A system for modeling ground movements for deep subduction zone earthquakes in Northeast India and adjacent regions

Abstract

System for modeling ground movements in deep ground plates, earthquakes in subduction zones in northeastern India and adjacent regions, consisting of: A database of strong ground motions configured to store historical earthquake data from deep intralabial sediments. Earthquakes in subduction zones, i.e., strong motion records of earthquakes within the subduction plate with focal depths between 50 km and 200 km, including earthquake data from Northeast India and adjacent regions; a central processing unit configured to extract region-specific seismic parameters from the strong earthquake records, the region-specific seismic parameters including focal depth, location and path parameters, quality factor, stress parameters, path duration and geometric propagation factor; a stochastic point source model module integrated into the central processing unit and configured to generate ground motion simulations for earthquake magnitudes from Mw 5.0 to Mw 8.0 and epicentral distances from 50 km to 300 km, wherein the stochastic point source model module is further configured to generate 36,500 ground motion simulations using the region-specific seismic parameters; and a regression analysis module connected to the stochastic point source module and configured to calculate regression coefficients from the ground motion simulations, wherein the regression analysis module is further configured to generate a ground motion model for intraplate earthquakes in Northeast India, wherein the ground motion model, upon execution or implementation, is configured to estimate the peak ground acceleration and spectral acceleration values, and wherein the ground motion model was developed for hard rock conditions corresponding to a VS30 of 2,800 m / s.

Description

AREA OF INVENTION

[0001] The present disclosure relates to a system for modeling ground movements in deep subduction zones in northeastern India and adjacent regions. BACKGROUND OF THE INVENTION

[0002] Northeast India (NEI) and adjacent regions have been struck by twenty strong earthquakes (Mw > 7.0) since 1400 AD. The average recurrence interval of strong earthquakes (Mw ≥ 8.0) in this region is estimated at 25 to 30 years, indicating high seismic activity. The Indian standard IS 1893:2016 assigns NEI to earthquake zone V, which has the highest zone factor (0.36) for calculating the horizontal seismic design coefficients. However, the spectral design acceleration values ​​given in IS 1893:2016 are based solely on historical seismic data and do not take into account factors such as the characterization of active faults or current earthquake hazard data.

[0003] Previous studies have attempted to estimate earthquake hazard for Northeast India using global model models (GMMs) that combined crustal and subduction zone earthquakes or were based on fewer recorded events for intraplate earthquakes. Other researchers developed GMMs for crustal earthquakes or validated stochastic models for mixed earthquake types, but did not explicitly address deep intraplate earthquakes.

[0004] Since both crustal and subduction zone earthquakes occur in Northeast India, and important input parameters for ground motion models (GMMs) such as stress parameters, magnitude scaling, focal depth, and attenuation differ, separate GMMs are required. Previous work has shown that ground motion parameters are relevant for intraplate earthquakes. Earthquakes in subduction zones are significantly more frequent than earthquakes at plate boundaries. Therefore, there is a need for a new and precise GMM specifically for earthquakes within the subduction zone in Northeast India.

[0005] The present invention therefore provides a GMM for intraplate earthquakes in NEI, which was developed using a stochastic point source model to enable an improved assessment of regional seismic hazard. SUMMARY OF THE INVENTION

[0006] The present disclosure relates to a system for modeling ground movements in deep subduction zones in Northeast India and adjacent regions. The present invention provides, in particular, a computational system specifically designed for deep earthquakes within the subduction zone of Northeast India (NEI) and adjacent regions. Due to the limited availability of strong earthquake measurement data in the NEI region, the system uses a stochastic point-source modeling approach in combination with region-specific seismic parameters to simulate 36,500 ground movements in various magnitude and distance scenarios.The system extracts critical seismic parameters such as quality factor, stress parameter, travel time, and geometric propagation factor from historical earthquake records and then applies these parameters in stochastic simulations to generate a robust predictive model for ground motion. The resulting model estimates the values ​​of maximum ground acceleration (PGA) and spectral acceleration (Sa) for earthquakes from Mw 5.0 to 8.0 at epicentral distances of 50 to 300 km, thus providing important data for seismic hazard analysis and earthquake-resistant planning in the region.

[0007] The present disclosure aims to provide a system for modeling ground motions in deep subduction zones. This system includes: a database of strong ground motions that stores historical earthquake data from deep subduction zones.

[0008] Earthquakes in subduction zones, i.e., strong ground motion records of intraplate earthquakes with focal depths of 50 km to 200 km, these records comprising earthquake data from Northeast India and adjacent regions; a central processing unit configured to extract region-specific seismic parameters from the strong ground motion records, the region-specific seismic parameters comprising focal depth, location and propagation parameters, quality factor, stress parameters, propagation duration, and geometric propagation factor;a stochastic point source model module integrated into the central processing unit and configured to generate ground motion simulations for earthquake magnitudes from Mw 5.0 to Mw 8.0 and epicentral distances from 50 km to 300 km, wherein the stochastic point source model module is further configured to generate 36,500 ground motion simulations using the region-specific seismic parameters;and a regression analysis module connected to the stochastic point source module and configured to calculate regression coefficients from the ground motion simulations, the regression analysis module further configured to generate a ground motion model for intraplate earthquakes in Northeast India, the ground motion model being configured during execution or implementation to estimate the peak ground acceleration and spectral acceleration values, and the ground motion model being designed for hard rock conditions corresponding to a VS30 of 2,800 m / s.

[0009] The purpose of this disclosure is to provide a system for modeling ground movements in deep subduction zones, specifically earthquakes in subduction zones in northeastern India and adjacent regions.

[0010] Another objective of this disclosure is the development of a region-specific ground motion model for deep ground movements within tectonic plates. Earthquakes in subduction zones in northeastern India and adjacent regions, for which there is little empirical data on strong ground movements, are investigated using stochastic simulation techniques based on region-specific seismic parameters extracted from available historical earthquake records.

[0011] Another objective of the present disclosure is to generate comprehensive ground motion predictions over wide magnitude and distance ranges by simulating 36,500 synthetic ground motions for earthquake magnitudes from Mw 5.0 to Mw 8.0 and epicentral distances from 50 km to 300 km, thereby providing reliable peak ground acceleration values ​​and spectral acceleration values ​​for seismic hazard analysis.

[0012] Another objective of the present disclosure is the establishment of an adaptive and scalable prediction system that includes bootstrap resampling for uncertainty quantification, sensitivity analyses for parameter validation, and modules for scaling site coefficients to convert predictions of hard rock conditions to different site classes and thus ensure applicability across different geological conditions.

[0013] A further objective of this disclosure is to provide a validated and objective tool for estimating ground motions through systematic comparison with recorded strong earthquake data, residual value analysis with respect to magnitude and distance, and verification using global intraplate measurements. This will ensure the reliability of earthquake engineering applications in the NEI region.

[0014] To further clarify the advantages and features of the present disclosure, the invention is described in more detail with reference to specific embodiments illustrated in the accompanying drawing. It is understood that this drawing merely shows typical embodiments of the invention and is therefore not to be understood as limiting its scope of protection. The invention is described and explained in more detail and with reference to the accompanying drawing. BRIEF DESCRIPTION OF THE IMAGE

[0015] These and other features, aspects and advantages of the present disclosure will be better understood when the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts, wherein: Fig. Figure 1 illustrates a block diagram of a system for modeling ground movements in deep subduction zones. Earthquakes in subduction zones in northeastern India and adjacent regions according to an embodiment of the present disclosure.

[0016] Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. DETAILED DESCRIPTION:

[0017] To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention.

[0018] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation thereof.

[0019] References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0020] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0022] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0023] The functional units described in this specification are referred to as devices. A device may be implemented in programmable hardware such as processors, digital signal processors, central processing units, FPGAs, PALs, PLDs, cloud processing systems, or similar. Devices may also be implemented in software for execution by various processor types. An identified device may contain executable code and, for example, comprise one or more physical or logical blocks of computer instructions, which may be organized as an object, procedure, function, or other construct. However, the executable files of an identified device need not be physically related; they may consist of different instructions stored in different locations that, when logically combined, constitute the device and fulfill its purpose.

[0024] The executable code of a device or module can consist of a single instruction or multiple instructions and can even extend across different code sections, applications, and storage media. Similarly, operational data within the device can be identified and represented, and can exist in any suitable form and be organized in any data structure. The operational data can be captured as a single data record or distributed across various storage media and may exist, at least partially, as electronic signals within a system or network.

[0025] References to “a selected embodiment”, “an embodiment”, or “an embodiment” in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Therefore, the phrases “a selected embodiment”, “in an embodiment”, or “in an embodiment” appearing at different points in this description do not necessarily refer to the same embodiment.

[0026] Furthermore, the described features, structures, or properties can be combined in one or more embodiments in any suitable manner. The following description contains numerous specific details to enable a comprehensive understanding of the embodiments of the disclosed subject matter. However, a person skilled in the art will recognize that the disclosed subject matter can also be realized without one or more of the specific details or with other methods, components, materials, etc. In other cases, known structures, materials, or processes are not presented or described in detail so as not to obscure aspects of the disclosed subject matter.

[0027] According to the exemplary embodiments, the disclosed computer programs or modules can be executed in a variety of ways, for example, as an application running in the memory of a device or as a hosted application running on a server and communicating with the device application or browser via various standard protocols such as TCP / IP, HTTP, XML, SOAP, REST, JSON, and other suitable protocols. The disclosed computer programs can be written in programming languages ​​that run either in the device's memory or on a hosted server, such as BASIC, COBOL, C, C++, Java, Pascal, or scripting languages ​​such as JavaScript, Python, Ruby, PHP, Perl, or other suitable programming languages.

[0028] Some of the described embodiments involve data transmission over a network, such as the transmission of various inputs or files. The network may include, for example, the internet, wide area networks (WANs), local area networks (LANs), analog or digital wired and wireless telephone networks (e.g., PSTN, ISDN, cellular networks, and xDSL), radio, television, cable, satellite, and / or other transmission or tunneling mechanisms for data. It may include multiple networks or subnetworks, each of which may, for example, have a wired or wireless data path. The network may include a circuit-switched voice network, a packet-switched data network, or another network for transmitting electronic data. For example, it may be based on the Internet Protocol (IP) or Asynchronous Transfer Mode (ATM) and support voice communication using VoIP, Voice over ATM, or similar protocols.In one embodiment, the network comprises a mobile network configured for the exchange of text or SMS messages.

[0029] Examples of networks include Personal Area Networks (PAN), Storage Area Networks (SAN), Home Area Networks (HAN), Campus Area Networks (CAN), Local Area Networks (LAN), Wide Area Networks (WAN), Metropolitan Area Networks (MAN), Virtual Private Networks (VPN), Enterprise Private Networks (EPN), the Internet, Global Area Networks (GAN), and so on.

[0030] Fig. Figure 1 illustrates a block diagram of a system for modeling ground movements in deep subduction zones. Earthquakes in subduction zones in northeastern India and adjacent regions according to an embodiment of the present disclosure.

[0031] According to Fig.1. The system (100) comprises: a powerful ground motion database (102) configured to store historical earthquake data from deep intra-plate earthquakes in subduction zones, i.e., powerful ground motion records of intra-plate earthquakes with focal depths of 50 km to 200 km, these records including earthquake data from Northeast India and adjacent regions; a central processing unit (104) configured to extract region-specific seismic parameters from the powerful ground motion records, the region-specific seismic parameters including focal depth, location and propagation parameters, quality factor, stress parameters, propagation duration, and geometric propagation factor; a stochastic point source model module (106) integrated into the central processing unit (104) and configured to perform ground motion simulations for earthquake magnitudes from Mw 5.0 to Mw 8.0 and epicentral distances from 50 km to 300 km, wherein the stochastic point source model module (106) is further configured to generate 36,500 ground motion simulations using the region-specific seismic parameters; and a regression analysis module (108) connected to the stochastic point source module (106) and configured to calculate regression coefficients from the ground motion simulations, wherein the regression analysis module (108) is further configured to generate a ground motion model for intraplate earthquakes in Northeast India, wherein the ground motion model is configured during execution or implementation to estimate the peak ground acceleration and the spectral acceleration values, and wherein the ground motion model was developed for hard rock conditions corresponding to a VS30 of 2,800 m / s.

[0032] In one embodiment, the quality factor is between 150 and 210, the stress parameter is between 120 and 300 bar, the kappa value is 0.006 seconds for hard rock level, and the geometric spreading factor is defined as 1 / R for a hypocentral distance R less than 100 km and 1 / (10√R) for a hypocentral distance R greater than or equal to 100 km.

[0033] In one embodiment, the stochastic point source model module (106) is further configured to generate 100 sets of ground motions for each scenario pair of magnitude and distance and to produce ground motion simulations from 365 scenario pairs of magnitude and distance intervals.

[0034] In one embodiment, the central processing unit (104) is further configured to calculate the travel time for the distance to the epicenter, the travel time being based on 5%-95% energy criteria from the acceleration and velocity database of earthquakes within the subduction plate.

[0035] In one embodiment, the regression analysis module (108) is configured to: implement a two-stage regression technique for calculating the regression coefficients; and generate the ground motion model using a function form that includes a magnitude scaling function, a depth scaling function, and a site enhancement term.

[0036] In one embodiment, the system (100) further comprises: a sensitivity analysis module (110) configured to evaluate uncertainties related to region-specific seismic parameters such as focal depth, cutoff frequency, stress parameters, radiation pattern, inelastic damping, geometric damping, and duration; wherein the standard deviations are in the range of 0.81 to 0.88 in natural logarithmic units, indicating that the generated ground motion model is not distorted.

[0037] In one embodiment, the system (100) further comprises: a bootstrap module (112) configured to determine the uncertainty of the damping parameters, wherein the bootstrap module (112) is configured to randomly select 90% of the earthquakes and 90% of the earthquake records, wherein the bootstrap module (112) is configured to generate 150 parameter sets to represent the observed variability, and wherein the bootstrap module (112) is configured to compute anelastic model parameters.

[0038] In one embodiment, the system (100) further comprises: a site coefficient scaling module (114) which is connected to the sensitivity analysis module (110) and serves to convert ground movement values ​​from hard rock conditions to different site classes.

[0039] In one embodiment, the system (100) further comprises: a validation module (116) configured to compare the ground motion model with recorded strong earthquake data from earthquakes within the subduction plate in northeast India, wherein the validation module (116) is configured to calculate residuals between the ground motion model and recorded events, and wherein the validation module (116) is configured to evaluate residuals in terms of magnitude and hypocentral distance.

[0040] In one embodiment, the ground motion model is further configured to: estimate peak ground acceleration values ​​for deep earthquakes within the subduction plate; estimate spectral acceleration values ​​at 5% attenuation for deep earthquakes within the subduction plate; and provide ground motion values ​​using site coefficient scaling factors.

[0041] The present invention provides a system for developing ground motion models specifically tailored to deep ground plates. Earthquakes in subduction zones in northeastern India and adjacent areas such as Bangladesh, Bhutan, China, Myanmar, and Nepal are investigated. Given the limited data on strong ground motions in this seismically active region, the system employs a sophisticated stochastic point source model that generates synthetic ground motions based on region-specific seismic parameters. The system comprises several integrated modules: a database of strong ground motions with historical earthquake data and focal depths of 50 to 200 km; a central processing unit that extracts key region-specific parameters such as quality factors (Q0: 150-210), stress parameters (120-300 bar), travel-time models, and geometric propagation factors; and a stochastic simulation module that...The system generates 500 ground motion scenarios for 365 magnitude-distance pairs and includes a regression analysis module that derives prediction equations using two-stage regression techniques. Validation mechanisms include bootstrap resampling for uncertainty quantification, sensitivity analyses with standard deviations of 0.81–0.88 (indicating unbiased predictions), and the ability to scale site coefficients to convert predictions from bedrock (VS30 = 2800 m / s) to different site classes. The system generates reliable estimates of maximum ground acceleration and spectral acceleration for earthquakes of magnitude Mw 5.0 to 8.0 at distances of 50–300 km, thus filling a critical gap in seismic hazard analysis tools for this vulnerable region.

[0042] Modeling Ground Movements in Deep Plates: The system for investigating earthquakes in subduction zones in northeastern India and adjacent regions was developed to overcome the limitations of available strong earthquake measurement data through a stochastic point source modeling approach. It operates by integrating a strong earthquake database, a central processing and analysis unit, and computational modules for stochastic simulation and parameter estimation.

[0043] In the first step, the system creates a database of strong ground motions, specifically containing records of earthquakes within the subduction zone of the Northeast India plate and adjacent regions. The database comprises 113 records of strong ground motions originating from 16 earthquakes within the subduction zone. The earthquake data were compiled from various national and international data repositories, including the National Earthquake Information Center (NEIC), the COSMOS Virtual Data Center, the International Seismological Center (ISC), the Indian Meteorological Department (IMD), the European Mediterranean Seismological Center (EMSC), and the Incorporated Research Institutions for Seismology (IRIS).

[0044] The system's central processing unit performs analytical calculations to extract and define region-specific seismic parameters for the simulation. These parameters include focal depth, location and propagation parameters, quality factor, stress parameters, propagation duration, and geometric propagation factor. Due to the limited size of the available dataset, the system uses synthetic ground motion simulations based on the stochastic point source model for magnitude ranges from Mw 4.0 to 8.5 and is suitable for simulating high-magnitude earthquakes within the subplate tectonic zone in the northeastern Southern Hemisphere.

[0045] The stochastic point source module within the system generates ground motions according to the fundamental representation of the Fourier amplitude spectrum of ground acceleration, expressed as: A(f)=C×S(f)×D(f)×P(f)×F(f) where A(f) denotes the Fourier amplitude spectrum of the ground acceleration; C is a scaling factor; S(f) represents the source spectral function; D(f) defines the damping function that characterizes the damping; P(f) is the high-frequency filter; and F(f) denotes the site gain function, which is equal to one for hard rock conditions.

[0046] The source spectral function S(f) is based on Brune's single-corner frequency model (1970, 1971) and is defined as a function of the corner frequency (fc) and the seismic moment (Mo). The corner frequency fc depends on the stress parameter (Δσ) and the shear wave velocity (VS) of the region. The attenuation factor D(f) takes into account both geometric and anelastic attenuation. The geometric propagation factor is represented as 1 / Rγ, where R is the source distance in kilometers and y is the geometric propagation exponent.

[0047] The system defines the regional quality factor Q(f) as a function of frequency using the relationship Q(f) = Q0f^η, where Q0 is the quality factor at 1.0 Hz and η represents the frequency dependence due to regional geological and tectonic conditions. Both the geometric attenuation model and the quality factor are frequency-dependent and correlated, thus ensuring a precise modeling of seismic wave propagation in Northeast India.

[0048] To limit high-frequency amplitudes, the system uses a high-frequency cutoff filter, expressed by P(f) = exp (-πκf). Here, κ represents the kappa factor, which characterizes the attenuation effects near the surface. The scaling factor C is defined as a function of the radiation coefficient, the density (ρ) of the Earth's crust at the source depth, and the gain at the free surface. For shear waves, a radiation coefficient between 0.45 and 0.65 is assumed, and the density is 2.8 g / cm³. 3 .

[0049] For the simulations, the site enhancement function F(f) for hard rock soils (VS30 ≈ 2800 m / s) is assumed to be equal to one. Using these formulations, the system's stochastic point source module generates large samples of synthetic ground motion data representing various combinations of magnitude and distance parameters relevant for NEI.

[0050] Through these integrated calculation methods, the system efficiently generates representative ground motion datasets suitable for developing a reliable and region-specific ground motion model for deep subduction zones in northeastern India and adjacent regions.

[0051] Modeling Ground Movements in Deep Subduction Zones: The application for simulating earthquakes in subduction zones in Northeast India (NEI) and adjacent regions includes a module with input parameters for defining region-specific seismic parameters for stochastic ground movement simulations. The system integrates focal depth, location, propagation path, quality factor, stress parameters, and propagation duration as input variables for generating synthetic ground movements.

[0052] The system's focal depth determination module defines the range of focal depths for simulating earthquakes within the subduction plate. Based on collected records of strong earthquakes in Northeast India and adjacent regions, focal depths range from 49 km to 125 km. Taking regional seismic characteristics into account, the system sets the focal depth range for simulations to 50 km to 200 km. This range considers both typical and potential shallower earthquakes within the subduction plate in Northeast India.

[0053] In one embodiment, the site parameter module classifies measurement sites based on the time-averaged shear wave velocity (VS30) in the uppermost 30 m. Most measurement stations in Northeast India (NEI) belong to site class C. Since only limited strong earthquake records are available for all site classes, the system simulates ground motions under hard rock conditions with a fixed shear wave velocity (VS30) of 2800 m / s. A kappa value of 0.006 s is used to represent near-surface attenuation at the hard rock level. Scaling factors for site coefficients are integrated to convert ground motion values ​​from hard rock conditions to other site classes if necessary.

[0054] In one embodiment, the path parameter module defines the geometric propagation model for propagation within the layer. This is used for subduction earthquakes in northeastern India. For focal distances R below 100 km, the geometric propagation factor G is defined as 1 / R. For distances R of 100 km and above, G is defined as 1 / (10√R). These values ​​account for the regional propagation characteristics of the waves.

[0055] In one embodiment, the system's damping module defines the frequency-dependent quality factor Q(f) and the stress parameter Δσ for earthquakes within the subduction plate. The quality factor is represented as Q(f) = Q0 f^η, where Q0 ranges from 150 to 210 and η ranges from 0.91 to 0.99. The pressure range is between 120 and 300 bar. The module uses a bootstrap method to assess the uncertainty of the damping parameters. In this process, 90% of the earthquakes and 90% of the recordings are randomly selected and duplicated as needed. This results in 150 parameter sets that represent the observed variability. The determined parameters of the anelastic model include log 10 (Q0) = 2.32 ± 0.15 and γ = 1.01 ± 0.005.

[0056] In one embodiment, the module calculates the wave propagation time Tp for each scenario at runtime. The system estimates Tp based on the acceleration and velocity data of earthquakes within the subduction plate, using 5% to 95% of the total energy. The total duration Td is defined as the sum of the source duration Ts and the travel time Tp. The system uses a linear two-segment model for the travel time with a slope change at 60 km for distances below and above this threshold. The module ensures consistency between the input and output travel times by adjusting the input model by a factor of 1.05. The final travel time model is defined as follows: Tp=R×(17.8 / 60) for R<60 km Tp=17.8+0.05×(R−60) for R≥60 km

[0057] In one embodiment, the system's stochastic point source module generates ground motions for earthquakes within the Northeastern Plate (NEI) using defined seismic input parameters. The ground motions are calculated for magnitudes from Mw 5.0 to Mw 8.0 and epicentral distances from 50 km to 300 km at a S S30 A ground motion of 2,800 m / s is simulated. A total of 365 scenario pairs of magnitude and distance are defined, with 100 sets of ground motions simulated for each scenario. This results in 36,500 synthetic ground motion records for the development of the region-specific ground motion model.

[0058] Uniform distribution is applied to the random sample of all seismic input parameters within their defined ranges. This ensures comprehensive capture of regional variability and supports the precise generation of a ground motion model suitable for deep intraslabic earthquakes in subduction zones in northeastern India and adjacent regions.

[0059] In one embodiment, the present system comprises a ground motion modeling module for subduction zones in Northeast India (NEI) and adjacent regions. This study includes a ground motion modeling module for estimating the maximum horizontal ground acceleration (PGA) and horizontal spectral acceleration (Sa) at bedrock sites. These ground motion parameters are defined as functions of earthquake magnitude (Mw), focus distance (R), and region-specific seismic input parameters.

[0060] The system implements a functional form for the ground motion model, which is expressed as follows: ln (S a)=θ 1+θ 4ΔC 1+(θ 2+θ 14+θ 3(Mw −7.8))ln(R+C 4 exp(θ 9(M w−6)))+θ 6R+θ 10+fmag(M w)+fDepth(Z h)+fFABA(R)+f−Site(PGA 1000,V S30)+σ where Zh represents the focal depth in kilometers, θ1 to θ14 are regression coefficients, σ ​​denotes the standard deviation of the ground motion parameters, FFABA defines the site type with a value of 0 for NEI as an unknown site for pre-bow or post-bow regions, and V S30 = 2,800 m / s represents the site condition for solid rock.

[0061] The system defines the magnitude scaling function fmag (Mw) using a piecewise defined formulation based on period-dependent variations of ΔC1 and a reference magnitude C1 = 7.8, with different expressions for Mw ≤ C1 + ΔC1 and Mw > C1 + ΔC1.

[0062] The depth scaling function fdepth (Zh) is calculated as follows: fDepth(Zh)=θ11×(min(Zh,120)−60)×Fevent

[0063] The site enhancement function fsite (PGA1000, VS30) is calculated depending on whether V S30 below or above a reference velocity Vlin, using the following expressions: f site(PGA 1000,V S30)=θ 12 Ln(VsVlin)−bLn(PGA 1000+c)+bLn(PGA 1000+c(VsVlin)n))(forV S30 <V lin or θ 12Ln(VsVlin)+bnLn(VsVlin)(for V S30≥V lin)

[0064] The system calculates the regression coefficients θ1 to θ14 using a two-stage regression procedure to minimize errors in parameter estimation. Additionally, a sensitivity analysis module evaluates potential uncertainties of the various seismic input parameters to ensure the robustness of the ground motion model. Through these integrated computational processes, the system generates a ground motion model for bedrock during earthquakes within the Northeastern Plate (NEI). This model is capable of estimating Sa and PGA values ​​across a range of magnitudes, distances, and region-specific conditions.

[0065] In one embodiment, the system includes a sensitivity analysis module that evaluates the impact of uncertainties in the seismic input parameters on the generated ground motion model (GMM). The module propagates uncertainties regarding focal depth, cutoff frequency, stress parameters, radiation pattern, inelastic and geometric attenuation, and propagation time through stochastic simulations using random sampling. The standard deviation for each input parameter is calculated. The maximum observed standard deviations range from 0.81 to 0.88 (natural logarithmic units), indicating that the GMM is not distorted with respect to the region-specific seismic input parameters. Uncertainties in the site coefficients and the kappa coefficient are treated separately, with kappa set to 0.006 s for solid rock.

[0066] The system also includes a site coefficient scaling module configured to convert ground motion values ​​from bedrock conditions to different site classes. The site coefficient Fs is defined as follows: ln(Fs)=a1 Ybr+a2+ln(σs)

[0067] Where Ybr represents the acceleration of the bedrock, σs the standard deviations of site classes A to D, and a1 and a2 the respective regression coefficients. The module uses these coefficients to fit the bedrock GMM to the conditions of site class C and compare it with available records.

[0068] The system includes a validation module that compares the generated GMM with recorded strong earthquake measurement data from earthquakes within the Northeastern Plate (NEI) of soil class C with a VS30 of approximately 360–500 m / s. The module calculates the residuals for PGA and Sa at 0.05, 0.2, and 1.0 s as a function of magnitude and focal distance. The residual analyses show that the GMM agrees with the recorded data and exhibits no systematic deviation with respect to Mw, PGA, or Sa.

[0069] The ground motion model was developed for hard rock conditions with a VS30 of 2800 m / s and a density of 2.8 g / cm³. 3Developed. Site coefficients were applied to scale the model to site class C. Comparisons of the generated ground motion model with other models for ground motion within a base plate are performed. Subduction earthquakes exhibit higher PGA and Sa values, which is attributed to NEI-specific parameters such as higher stresses, lower crustal damping, and lower kappa values. The system allows for the estimation of ground motion over a wider focal depth range (50–200 km) than global models and provides region-specific results for intrasubduction earthquakes in the northeastern region.

[0070] The system also includes a model update module configured to integrate newly available strong earthquake records from NEI and adjacent regions, thus enabling a refinement of the GMM over time.

[0071] In summary, the system provides a horizontal GMM for deep earthquakes within the Northeastern Plate (NEI) for PGA and Sa at 5% attenuation. It is based on 36,500 stochastic simulations for magnitudes of Mw 5.0–8.0 and epicentral distances of 50–300 km. Regression coefficients for bedrock were calculated from these simulations, and sensitivity analyses confirm minimal deviation. Validation using available measurement data and comparison with global models demonstrate the model's reliability and its NEI-specific properties.

[0072] The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims.

[0073] The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A System for Modeling Ground Movements for Deep Subduction Zone Earthquakes in Northeast India and Adjacent Regions. 102 Strong Earthquake Database 104 Central unit 106 Module For Stochastic Point Source Models 108 Regression Analysis Module 110 Module on Sensitivity Analysis 112 Bootstrap module 114 Location coefficient scaling module 116 Validation module QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited non-patent literature

[0000] IS 1893:2016

[0002]

Claims

[1] System for modeling ground movements in deep ground plates Earthquakes in subduction zones in northeast India and adjacent regions, consisting of: A database of strong ground motions configured to store historical earthquake data from deep intralabial sediments. Earthquakes in subduction zones, i.e., strong motion records of earthquakes within the subduction plate with focal depths between 50 km and 200 km, including earthquake data from Northeast India and adjacent regions; a central processing unit configured to extract region-specific seismic parameters from the strong earthquake records, the region-specific seismic parameters including focal depth, location and path parameters, quality factor, stress parameters, path duration and geometric propagation factor; a stochastic point source model module integrated into the central processing unit and configured to generate ground motion simulations for earthquake magnitudes from Mw 5.0 to Mw 8.0 and epicentral distances from 50 km to 300 km, wherein the stochastic point source model module is further configured to generate 36,500 ground motion simulations using the region-specific seismic parameters; and a regression analysis module connected to the stochastic point source module and configured to calculate regression coefficients from the ground motion simulations, wherein the regression analysis module is further configured to generate a ground motion model for intraplate earthquakes in Northeast India, wherein the ground motion model, upon execution or implementation, is configured to estimate the peak ground acceleration and spectral acceleration values, and wherein the ground motion model was developed for hard rock conditions corresponding to a VS30 of 2,800 m / s. [2] System according to claim 1, wherein the quality factor is in the range of 150 to 210; the stress parameter is in the range of 120 to 300 bar; the kappa value for hard rock is 0.006 seconds; and the geometric spreading factor is defined as 1 / R for a hypocentral distance R less than 100 km and as 1 / (10√R) for a hypocentral distance R greater than or equal to 100 km. [3] System according to claim 1, wherein the stochastic point source model module is further configured to generate 100 sets of ground motions for each scenario pair of magnitude and distance and to create ground motion simulations from 365 scenario pairs of magnitude and distance intervals. [4] System according to claim 1, wherein the central processing unit is further configured to calculate the travel time for the distance to the epicenter, the travel time being based on 5%-95% energy criteria from an acceleration and velocity database of earthquakes within the subduction plate. [5] System according to claim 1, wherein the regression analysis module is configured to: implement a two-stage regression technique for calculating the regression coefficients; and generate the ground motion model using a function form that includes a magnitude scaling function, a depth scaling function and a site enhancement term. [6] System according to claim 1, further comprising: a sensitivity analysis module for evaluating uncertainties related to the region-specific seismic parameters including focal depth, cutoff frequency, stress parameters, radiation pattern, inelastic damping, geometric damping and time duration; wherein the standard deviations are in the range of 0.81 to 0.88 in natural logarithmic units, indicating that the generated ground motion model is not distorted. [7] System according to claim 1, further comprising: a bootstrap module for determining the uncertainty of the damping parameters, wherein the bootstrap module is configured to randomly select 90% of the earthquakes and 90% of the earthquake records, wherein the bootstrap module is configured to generate 150 parameter sets to represent the observed variability, and wherein the bootstrap module is configured to compute anelastic model parameters. [8] System according to claim 1, further comprising: a module connected to the sensitivity analysis module for scaling the site coefficient, which is configured to convert ground movement values ​​from hard rock conditions to different site classes. [9] System according to claim 1, further comprising: a validation module configured to compare the ground motion model with recorded strong earthquake data from earthquakes within the subduction plate in Northeast India, wherein the validation module is configured to calculate residuals between the ground motion model and recorded events and wherein the validation module is configured to evaluate residuals in terms of magnitude and hypocentral distance. [10] System according to claim 1, wherein the ground motion model is further configured to: estimate peak ground acceleration values ​​for deep earthquakes within the subduction plate; estimate spectral acceleration values ​​at 5% damping for deep earthquakes within the subduction plate; and provide ground motion values ​​using site coefficient scaling factors.

Images