnitude and focal mechanism. These characteristics make PEGS potentially more accurate than P-wave based early warning algorithms (which produce saturated magnitude estimations) and faster than Global Navigation Satellite Systems (GNSS)-based systems. We use a deep learning model called PEGSNet, originally developed for application in Japan, to track the temporal evolution of the magnitude of the 2010 M_w 8.8 Maule earthquake. The model is a Convolutional Neural Network (CNN), trained on a database of synthetic PEGS -- simulated for an exhaustive set of possible earthquakes distributed along the Chilean subduction zone -- augmented with empirical noise. The approach is multi-station and leverages the information recorded on all the available stations to estimate as fast as possible the magnitude and location of an on-going earthquake. Our results indicate that PEGSNet could have estimated an M_w > 8.7 earthquake after 100 seconds in the Maule case. Our synthetic tests using real data and noise recordings further support the instantaneous tracking of the source time function of the earthquake.

How to cite: Arias, G., Bletery, Q., Licciardi, A., Juhel, K., Ampuero, J.-P., and Rouet-Leduc, B.: Rapid source characterization of the Maule earthquake using Prompt Elasto-Gravity Signals, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5710, https://doi.org/10.5194/egusphere-egu23-5710, 2023.

X2.55

EGU23-10532

SM7.2

Comparison of Characteristics of MEMS and Geophone Sensors for Earthquake Detection

Jeong Beom Seo

Posters virtual: Fri, 28 Apr, 10:45–12:30 | vHall GMPV/G/GD/SM

vGGGS.6

EGU23-3041

SM7.2

ECS

Application of Fuzzy Algorithm for Assessing Seismically Induced Landslide Hazard for an Important Road Corridor in the Lower Indian Himalayas

Arun Tyagi, Ritu Raj Nath, and Saurabh Chaurasia

A critical part of planning and managing road infrastructures in mountainous regions is the
pragmatic assessment of the prevailing and credible landslides hazard. Such assessments assume
greater significance for the Himalayan region, where seismically induced landslides present a
greater threat than commonly recognized, and require a robust comprehension of two hazards:
earthquake and the landslides induced by the former. However, the traditional practice of
landslide hazard assessment often neglects seismic factor due to paucity of pertinent data, which
may further be ascribed to the rarity of an extreme event. In this context, an endeavor has been
made in this study to evaluate the seismically induced landslide hazard for a scenario earthquake
of 10% exceedance probability in 50 years for an important road corridor in the lower Indian
Himalayas using Fuzzy algorithms. Probabilistic Seismic Hazard Assessment (PSHA) has been
carried out for the study area to calculate the Peak Ground Acceleration (PGA) of the scenario
earthquake, which is then used as a landslide triggering factor. PGA is integrated with eight
different landslide controlling factors viz. lithology, slope angle, aspect, elevation profile,
distance form fault, distance from drainage, distance from road, and land-use-land-cover patterns
in a Geographical Information System (GIS). 232 numbers of landslides are mapped for the
study area using high resolution Google earth imagery platform. The Fuzzy Cosine Amplitude
method is used to define the degree of similarity (strength of correlation) between the observed
landslides (dependent variable) and the landslide causative factors (independent variable(s)).
Expectedly, the probability of landslide occurrence correlates (degree of similarity) to the PGA
in a linear pattern (goodness of fit = 0.9954). The result of the study is discussed in terms of a
seismically induced Landslide Hazard Zonation (LHZ) map for the study, which is generated
using three Fuzzy operators (AND, OR and GAMMA). The prepared LHZ map demarcates more
than 40% of the study area as the zones of high to very high landslide hazard under the scenario
earthquake, with a prediction accuracy of 80%. The study shows that probabilistically generated
PGA can be included as seismic parameter for a more comprehensive assessment of the landslide
hazard in seismically active regions.

Keywords: Fuzzy Cosine Amplitude, Probabilistic Seismic Hazard Assessment (PSHA), Peak
Ground Acceleration (PGA), Landslide Hazard Zonation (LHZ), the Himalayas

How to cite: Tyagi, A., Nath, R. R., and Chaurasia, S.: Application of Fuzzy Algorithm for Assessing Seismically Induced Landslide Hazard for an Important Road Corridor in the Lower Indian Himalayas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3041, https://doi.org/10.5194/egusphere-egu23-3041, 2023.

vGGGS.7

EGU23-3077

SM7.2

ECS

Probabilistic Seismic Hazard Assessment of Assam, North-East India with the Incorporation of Topography Based VS30 Values

Mayuri Borah, Ramanand Dubey, and Josodhir Das

In this study, Probabilistic seismic hazard assessment (PSHA) is performed for Assam, North-East (NE) India. NE India being bounded by latitude 20⁰-30⁰ N and longitude 87⁰-98⁰E, is considered as one of the most earthquake-prone areas in the world. As per seismic zoning map of India (IS 1893 (2016), Part 1), most of the states in NE region have been placed in seismic zone V, which has the highest zone factor in the country. Among the eight north-eastern states, Assam serves as the gateway to the other seven states. As this region lies on one of the most vigorous tectonic plates in the world, it has experienced several devastating earthquakes in the past. Considering the seismicity of this region, seismic hazard assessment plays a significant role to assess the seismic risk for the future. The NE India region is broadly divided into four seismogenic sources, and further sub-divided into nine seismogenic sources based on the tectonic features and seismicity characteristics. For the study of hazard assessment, a unified moment magnitude catalogue has been used, where the events are assembled from various databases (ISC, IMD, USGS-NEIC). The catalogue has been declustered and the seismicity parameters are calculated for each source zone. The hazard maps have been presented at the bedrock level, in terms of peak ground acceleration (PGA) and spectral acceleration (S_a) values. The PGA values vary in between 0.16-0.57 g, while the S_a values are obtained in the range of 0.12-0.77 g. Further, topography based V_S30 values have been considered for all the source zones and hazard maps are prepared with the incorporation of the site-specific V_S30 values. These hazard maps are expected to give insight to the local site-specific seismic hazard variation for the Assam region and would be useful for the preparedness of risk and disaster mitigation measures.

Keywords: PSHA, NE India, Assam, PGA, Hazard Map

How to cite: Borah, M., Dubey, R., and Das, J.: Probabilistic Seismic Hazard Assessment of Assam, North-East India with the Incorporation of Topography Based VS30 Values, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3077, https://doi.org/10.5194/egusphere-egu23-3077, 2023.

vGGGS.8

EGU23-4108

SM7.2

ECS

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Comparison of Deterministic and Probabilistic framework for Vs30 estimation in data scarce region: a case study for Southern Bihar, India.

Mudit Srivastava and Mukat Lal Sharma

Time-averaged shear-wave velocity in the topmost 30 meters of soil (V_s30) is a broadly accepted tool employed for site characterization. Adoption of V_s30 in the development of region-specific ground motion prediction equations and seismic design provisions marked it as a global parameter for local-site effect studies. Challenges arise in tectonically complex regions where an evaluation of V_s30 requires great exertion including mobility of equipment and manpower. Over the period, where researchers are still engaged in studying the effects and limitations of V_s30 at a location of interest, during the years various proxies have arrived for V_s30estimation. Also, the selection of proxy depends upon the existing prior information about the region and its relationship with measured V_s30 values. Data scarce region requires interpolation techniques to address extensive geographical area with limited attainable datasets. Various deterministic (Inverse distance weighing, spline, etc.) and probabilistic (kriging formats) interpolation techniques are widely used for robust estimation. In this study, an attempt has been made for a reliable region-specific selection of interpolation techniques. 35 V_s30 measurements are used as primary data and the topographic-slope proxy-based V_s30 model by U.S. Geological Survey is used as secondary data. Quantitative assessment acknowledges the existence, and validity which provides an understanding of the merits and flaws of interpolation techniques. The applicability of IDW, kriging and Bayesian scheme for sturdy estimation of Vs30 with focus on Southern Bihar region is examined for seismic response studies providing paramount importance to hazard and risk mitigation.

Keywords: V_s30,Topographic-slope Proxy, IDW, Kriging, Bayesian Scheme.

How to cite: Srivastava, M. and Sharma, M. L.: Comparison of Deterministic and Probabilistic framework for Vs30 estimation in data scarce region: a case study for Southern Bihar, India., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4108, https://doi.org/10.5194/egusphere-egu23-4108, 2023.

vGGGS.9

EGU23-4810

SM7.2

ECS

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Lithospheric imaging beneath North India using surface wave tomography

Deepak Kumar, Suresh Gaddale, Satish Maurya, and Subhash Chandra Gupta

We provide a shear wave velocity model of the Indo-Gangetic plain (IGP) that extends down to a depth of 100 km. Using vertical component seismograms of 108 broadband (BB) stations (50 and 58) of IRIS-DMC and National Centre for Seismological (NCS) respectively, located in and around the Northern Indian plate. The group velocity dispersion of the Rayleigh wave has been picked along ~3000 paths across the study region over a period range of 8 to 80s. To construct the 3D shear wave velocity structure, we employ a two-step surface wave tomography procedure. In the first step, regionalized dispersion maps are prepared for each period of correlation length of 60km, and subsequently, we employ the Markov chain Monte Carlo (McMC) trans-dimensional Bayesian inversion algorithm to obtain the shear wave velocity structure. In regionalized dispersion maps, at short periods (~8s) we see slow velocity in northern IGP and region reported thick basement (~6km) from previous studies. For moving towards increasing periods map indicate slow velocity anomalies in the Himalayan and Tibetan plateau region are associated with a thick crust (>50) in contrast to the typical crust (~40km) of IGP. The fast and slow velocities areas are identified which are associated with the Indian shield and thick crust in the Himalayas. Further, we inverted regionalized geographical locations to get shear wave velocity at each point to make a 3D lithospheric model. We have used 20 chains with 600k burn-in phase and 300k in the main phase for sampling the posterior distribution and from the final posterior distribution best 500k models with of 5% deviation has been selected after removing those model that has outlier chains with unrealistic models.

Keywords: Surface waves, Bayesian inversion, Seismic tomography, Northwestern Himalayas.

How to cite: Kumar, D., Gaddale, S., Maurya, S., and Gupta, S. C.: Lithospheric imaging beneath North India using surface wave tomography, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4810, https://doi.org/10.5194/egusphere-egu23-4810, 2023.

vGGGS.10

EGU23-10932

SM7.2

ECS

Scenario Seismic Risk assessment of 1934 Bihar-Nepal Earthquake

CLal lawmawma and Mukat Lal Sharma

This paper presents seismic risk assessment of Bihar considering a hypothetical earthquake event similar to 1934 Bihar-Nepal earthquake. Assessment of risk has been carried out for all districts of Bihar and risk is presented in terms of economic loss. The loss estimation is performed through the combination of seismic hazard, structural vulnerability, and exposure data. Regarding the seismic input, a non-linear probabilistic approach is used to estimate the hypocenters of 1934 Bihar-Nepal earthquake and other source parameters are taken from literature. Abrahamson and Silva (2014) ground motion prediction equation is used to generate the strong ground motion at the surface level. Building exposure data are based on national census survey of India 2011. The census data provides common building typologies for each district and their relative distribution. On the basis of wall material used for construction all the buildings are grouped into four class, and seismic vulnerability functions (Martin and Silva,2011) are assigned to each building class. For each districts, total number of buildings are aggregated at the location of the maximum ground motion. The area per building class has been assumed and reconstruction costs per square metre for each districts have been assigned based on local expert input and values identified in the literature. Finally, the district level distribution of economic loss for this earthquake scenario is obtained using the OpenQuake-engine. From this study, the expected economic loss is highest in Madhubani district followed by Muzzafarpur, Dharbanga and Sitamarhi district. Un-reinforced masonry buildings type construction, most prevalent in the rural region would experience maximum loss. A repeat of 1934 Bihar-Nepal earthquake in present day would have devastating consequences, although this scenario addresses a hypothetical event, the seismic risk assessment constitute important tools for framing public policies toward land-use planning, building regulations, insurance, emergency preparedness and could eventually minimizes economic disruption caused by earthquake.

How to cite: lawmawma, C. and Sharma, M. L.: Scenario Seismic Risk assessment of 1934 Bihar-Nepal Earthquake, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10932, https://doi.org/10.5194/egusphere-egu23-10932, 2023.

vGGGS.11

EGU23-11563

SM7.2

ECS

Deterministic Seismic Hazard Assessment by revisiting 1991 Uttarkashi and 1999 Chamoli Earthquake for Uttarakhand, India

Yaggesh Sharma, Mudit Srivastava, Priyanka Sharma, and Deepak Kumar

Deterministic Seismic Hazard Assessment is a quantitative site-specific evaluation of ground response for a particular region. Destructive effects caused due to occurrence of natural seismic hazards can be minimized by accounting mindful mitigation measures. One of the fundamental phases in risk assessment is the précised determination of the risk over a certain period. Considering the earthquake rupture model and a couple of defined region-specific ground motion models as input, earthquake scenarios to determine peak ground acceleration (PGA) and spectral periods (SA) are examined. In the present study, the Topographic slope as a proxy for shear wave velocity in upper soil of 30-meter (V_s30) estimation is assessed for rapid prediction and first-order studies. Further, two distinct major earthquake scenarios, the 1991 Uttarkashi and 1999 Chamoli earthquakes are revisited to estimate the distribution of PGA and SA at 0.2 sec and 1 sec for the area of interest. Thus, obtained results for Uttarakhand are presented in terms of V_s30, PGA, 0.2 sec, and 1-sec spectral values respectively.

Keywords: Deterministic seismic hazard, V_s30,1991 Uttarkashi, 1999 Chamoli, scenario earthquakes, PGA, SA.

How to cite: Sharma, Y., Srivastava, M., Sharma, P., and Kumar, D.: Deterministic Seismic Hazard Assessment by revisiting 1991 Uttarkashi and 1999 Chamoli Earthquake for Uttarakhand, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11563, https://doi.org/10.5194/egusphere-egu23-11563, 2023.

vGGGS.12

EGU23-16903

SM7.2

Evaluating Seismic Hazard based on Simulated Earthquake Strong Ground Motions in Himalaya

Dinesh Kumar, Anjali Sharma, and Renu Yadav

The importance of site-specific earthquake strong ground motions for the proper evaluation of seismic hazard of a region has now been well recognized. It is useful to generate site-specific ground motions required for the designing of earthquake resistant buildings in seismic active regions as required number of observed records are not available at all the sites of interest. The simulated time histories enhance the sparse data base of observed accelerograms which is useful for improving seismological understanding of an earthquake process. Most of the earthquakes in Indian region occur in Himalaya which is the result of collision of northward drifting Indian plate with Eurasian plate. The seismic hazard is severe in the region of Himalaya. The first step to mitigate the seismic hazard is to evaluate the same.

In the present study, the seismic hazard has been estimated in the regions of Himalaya using simulated strong ground motions from earthquakes. A modified hybrid technique has been used for the simulation of earthquake strong ground motions. In this technique a composite source model (Zeng et al, 1994) has been combined with semi-empirical envelope technique (Midorikawa, 1993). In the technique, the envelope function of target earthquake is computed by summation of envelope functions that are generated from small size earthquakes distributed randomly on the fault plane. In order to simulate the ground motions at surface level, the high frequency decay parameter and site amplification functions have been taken into account.

The strong ground motions have been simulated at large number of points distributed spatially in the region. The scenario hazard maps in the form of spatial distribution of peak ground acceleration values have been presented due to a great earthquake (M 8.5) in Central Seismic Gap of Himalaya and a major earthquake (M 6.9) in NE Himalaya. The scenario hazard maps prepared in the present study may be useful to the local administrators for the mitigation of the earthquake hazard in the region. These maps give the idea about the possible scenario in case of similar size future earthquake occurs in the region. The maps presented here are useful to mitigate the seismic hazard from the region.

How to cite: Kumar, D., Sharma, A., and Yadav, R.: Evaluating Seismic Hazard based on Simulated Earthquake Strong Ground Motions in Himalaya, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16903, https://doi.org/10.5194/egusphere-egu23-16903, 2023.