Earthquake Source Parameters Research Articles

SEISMICITY of the BAIKAL REGION and TRANSBAIKALIA in 2018–2019

We considered a character of the seismic process in the Baikal region and Transbaikalia in 2018–2019. During this time period, 14545 earthquakes with КR≥5.6 were registered. More than 94 % of the seismic events were localized in the Baikal rift zone. Within it, relatively strong earthquakes occurred in Khubsugul-Tunka area (March 29, 2019, Mw=4.8) and in the junction zone of the northeastern flank of the Baikal rift and the Olekma-Stanovoy orogenic system (September 28, 2019, Mw=5.1). 49 earthquakes caused felt macroseismic effects which most often manifested themselves in the settlements of the region with intensity of 3–4 and 4. Intensity of 5–6 was observed in few cases. Earthquake source parameters were determined for 81 seismic events with КR≥9.1. Most of them occurred in the seismotectonic regime of subhorizontal extension. 61 focal mechanisms were determined from P-wave first-motion polarities. Source parameters for 13 earthquakes were estimated using a joint inversion of P-wave first-motion polarities and surface wave amplitude spectra. In general, 2018–2019 are characterized by a moderate level of seismic activity.

SPECTRAL and SOURCE PARAMETERS of NORTH CAUCASUS EARTHQUAKES in 2018–2019

The dynamic parameters of the sources of 44 earthquakes in the North Caucasus were determined from the amplitude spectra of S-waves for the period 2018–2019 with an energy class according to T.G. Rautian scale KR=9.1–14.1. Earthquake records from regional digital seismic stations of the North Caucasus network, with epicentral distances of no more than 300 km were used for the calculation. The source spectra were calculated based on the frequency-dependent quality factor Q(f) for earthquakes in the North Caucasus western zone – Q(f)=90f 1.02 – and eastern zone – Q(f)=97f 0.89 . The SEISAN program was used for the spectral analysis of 31 earthquakes. The following values were obtained: 0 – spectral density, f0 – corner frequency, M0 – scalar seismic moment, Mw – moment magnitude, L – length of the rupture at the source of an earthquake, or r – radius of the circular area,  – stress drop. Relationships between lgM0 and KR and Mw and KR are obtained.

EARTHQUAKE FOCAL PARAMETERS in the CRIMEAN-BLACK SEA REGION in 2018–2019

The results of calculation and analysis of spectral and dynamic parameters of the sources (М0,r0, , , ησ, r, ū, Eu и Mw) of 24 Crimean earthquakes for 2018–2019 in the range of energy classes KП=5.8–11 are presented. 157 station amplitude spectra of P- and S- seismic waves recorded at seven regional digital seismic stations were used for calculations. The determination of dynamic parameters of the sources was carried out using the Brune theoretical dislocation model. The average values of the dynamic parameters of earthquake sources for 2018–2019 were in most cases estimated for several stations and for different types of waves, which ensured a small value of the degree of scattering of individual estimates. For the studied 2018–2019 earthquakes, the average values of the seismic moment М0, the radius of the circular dislocation r0, and the stress drop  were within the confidence intervals of the long-term dependencies М0(KП), r0 (KП) and (KП). For five strongest earthquakes of 2018 (January 31, April 24, September 9, September 13, and October 15 with KП=10.5–11) and two earthquakes of 2019 (May 8 and December 4), focal mechanism solutions were obtained within the framework of the double dipole model. Type of movement in the source of January 31, 2018 earthquake is a strike-slip with a normal fault component, of September 9 and September 13, 2018 – a normal fault, of April 24 and October 15, 2018, as well as May 8 and December 4, 2019 – a reverse fault. The spectral composition of seismic oscillations was additionally studied from the energy spectra of 15 earthquakes using records at the Alushta station located at distances 5–285 km from source zones. At the qualitative level, a shift of the spectrum maximum (fqmax) towards higher frequencies for weak shocks and close epicentral distances.

Impact of mining-induced seismicity on land subsidence occurrence

Mining-induced seismicity can damage buildings and surface infrastructure and cause fatalities. It is a growing concern, owing to the sudden occurrence of seismic events, lack of effective prediction capabilities, and associated consequences. In this study, we analyzed 13 high-energy mining-induced earthquakes from 2016 to 2020 in Poland's Legnica-Glogow Copper District (LGCD), based on data recorded by the Induced Seismicity-European Plate Observing System (IS-EPOS). We used European Space Agency (ESA) Sentinel-1 Copernicus satellite radar images, to study the co-seismic subsidence in the region. Additionally, we employed volumetric point source approximation and a Bayesian inverse modeling scheme to estimate the location and strength of subsurface collapses from the land subsidence data. Finally, we correlated the geological constraints with the land subsidence and earthquake source parameters. The interferometric synthetic aperture radar (InSAR) displacement maps portrayed maximum line-of-sight (LOS) displacements of −144 mm and − 124 mm in ascending and descending images, respectively, with maximum land subsidence reaching −142 mm. Notably, only a few earthquakes with large magnitudes caused land subsidence. There was a significant correlation between earthquake magnitude and land subsidence; the greater the thickness of the loose Quaternary strata, the smaller the land subsidence. According to the inversion results, the focal depths of the collapses associated with the earthquakes were 437–669 m. However, the estimated collapse depths were situated above the mining exploitation fields, within the rigid Triassic rock layers. The associated collapse energy was 1.62 times greater than the seismic energy, indicating aseismic deformation in the field. Additionally, we observed spatial discrepancy between the location of the earthquake epicenters and the maximum land subsidence. Thus, our study can improve the current understanding of the impact of mining-induced seismicity, the occurrence of associated land subsidence, and seismic mechanisms in mining areas.

A numerical methodology for estimating site-specific cascading earthquake and tsunami dynamic loading on critical infrastructure

A numerical methodology for deriving cascading earthquake and tsunami loading patterns for use in critical infrastructure design is proposed. Earthquake source parameters are first used to generate synthetic ground motion acceleration time series at the site of a critical infrastructure using an open-source stochastic extended finite-source simulation algorithm. Using the same earthquake source information, the tsunami phases of generation, propagation, and inundation are modeled using a coupling approach for determining tsunami wave forces. The coupling approach combines (1) a code based on non-linear shallow water equations solved by the Finite Volume method, which is used to capture the tsunami flow characteristics from generation through the inundation phase until relatively close to the structure, coupled with (2) an open-source computational fluid simulation code, which relies on solving the Navier–Stokes equations using the Smoothed Particle Hydrodynamics method. An appendix presents the validation of the numerical methodology through correlation of the numerical simulations with field data recorded during the 2011 Japan Earthquake and Tsunami. The methodology is then employed to characterize the ground shaking and tsunami loading patterns at the site of the container terminal of the Sines deep-water seaport in Portugal, when subjected to the historic 1755 Great Lisbon Earthquake and Tsunami plausible scenarios. For the site, the generated ground motion acceleration time series show peak accelerations exceeding Eurocode 8 design values. Tsunami flow depths and velocities were computed in horizontal and vertical directions and could be used in the assessment of the existing infrastructure or the design of the future expansion of the port.

Real‐Time Fault Tracking and Ground Motion Prediction for Large Earthquakes With HR‐GNSS and Deep Learning

AbstractEarthquake early warning (EEW) systems aim to forecast the shaking intensity rapidly after an earthquake occurs and send warnings to affected areas before the onset of strong shaking. The system relies on rapid and accurate estimation of earthquake source parameters. However, it is known that source estimation for large ruptures in real‐time is challenging, and it often leads to magnitude underestimation. In a previous study, we showed that machine learning, HR‐GNSS, and realistic rupture synthetics can be used to reliably predict earthquake magnitude. This model, called Machine‐Learning Assessed Rapid Geodetic Earthquake model (M‐LARGE), can rapidly forecast large earthquake magnitudes with an accuracy of 99%. Here, we expand M‐LARGE to predict centroid location and fault size, enabling the construction of the fault rupture extent for forecasting shaking intensity using existing ground motion models. We test our model in the Chilean Subduction Zone with thousands of simulated and five real large earthquakes. The result achieves an average warning time of 40.5 s for shaking intensity MMI4+, surpassing the 34 s obtained by a similar GNSS EEW model. Our approach addresses a critical gap in existing EEW systems for large earthquakes by demonstrating real‐time fault tracking feasibility without saturation issues. This capability leads to timely and accurate ground motion forecasts and can support other methods, enhancing the overall effectiveness of EEW systems. Additionally, the ability to predict source parameters for real Chilean earthquakes implies that synthetic data, governed by our understanding of earthquake scaling, is consistent with the actual rupture processes.

A novel method for inverting coseismic 3D surface deformation using InSAR considering the weight influence of the spatial distribution of GNSS points

The combination of InSAR and GNSS data can provide more accurate measurements of coseismic 3D surface deformations, which are crucial in determining earthquake source parameters. Currently, the methods for inverting coseismic 3D surface deformations by integrating GNSS and InSAR data using the strain model (SM) (e.g., Simultaneous and integrated strain tensor estimation from geodetic measurements to obtain 3D displacement maps (SISTEM), SM-VCE) simply select GNSS points based on a certain number or distance, without considering the weight influence of the spatial distribution of G NSS points on the InSAR and GNSS data fusion results. To address this issue, this paper proposes a new method that uses Voronoi diagrams analyze the contribution of GNSS points to the deformation at target points. In this paper, GNSS points with high contribution can be used to construct observation value equations and optimize the internal weight of GNSS data. The proposed approach characterizes the contribution of GNSS data to target points by analyzing the changes in the Delaunay triangulation area formed before and after the target points are added to the GNSS data. Simulation experiments reveal that the proposed method outperforms conventional methods in accuracy and completeness. The proposed method is then applied to map the coseismic 3D surface deformations of the 2020 Mw6.5 Monte Cristo Range earthquake and find that the maximum deformation in the east–west, north–south, and vertical directions is about 13 cm, 11 cm, and 24 cm, respectively.

Novel prediction models of earthquake early warning for ground motion power in Taiwan

AbstractStudies have indicated that the power of seismic waves, rather than the acceleration and velocity, strongly correlates with the level of structural damage and the shutdown of high‐tech equipment during high‐precision process. However, current earthquake early warning (EEW) systems in Taiwan issue a warning only for a seismic wave with high acceleration or velocity but not for that with high power. To extend the application of the EEW system to high‐tech fields, conventional and novel hybrid prediction models were developed in this study to predict the ground motion power and to issue EEWs for high‐power ground motions. A conventional prediction model that relies on earthquake source parameters was first developed to determine ground motion characteristics, and a hybrid prediction model that relies not only on earthquake source parameters but also on the on‐site ground motion parameter of P‐waves was developed to improve the prediction accuracy. The two‐step maximum likelihood method was used to derive model coefficients for considering the mixed effect of the ground motion records. The source, path, and site effects on the short‐period power and the long‐period power of ground motions were illustrated by the proposed models. A method for determining a broadcast scenario of EEW systems for high‐power ground motions with a target success rate and damage threshold was also developed. These novel prediction models can be used to develop a customized EEW system to provide an early warning for high‐power seismic waves in Taiwan.

Source Parameters of Strong Turkish Earthquakes on February 6, 2023 (<i>M<sub>w</sub></i> = 7.8 and <i>M<sub>w</sub></i> = 7.7) from Surface Wave Data

Abstract—Based on the amplitude spectra of surface waves, the source parameters of the strong Turkish earthquakes of February 6, 2023 (Mw = 7.8 and Mw = 7.7) were calculated in two approximations: an instantaneous point source and an elliptical shear dislocation. As a result, rupture planes were identified, data were obtained on the scalar seismic moment, moment magnitude, focal mechanism, and source depth of the considered seismic events, and the integral parameters characterizing the rupture geometry and its development in time were estimated. It is shown that the sources of the earthquakes under study were formed under the influence of the regional stress field and their focal mechanisms were left lateral faults with a strike direction close to the strike of the East Anatolian fault zone for the first event and close to the strike of the Sürgü-Çardak fault system for the second. For the first earthquake, our estimates of the rupture duration and its length (t = 52.5 s, L = 180 km) probably refer not to the entire rupture, but only to its main phase, confined to the northeastern segments of the East Anatolian fault and characterized by maximum displacements and values of the released seismic moment. The values of t = 30 s and L = 180 km that we obtained for the second earthquake fully characterize the entire rupture.

Pemodelan Percepatan Tanah sebagai Parameter Hazard Gempabumi menggunakan Metode Deterministic Seismic Hazard Analysis di Kota Manado

Research has been carried out on modeling ground acceleration as an earthquake hazard parameter in Manado. This study aims to create an earthquake modeling scenario using Deterministic Seismic Hazard Analysis to determine the estimated PGA value and the intensity value of a significant earthquake. This study utilizes the BMKG SHAKEMAP software to determine the PGA value which is then converted into a modified Mercalli intensity value. A total of 13 modeling scenarios were built in this study. The results show that the scenario of significant earthquake modeling in Manado City and its surroundings using the 2018 BMKG catalog input shows varying results. This depends on the earthquake source parameters as well as the distance from the source to the site. Scenario model 10 is the most representative because it has the same intensity value as the June 20, 1991 earthquake. The resulting PGA value shows the area that has the highest PGA in Manado comes from the 3rd model (8.1% g) and the lowest from the model to 12 (1.4 % g). Due to the limited historical intensity data from the catalog, only 8 models out of 13 models were tested for root mean square error verification ranging values from 0 to 0.25.

Earthquake source parameters in Zagros region (Iran) from the time-evolutive P-wave displacement

The rupture process of the recent moderate-to-large earthquakes in the Zagros area along the Iran plateau is investigated by analysing the strong motion data provided by the Iranian Building and Housing Research Centre (BHRC). The selected dataset includes the largest and deadliest 2017 Mw 7.3, Iran-Iraq (Ezgeleh) earthquake. The earthquake source parameters (moment magnitude, rupture duration and length, average slip, and static stress drop) are determined using a time-domain, parametric modelling technique based on the time evolution of the P-wave displacement signals. The earthquake source parameters are calculated from simulated triangular moment-rate functions assuming the circular source models for a constant rupture velocity. The anelastic attenuation effect is modelled through the independent frequency-Q parameter ranging from 50 to 200 and accounted for by a post-processing procedure that retrieves the attenuation-corrected, moment-rate triangular shape. Results show that the average static stress-drop with different {Q}_{P}, varies between <Δσ> = 0.9 (0.7–1.2) MPa and <Δσ> = 1.6 (1.2–2.0) MPa. Overall, in this research, the rupture radius/length empirically scales with the seismic moment with a self-similar, near-constant stress drop of about 1 MPa. Assuming a circular rupture model for the Ezgeleh earthquake, we estimate a moment magnitude of 6.9, rupture duration of 7 s, source radius of 16 km, average slip of about 2 m and static stress drop of 3.4 MPa.

Computing Theoretical Seismograms from a Point Source in a Spherical Multilayered Medium

Abstract Computing theoretical seismograms from a point source in a given Earth model is essential for modeling and inversion of observed seismic waveforms for Earth’s structure and earthquake source parameters. Here, we derived the propagator matrices and source terms for a spherical multilayered Earth model using the exact earth flattening transformation. We found that their differences from their counterparts in horizontal layered media are inversely proportional to the nondimensional horizontal wavenumber and its higher order. In addition, all the source terms in a spherical layered model have a source-depth dependent scaling factor that differs from in a horizontal layered model by up to 6% for deep earthquakes. The surface displacement produced by a point source can be obtained in a similar form as in horizontal layered media. Computation of theoretical seismograms was implemented using the generalized reflection and transmission coefficients method. Numerical tests show that our formulae and implementation are correct and efficient for computing full-wave seismograms, including the permanent displacements, at teleseismic distances up to 100°. Individual seismic phases can be isolated and analyzed semianalytically because the generalized reflection and transmission method is used. Furthermore, our analytic expression of displacement in terms of the propagator matrices and source terms can be used to derive analytic derivatives of seismograms for full-wave waveform inversion.

Data-driven analysis of crustal and subduction seismic environments using interpretation of deep learning-based generalized ground motion models

Studies on understanding the regional seismological differences based on the variations in the characteristics of the ground motion waves recorded during seismic events have provided independent insights into the different seismic environments of the world. This study aims to showcase the differences between three of the major seismic environments of the world including Japanese subduction, Chilean subduction, and Californian crustal. The study is based on developing deep learning (DL)-based surrogate generalized ground motion models (GGMMs) and analyzing them to understand the patterns between the earthquake source parameters and the resulting ground motion waveforms’ engineering characteristics. The GGMMs are developed using long short-term memory (LSTM) based recurrent neural networks (RNNs), which are trained using six earthquake source and site parameters as the inputs and a 25 × 1 vector of amplitude-, duration-, and energy-based ground motion intensity measures (IMs). The GGMMs are trained and evaluated using carefully selected large datasets of ground motion records from the Japanese subduction, Chilean subduction, and Californian crustal sources (∼2000 records from each source). The models are developed in two settings: i) three independent GGMMs using the three datasets of each source, ii) one combined GGMM using the combined dataset. While the former provides individual surrogate models of the regional seismic environments and allows relative comparison among the three environments, the latter acts as a global seismic surrogate model and allows comparison in absolute terms. The seismic environments are investigated by analyzing the two types of GGMMs using explainable artificial intelligence (XAI) and game theory based Shapley explanations (SHAP). As the direct physical study of the seismic environments is not generally feasible/practical, the proposed GGMMs surrogating the process becomes a source of knowledge. By interpreting them, inferences about the seismic environments are derived. Results indicate the peculiar nature of the earthquakes arising from the three seismic backgrounds, further emphasizing the importance of conducting independent regional seismic hazard and risk analysis. In particular, the role of magnitude and rupture distance is observed to have a significantly different impact on the different IMs of the three different environments. The study further sets a novel basis to utilize advanced DL and XAI methods in understanding convoluted physics and engineering phenomena.

Revised Empirical Relations Between Earthquake Source and Rupture Parameters by Regression and Machine Learning Algorithms

Revised Empirical Relations Between Earthquake Source and Rupture Parameters by Regression and Machine Learning Algorithms

PREM-like velocity structure in the outermost core from global SKS and ScS waveform modeling

Seismic structure in the Earth's outermost core is important for our understanding of thermochemical stratification in the outer core, and is yet heavily debated. Here we study the compressional velocity structure in the Earth's outermost core based on waveform modeling of a unique untapped SKS and ScS dataset near bifurcation distances, collected from global seismic arrays for earthquakes occurring from 2000 to 2020. Using the SKS-ScS array dataset minimizes the effects of many uncertainties associated with earthquake source parameters and seismic heterogeneities in the mantle, and affords an opportunity to study and assess the seismic structure in the outermost core. We study outer core structure by testing two end-member models: 1) the D″ model that attributes any anomalous seismic observations to the effects of the lowermost mantle structure and is paired with a PREM (the Preliminary Reference Earth's Model) structure in the outermost core and 2) the outer core model that is paired with either a PREM or a tomographic structure in the lowermost mantle and attributes other unexplained seismic signals to an outer core structure. The results of the outer core models present unreasonable large lateral variations of >3.1% in the outermost core, while the inferred D″ models exhibit large-scale seismic anomalies that are consistent with the tomographic models and small-scale anomalies that are confirmed by further analysis of the seismic array data. Our analyses suggest a PREM-like seismic velocity structure and a lack of strong thermochemical anomalies in the topmost ∼200 km of the outer core, placing bounds on possible thermal and compositional conditions in the region of the Earth's outermost core. Our study also identifies the existence of small-scale seismic anomalies and sharp velocity variations in the lowermost mantle beneath the south coast of Alaska, northwestern Atlantic and the middle of Central America.

Quantifying Site Effects and Their Influence on Earthquake Source Parameter Estimations Using a Dense Array in Oklahoma

AbstractWe investigate the effects of site response on source parameter estimates using earthquakes recorded by the LArge‐n Seismic Survey in Oklahoma (LASSO). While it is well known that near‐surface unconsolidated sediments can cause an apparent breakdown of earthquake self‐similarity, the influence of laterally varying site conditions remains unclear. We analyze site conditions across the 1825‐station array on a river plain within an area of 40 km by 23 km using vertical ground motions from 14 regional earthquakes. While the source radiation pattern controls P‐wave ground motions below 8 Hz, the surface geology correlates with P‐wave ground motions above 8 Hz and S‐wave ground motions at 2–21 Hz. Stations installed in alluvial sediments have vertical ground motions that can exceed three times the array median. We use the variation of ground motion of regional earthquakes across the array as a proxy for site effects. The corner frequencies and stress drops of local earthquakes (ML = 0.01–3) estimated using a standard single‐spectra approach show negative correlations with the site‐effect proxy, while the seismic moments show positive correlations. In contrast, the spectral‐ratio approach effectively shows no correlation. The overall bias is small as expected for this relatively homogeneous structure; accurate estimation of site‐related biases requires at least 30 stations. Correcting for site‐related biases reduces the standard deviations of the source parameters by less than 13% of the total variations. Remaining variations are partially associated with source directivity and model misfits— as small earthquakes can have complex ruptures.

Peak Ground Velocity (PGV) estimation of Mw 5.6 Cianjur earthquake November 11, 2022 using Global Positioning System (GPS)

The primary purpose of GPS is to be used for positioning, navigation, and timing (PNT). In the last decade, research on GPS for earthquake hazards has increased rapidly, such as for tectonic deformation, GPS seismology, modeling earthquake source parameters, estimation of magnitude, etc. In this study, following the Mw 5.6 Cianjur earthquake on November 11, 2022, we estimate the peak ground velocity from the CJUR GPS station, which is located about 15 km from the epicenter. We compute GPS velocity from the high-rate GPS receiver independent exchange (RINEX) data and broadcast ephemeris. Using a narrow line combination of L1 and L2 observations, we invert velocity epoch by epoch. The result shows the CJUR GPS station has a peak ground velocity of about 11.5 cm/s and equal to VI MMI scale. The peak ground velocity from the GPS station could be important for deriving the earthquake shakemap. By increasing the number of GPS stations in Indonesia, it is very important to support earthquake hazard monitoring.

An application for the earthquake spectral and source parameters and prediction using adaptive neuro fuzzy inference system and machine learning

Parameters related to earthquake origins can be broken down into two broad classes: source location and source dimension. Scientists use distance curves versus average slowness to approximate the epicentre of an earthquake. The shape of curves is the complex function to the epicentral distance, the geological structures of Earth, and the path taken by seismic waves. Brune’s model for source is fitted to the measured seismic wave’s displacement spectrum in order to estimate the source’s size by optimising spectral parameters. The use of ANFIS to determine earthquake magnitude has the potential to significantly alter the playing field. ANFIS can learn like a person using only the data that has already been collected, which improves predictions without requiring elaborate infrastructure. For this investigation’s FIS development, we used a machine with Python 3x running on a core i5 from the 11th generation and an NVIDIA GEFORCE RTX 3050ti GPU processor. Moreover, the research demonstrates that presuming a large number of inputs to the membership function is not necessarily the best option. The quality of inferences generated from data might vary greatly depending on how that data is organised. Subtractive clustering, which does not necessitate any type of normalisation, can be used for prediction of earthquakes magnitude with a high degree of accuracy. This study has the potential to improve our ability to foresee quakes larger than magnitude 5. A solution is not promised to the practitioner, but the research is expected to lead in the right direction. Using Brune’s source model and high cut-off frequency factor, this article suggests using machine learning techniques and a Brune Based Application (BBA) in Python. Application accept input in the Sesame American Standard Code for Information Interchange Format (SAF). An application calculates the spectral level of low frequency displacement (Ω0), the corner frequency at which spectrum decays with a rate of 2(fc), the cut-off frequency at which spectrum again decays (fmax), and the rate of decay above fmax on its own (N). Seismic moment, stress drop, source dimension, etc. have all been estimated using spectral characteristics, and scaling laws. As with the maximum frequency, fmax, its origin can be determined through careful experimentation and study. At some sites, the moment magnitude was 4.7 0.09, and the seismic moment was in the order of (107 0.19) 1023. (dyne.cm). The stress reduction is 76.3 11.5 (bars) and the source-radius is (850.0 38.0) (m). The ANFIS method predicted pretty accurately as the residuals were distributed uniformly near to the centrelines. The ANFIS approach made fairly accurate predictions, as evidenced by the fact that the residuals were distributed consistently close to the centerlines. The R2, RMSE, and MAE indices demonstrate that the ANFIS accuracy level is superior to that of the ANN.

Composite Source Model for Broadband Ground-Motion Simulations: 2021 Mw 7.4 Maduo, China, Earthquake Case Study

Abstract On 22 May 2021, at 2:40 a.m. (local time), an earthquake with a moment magnitude of 7.4 was reported to have occurred in Maduo County (34.59° N, 98.34° E), China. The focal depth was 17 km. After the earthquake, many scholars inverted the slip distribution of the Maduo earthquake based on ground-motion recordings, and the Global Positioning System and the Interferometric Synthetic Aperture Radar data. To verify the reliability of the fast estimation source model method adopted in this study, the hybrid slip model of the 2011 Mw 7.4 Maduo earthquake, in Qinghai Province, China, is synthetized based on the stochastic finite-fault model and the semiempirical relation between the earthquake magnitude (Mw) and source parameters. The truncated normal distribution and regional crustal structure are used to generate 30 sets of global source parameters, which describes that the deterministic characteristics of the source mainly include the size of the rupture fault, and the average slip, and the local source parameters. The local parameters include asperity and k2 model parameters. Finally, stochastic finite-fault modeling is adopted to synthetic the acceleration response spectra at four selected sites meeting different geological conditions and orientations, and the minimum residual principle is used to select the final slip model for the Maduo earthquake with the minimum residual of the average response spectra. The research results show that the width, maximum slip, and asperity body position of the source model estimated in this study are consistent with those of the inversion slip model, which verifies the reliability and applicability of this method. This work provides technical and theoretical support for rapid prediction of the source model of earthquakes with specific magnitudes.

Determination of Paleotsunami Inundation Zone Using Finite Difference Method in Southern Coastal Lebak, Banten

Paleotsunami deposits have been found in Indonesia, including in Pacitan, Kulon Progo, Cilacap, Pangandaran, and Sukabumi. This research aims to obtain an ideal model for determining the paleotsunami inundation zone along the southern coastal Lebak, Banten using finite difference numerical modeling through the MOST (Method of Splitting Tsunamis)-based ComMIT software. Field observations were made to identify the presence of paleotsunami deposits in the swale area on the southern coastal Lebak, Banten. Several tsunami scenarios such as Pangandaran in 2006, Indian Ocean Tsunami (IOT) in 2004, Tohoku in 2011, Sunda Strait megathrust segment, and megathrust along the south of Java were used in the modeling process to identify the characteristics of paleotsunami that were produced the tsunami deposits in the study area. Modeling is carried out using two schemes, such as using the ComMIT database unit source (subduction zone segments) and using the earthquake source parameters that are entered manually. The results show the best tsunami scenario that may have ever occurred in research area is the tsunami event with magnitude of 9.1 Mw, with maximum inundation range of 5.2 km, and run-off of up to 32 m, which is estimated as a tsunami event that precipates paleotsunami deposits in study area.