Earthquake Model Research Articles

Comparing Solutions to the Linear Space-Fractional Telegraph Equation

The applications of the fractional differentiation for the mathematical modelling of real-world physical problems such as the earthquake modelling, and allometric scaling laws in biology, etc., have been widespread in this modern era. Fractional differential theory has gained much more attention as the fractional order system response ultimately converges to the integer order equations. Differential equations of fractional order have been successfully employed for modelling the so-called anomalous phenomena during last two decades. One of these nonlinear partial differential equations, the linear space-fractional telegraph equation. That applied into signal analysis for transmission, propagation of electrical signals, and so on. The aim of this article is to compare were the fractional Sumudu decomposition method (SDM), a double Sumudu matching transformation method, a finite difference scheme, a finite difference scheme based on a combination of the extended cubic B-splines (ExCuBs) method, and a quadratic spline functions with the solution of the linear space-fractional telegraph equation. We will conduct a comparison of the stability of the methods and convergence. In addition, numerical examples will be presented to illustrate the accuracy of these methods.

Influence of Physical Characteristics of Earthquake Input Model on Seismic Response of Hardfill Dam

ABSTRACT To elucidate the influence of physical characteristics of earthquake input models on seismic responses of Hardfill dams, three earthquake input models are selected to perform investigations. First, the theoretical basis of earthquake input models is presented, and the radiation-damping effect and the complex wavefield effect derived from the physical characteristics are clarified. Then, the seismic response of a Hardfill dam under different earthquake input models is investigated, by taking the Oyuk dam as an example. Finally, the influence of the radiation-damping effect and the complex wavefield effect on the seismic response of the Hardfill dam are revealed quantificationally. The results show that only the complex wavefield model can accurately capture characteristics of the wavefield and the motion field. The radiation-damping effect significantly reduces the seismic response, while the complex wavefield effect increases it in most cases.

Adaptive compensation for multi-axial real-time hybrid simulation via nonlinear parameter estimation

For Real-time hybrid simulation (RTHS) to be stable and accurate, it is essential to address the time desynchronization issue between the numerical and physical substructures. Desynchronization is primarily caused by time delays, inherent dynamics of the control plant, system uncertainties, and noises. While existing adaptive compensators have shown effective tracking performance in single-input single-output (SISO) RTHS, their effectiveness in multi-input multi-output (MIMO) RTHS has not been fully demonstrated. MIMO-RTHS presents additional challenges due to its larger solution space, and significant dynamic coupling between actuators. To address these challenges, this study introduces an adaptive compensation framework for MIMO-RTHS. The proposed framework utilizes a control law based on the inverse dynamics of the control plant, incorporating real-time adaptive parameter updates through Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) methods. Both the transfer function (TF) and discrete-time state-space (SS) models of the plant are employed in distinct parameter estimation cases. The performance of the proposed compensation is validated through a multi-axial RTHS (maRTHS) benchmark problem. Extensive simulations on the maRTHS incorporating various earthquake inputs, sensor noise, and model uncertainties, demonstrated an excellent tracking performance and strong robustness across four parameter estimation cases (EKF-TF, UKF-TF, EKF-SS, and UKF-SS). The use of UKF with SS model (UKF-SS) achieved superior performance, effectively managing nonlinearities and noise without requiring low-pass filtering.

Coseismic Shallow Slip Deficit Accounted for by Diffuse Off‐Fault Deformation

AbstractEarthquake ruptures produce fault slip and kilometer‐wide diffuse deformation of the host rocks. However, the origin of the diffuse deformation and its role in the rupture process are debated. We produce a refined slip model for the 2019 Ridgecrest, California, earthquakes, and analyze the relations between down‐dip rupture process, and surface diffuse deformation. We show that the decrease in coseismic slip toward the ground surface, also known as shallow slip deficit (SSD), correlates with the occurrence of diffuse deformation at the surface, which is not accounted for by models assuming elastic host rocks. Hence, we suggest that a significant part of the SSD in earthquake source models could be interpreted as a proxy for shallow diffuse inelastic deformation around faults. Revisiting earthquake source models for 28 continental earthquakes, we discuss the controlling parameters of the SSD and diffuse deformation, and propose a conceptual model of the near‐field coseismic surface deformation.

The evolution of online public opinion on earthquakes: a system dynamics approach

Earthquakes endanger lives and properties and trigger widespread online public opinion. If the government fails to implement emergency measures quickly, the risks associated with this online public opinion may escalate and present significant challenges in managing it. To solve this issue, this paper investigates the M5.7 earthquake in Yibin, China, as a case study, drawing data from China’s Sina Weibo. It also develops an evolutionary model of earthquake online public opinion using the life cycle theory of public opinion dissemination, public opinion crisis management theory, and system dynamics methods. This study chooses a system dynamic model because it can capture overall trends and feedback cycles. This approach provides a comprehensive view of the evolution of the whole system’s behavior and public opinion during the crisis. This model analyzes the evolution of the online public opinion of the earthquake from the perspective of the earthquake, netizens (social media users), media, and government. This study explores the differences between online public opinion by adjusting the size of different variables in the model. This provides an entry point for better control of the development and communication of earthquake online public opinion. The findings indicate that, following the earthquake, the media exerted the most significant influence on public opinion, mainly through media participation, diffusion, and impact, demonstrating a significantly positive correlation with the magnitude of public opinion. Moreover, the simulation revealed that the government is leading in preventing and controlling online public opinion. This study presents the following innovations: (1) It builds a comprehensive online public opinion model for earthquakes, which provides insights for studying online public opinion regarding other sudden natural disasters. (2) It employs system dynamics to simulate earthquake online public opinion, providing countermeasures and recommendations for the government to manage and regulate this discourse effectively.

The Collaborative Seismic Earth Model: Generation 2

AbstractGeological interpretations, earthquake source inversions and ground motion modeling, among other applications, require models that jointly resolve crustal and mantle structure. With the second generation of the Collaborative Seismic Earth Model (CSEM2), we present a global multi‐resolution tomographic Earth model that serves this purpose. The model evolves through successive regional‐ and global‐scale refinements. While the first generation aggregated regional models, with this study, we ensure consistency between all individual submodels, resulting in a model that accurately explains wave propagation across scales. Recent regional tomographic models were incorporated, comprising continental‐scale inversions for Asia and Africa, as well as regional inversions for the Western US, Central Andes, Iran, and Southeast Asia. Across all regional refinements, over 793,000 source‐receiver pairs contributed. Moreover, the long‐wavelength Earth model (LOWE) introduces large‐scale structures outside of pre‐existing local refinements. A full‐waveform inversion for global anisotropic P‐and S‐wave speed structure over a total of 194 iterations with a minimum period of 50 s on a large data set of 1 hr of waveform data from 2,423 earthquakes and over 6 million source‐receiver pairs ensures that regional updates in the crust and uppermost mantle translate into updates of deeper, global‐scale structure. To test the performance of CSEM2, we evaluate waveform fits between observed and synthetic seismograms at 50 s for an independent data set on the global scale, and on the regional scale for lower periods. We accurately simulate waveforms within and across regional refinements, maintaining the original resolution of the submodels embedded in the global framework.

A rupture directivity adjustment model and its application in seismic hazard

This article contains a rupture directivity adjustment model for strike-slip earthquakes that can be applied to a traditional ground-motion model (GMM; one without explicit treatment of rupture directivity) to incorporate rupture directivity effects in either deterministic or probabilistic seismic hazard analyses. Application of the directivity model requires adjustments to both the GMM median and aleatory variability. The model described herein supersedes the previous models for strike-slip earthquakes developed by the authors of this article (Abrahamson, 2000; Bayless et al., 2020; Somerville, 2003; and Somerville et al., 1997; chapter 2 of Spudich et al., 2013). A key feature of the directivity model is that it is centered because it does not alter the magnitude and distance scaling of the GMM when averaged over uniformly distributed sites at a given rupture distance. Additionally, we address a longstanding issue regarding the directivity condition contained in the NGA-W2 dataset (Ancheta et al., 2014) and conclude that the biases in the mean and standard deviation of directivity effects are small enough to ignore for the purposes of modeling directivity. We provide guidance on the directivity model implementation, including a review of deterministic and probabilistic seismic hazard applications, and recommend methods for modeling hypocenter locations and multisegment ruptures. The model addresses the RotD50 (Boore et al., 2010) horizontal component of 5% damped spectral acceleration. A future update will address directionality and directivity effects for other styles of faulting. Implementations of the model are provided in the electronic supplement.

Poroelastic effects on rupture propagation across fault stepovers

The role of poroelasticity in influencing the frequency of ruptures jumping through strike-slip stepovers remains unclear. To understand how poroelastic effects govern long-term rupture behavior in strike-slip fault systems with stepovers, we conduct earthquake sequence simulations incorporating undrained pore pressure responses across the full spectrum of Skempton's coefficient. Our findings reveal that Skempton's coefficient significantly affects the effective normal stress, which can either cause fault clamping or unclamping, and ultimately influences rupture propagation across fault stepovers. The likelihood of rupture jumping is predominantly determined by Skempton's coefficient and the width of the stepover, with Skempton's coefficient showing an approximately linear relationship to the critical jumpable step size. Specifically, a higher Skempton's coefficient facilitates rupture jumping across fault segments, even over larger stepover distances. Analytical solutions involving dislocation and Skempton's coefficient provide practical methods for evaluating pore pressure changes and associated seismic hazards near fault stepovers. Our statistical analysis identifies a critical jumpable width of 4.4–5.1 km due to static stress transfer, assuming a typical range of Skempton's coefficient for compressional stepovers, beyond which ruptures are unlikely to propagate. This study underscores the potential of using physics-based earthquake sequence models to reflect statistical fault rupture behaviors. Given that multi-segment earthquake ruptures present challenges in assessing maximum rupture lengths, our findings offer crucial insights into the role of poroelastic effects and the conditions that facilitate or limit rupture propagation across fault stepovers.

A Novel Method for Evaluating Earthquake Forecast Model Performance and Its Implications for Refining Seismic Likelihood Model

Summary Utilizing statistical tests to evaluate earthquake forecasting models is crucial to improve forecasting strategies for seismic hazard assessment. We develop a novel evaluation method for alarm-based earthquake forecast, taking into account the magnitude of seismic energy and the impact area of earthquakes, instead of using solely seismic event number and epicenter locations in conventional approaches. First, we derive a scale-law of Seismic Area by statistically analyzing coseismal maps of past M ≥ 7.0 earthquakes. Second, we proportionally allocate Seismic Moment to surrounding cells based on corresponding seismic area within each cell (SASM-test). Compared to the Molchan test which is conventionally applied for models that forecast the epicenter location, our proposed SASM-test can be applied to the evaluation of forecasting models that focus on the whole earthquake rupture (source area). Third, we apply the SASM-test method to the time-independent probabilistic earthquake forecasting model for the southeastern Tibetan Plateau (RELM-TibetSE) and compare it with other evaluation methods. The retrospective testing shows that the SASM-test demonstrate relatively higher sensitivity, enabling to detect subtle differences between similar models that conventional methods may overlook. Additionally, retrospective test results indicate that: (a) Earthquake forecasting models using GNSS data performed better in forecasting the ‘source area’ than the ‘epicenter location’; (b) Forecasting models based on principal strain rate outperformed the models based on maximum shear strain rate in forecasting both the epicenter location and the source area; (c) Incorporating spatially varying seismogenic layer thickness and rigidity into seismic forecasting models could improve their ability to forecast the ‘source area’ compared to using uniform seismogenic layer properties. The newly proposed SASM-test method can provide a more sensitive and comprehensive approach for the evaluation of earthquake forecasting models, contributing to the refinement of seismic hazard assessments.

Duration Models for Subduction Earthquakes

ABSTRACT An empirical model for the significant duration of acceleration for subduction zone earthquakes is developed based on the normalized Arias intensity. The data set is a subset from the Next Generation Attenuation-Subduction database (Bozorgnia and Stewart, 2020). The functional form of the duration model uses an additive model for the source, path, and site terms with three lognormally distributed random effects for the source, path, and site. The remaining aleatory variability is modeled by a power-normal distribution. There are two parts of the duration model: a model for the D5–75 duration and a model for the D5–X/D5–75 ratio, which can be combined with the D5–75 model to estimate the DY–X for any interval. The D5–75 model includes regionalized coefficients for Japan, New Zealand, South America, and Taiwan. The large-magnitude (M >7) scaling of the D5–75 model is much weaker than the recent duration model by Bahrampouri et al. (2021). The residuals of the D5–75 model are negatively correlated with the lnPGA residuals with a correlation coefficient of −0.50 such that above-average peak ground acceleration values are associated with below-average duration values.

Development of a regionally consistent and fully probabilistic earthquake risk model for Central Asia

Abstract. A fully probabilistic earthquake risk model was developed for five countries in Central Asia, providing updated earthquake loss estimates with a higher level of detail on all components with respect to previous studies in the region. Additionally, a regionally consistent approach that, on the one hand, allows for direct comparisons at different disaggregation levels (e.g., country and oblast) was used. On the other hand, this updated earthquake risk assessment aims to facilitate initiating a policy dialogue regarding national and regional disaster risk financing and insurance applications. This earthquake risk model made use of a regional probabilistic seismic hazard analysis and a comprehensive exposure database on which different types of assets and sectors were included and for which two scenarios (the years 2020 and 2080) were modeled. For each type of asset, a unique vulnerability function was derived and later used for convolution with the hazard data, which allowed for the estimation of the loss exceedance curve, at different disaggregation levels, from where other risk metrics such as the average annual loss (AAL) and specific return period losses were obtained. The regional earthquake AAL for the five countries and for the 2020 exposure scenario has been estimated to be around USD 2 billion, with the Kyrgyz Republic and Tajikistan being the countries with the highest earthquake risk levels in the region. Besides the probabilistic earthquake risk results, as-if scenarios were modeled using a pseudo-deterministic approach to assess the human and economic losses for realistic and representative earthquakes for the main cities within earthquake-prone regions in the five countries of the study area.

Real-time modeling of transient crustal deformation through the quantification of uncertainty deduced from GNSS data

We propose a new method for real-time uncertainty monitoring of earthquake and volcano source models using data from the global navigation satellite system (GNSS) and explore its application concerning observation station placement. The Geospatial Information Authority of Japan operates two main types of GNSS earth observation network system (GEONET) coordinates for crustal deformation monitoring on different time scales: post-processing analysis values and real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD uses the Markov Chain Monte Carlo (MCMC) method termed real-time automatic uncertainty estimation of a coseismic single rectangular model using GNSS data (RUNE) for single rectangular fault model estimation to handle uncertainty. Thus far, no GNSS monitoring system can automatically detect transient crustal deformation events, such as volcanic activity and earthquake swarms, on timescales of a day or less. We extended RUNE and developed a core program for a new monitoring system for earthquake and volcanic source models and their uncertainties. Our program achieved automatic and stable MCMC utilization for rectangular fault, dike, Mogi, and spheroid models by increasing the computational speed, improving search efficiency, and adjusting hyperparameters. The program automatically determines the standard deviation of the likelihood function assuming a normal distribution with weights for each observation station. The calculation time was within 15 s for 1 × 106 samples on a standard 1U server. We assessed the reliability of the developed method using synthetic and observed GNSS data from the 2015 Sakurajima volcanic event. The results were consistent with the assumed model and previous studies and indicated an advantage in automatically quantifying uncertainty in a short computation time. Based on MCMC samples, we developed a new visualization algorithm to indicate areas on a map in which the number of observation stations should be expanded. We assessed the reliability using data from the 2023 Noto Peninsula earthquake [Mj 6.5]. The results indicate that the algorithm is helpful in studying the placement of stations. The above model extensions and their application are essential to achieve a rapid quantitative understanding of disaster events near urban areas and for utilizing this information in emergency response activities.Graphical

Limited Preservation of Strike‐Slip Surface Displacement in the Geomorphic Record

AbstractOffset geomorphic markers are commonly used to interpret slip history of strike‐slip faults and have played an important role in forming earthquake recurrence models. These data sets are typically analyzed using cumulative probability methods to interpret average amounts of slip in past earthquakes. However, interpretation of the geomorphic record to infer surface slip history is complicated by slip variability, measurement uncertainty, and modification of offset features in the landscape. To investigate how well geomorphic data record surface slip, we use offset measurements from recent strike‐slip surface ruptures (n = 39), faults with geomorphic evidence of multiple strike‐slip earthquakes (n = 29), and synthetic slip distributions with added noise (n10,000) to examine the constraints of the geomorphic record and the underlying assumptions of the cumulative offset probability distribution analysis method. We find that the geomorphic record is unlikely to resolve more than two paleo‐slip distributions, except in specific cases with low slip variability, high slip‐per‐event, and semiarid climate. In cases where site‐specific conditions allow for interpretation of more than two earthquakes, lateral extrapolation along a fault is not straightforward because on‐fault displacement and distributed deformation may be spatially variable in each earthquake. We also find that average slip in modern earthquakes is adequately recovered by probability methods, but the reported prevalence of strike‐slip faults with characteristic slip history is not supported by geomorphic data. We also propose updated methods to interpret slip history and construct uncertainty bounds for paleo‐slip distributions.

Dynamic Rupture Modeling of Large Earthquake Scenarios at the Hellenic Arc Toward Physics‐Based Seismic and Tsunami Hazard Assessment

AbstractThe Mediterranean Hellenic Arc subduction zone (HASZ) has generated several 8 earthquakes and tsunamis. Seismic‐probabilistic tsunami hazard assessment typically utilizes uniform or stochastic earthquake models, which may not represent dynamic rupture and tsunami generation complexity. We present an ensemble of ten 3D dynamic rupture earthquake scenarios for the HASZ, utilizing a realistic slab geometry. Our simplest models use uniform along‐arc pre‐stresses or a single circular initial stress asperity. We then introduce progressively more complex models varying initial shear stress along‐arc, multiple asperities based on scale‐dependent critical slip weakening distance, and a most complex model blending all aforementioned heterogeneities. Thereby, regional initial conditions are constrained without relying on detailed geodetic locking models. Varying epicentral locations in the simplest, homogeneous model leads to different rupture speeds and moment magnitudes. We observe dynamic fault slip penetrating the shallow slip‐strengthening region and affecting seafloor uplift. Off‐fault plastic deformation can double vertical seafloor uplift. A single‐asperity model generates a 8 scenario resembling the 1303 Crete earthquake. Using along‐strike varying initial stresses results in 8.0–8.5 dynamic rupture scenarios with diverse slip rates and uplift patterns. The model with the most heterogeneous initial conditions yields a 7.5 scenario. Dynamic rupture complexity in prestress and fracture energy tends to lower earthquake magnitude but enhances tsunamigenic displacements. Our results offer insights into the dynamics of potential large Hellenic Arc megathrust earthquakes and may inform future physics‐based joint seismic and tsunami hazard assessments.

Population displacement after earthquakes: benchmarking predictions based on housing damage

In the aftermath of an earthquake, the number of residents whose housing was destroyed is often used to approximate the number of people displaced (i.e., rendered homeless) after the event. While this metric can provide rapid situational awareness regarding potential long-term housing needs, more recent research highlights the importance of additional factors beyond housing damage within the scope of household displacement and return (e.g., utility disruption, tenure, place attachment). This study benchmarks population displacement estimates using this simplified conventional approach (i.e., only considering housing destruction) through three scenario models for recent earthquakes in Haiti, Japan, and Nepal. These model predictions are compared with officially reported values and alternate mobile location data-based estimates from the literature. The results highlight the promise of scenario models to realistically estimate population displacement and potential long-term housing needs after earthquakes, but also highlight a large range of uncertainty in the predicted values. Furthermore, purely basing displacement estimates on housing damage offers no view on how the displaced population counts vary with time as compared to more comprehensive models that include other factors influencing population return or alternative approaches, such as using mobile location data.

Assessing the Adequacy of Earthquake Catalog Sampling for Long-Term Seismicity in Low-to-Moderate Seismic Regions: A Geodetic Perspective

Abstract Seismic hazard assessment in low-to-moderate seismicity regions can benefit from the knowledge of surface deformation rates to better constrain earthquake recurrence models. This, however, amounts to assuming that the known seismicity rate, generally observed over historical times (i.e., up to a few centuries in Europe), provides a representative sample of the underlying long-term activity. We here investigate how this limited sampling can affect the estimated seismic hazard and whether it can explain the disagreement between the seismic moment loading rate as seen by nowadays Global Navigation Satellite Systems (GNSS) measurements and the seismic moment release rate by past earthquakes, as is sometimes observed in regions with limited activity. We approach this issue by running simulations of earthquake time series over very long timescales that account for temporal clustering and the known magnitude–frequency distribution in such regions, and that those are constrained to a seismic moment rate balance between geodetic and seismicity estimates at very long timescales. We show that, in the example of southeastern Switzerland, taken here as a case study, this sampling issue can indeed explain this disagreement, although it is likely that other phenomena, including aseismic deformation and changes in strain rate due to erosional and/or glacial rebound, may also play a significant role in this mismatch.

A brief overview of the past, present and future of the Global Earthquake Model (GEM) Foundation

The Global Earthquake Model (GEM) is an initiative that originally emerged from discussions and proposals made by the Global Science Forum of the Organization for Economic Co-operation and Development (OECD) in the early 2000’s. In 2009, GEM established itself as a non-profit legal entity, the GEM Foundation, and 15 years later, it continues to operate globally with a mission to provide transparent resources for seismic hazard and risk assessment, as well as to support disaster risk reduction for a wide range of natural hazards. This paper highlights the main milestones from the past 15 years and the vision of the foundation to 2030. An overview is also provided of the status of GEM products and services, all of which are made available for the benefit of society, under a strategy that aims to assure the continued financial sustainability of the organization.

A Stochastic Model for Induced Seismicity at the Geothermal Systems: A Case of the Geysers

Abstract Induced seismicity has emerged as a source of a significant earthquake hazard associated with recent development of unconventional energy resources. Therefore, it is imperative to develop stochastic models that can accurately describe the observed seismicity rate and forecast its evolution. In this study, a mechanism suggested by linear response theory is incorporated into a stochastic earthquake model to account for changes in the seismicity rate. It is derived that the induced rate can be modeled as a convolution of the forcing, related to fluid injection operations, and a specific response kernel. The model is incorporated into a Bayesian framework to compute the probabilities for the occurrence of the largest expected events during future time intervals. The applicability of the model is illustrated by analyzing the injection and seismicity data at the Geysers geothermal field in California. The suggested approach provides further insight into the probabilistic assessment of earthquake hazard associated with fluid injection operations. It also can be used for probing the rheological properties of the subsurface by analyzing the inherent characteristic timescales associated with the subsurface response to external forcing.

New Features in the pyCSEP Toolkit for Earthquake Forecast Development and Evaluation

Abstract The Collaboratory for the Study of Earthquake Predictability (CSEP) is a global community dedicated to advancing earthquake predictability research by rigorously testing probabilistic earthquake forecast models and prediction algorithms. At the heart of this mission is the recent introduction of pyCSEP, an open-source software tool designed to evaluate earthquake forecasts. pyCSEP integrates modules to access earthquake catalogs, visualize forecast models, and perform statistical tests. Contributions from the CSEP community have reinforced the role of pyCSEP in offering a comprehensive suite of tools to test earthquake forecast models. This article builds on Savran, Bayona, et al. (2022), in which pyCSEP was originally introduced, by describing new tests and recent updates that have significantly enhanced the functionality and user experience of pyCSEP. It showcases the integration of new features, including access to authoritative earthquake catalogs from Italy (Bolletino Seismico Italiano), New Zealand (GeoNet), and the world (Global Centroid Moment Tensor), the creation of multiresolution spatial forecast grids, the adoption of non-Poissonian testing methods, applying a global seismicity model to specific regions for benchmarking regional models and evaluating alarm-based models. We highlight the application of these recent advances in regional studies, specifically through the New Zealand case study, which showcases the ability of pyCSEP to evaluate detailed, region-specific seismic forecasts using statistical functions. The enhancements in pyCSEP also facilitate the standardization of how the CSEP forecast experiments are conducted, improving the reliability, and comparability of the earthquake forecasting models. As such, pyCSEP exemplifies collaborative research and innovation in earthquake predictability, supporting transparent scientific practices, and community-driven development approaches.

Seismic reliability analysis using Subset Simulation enhanced with an explorative adaptive conditional sampling algorithm

Reliability analysis of structures under earthquake loading represents a significant engineering challenge. This is due to the required and rather numerically involving non-linear dynamic analysis, the large computational burden when targeting small failure probabilities, and the synthetic earthquake model representation that may contain thousands of random variables. Subset Simulation is an efficient reliability analysis technique that can handle the challenge of a high-dimensional space with a reduced number of structural analysis calls compared to crude Monte Carlo Simulation. In this contribution, firstly, we investigate the conditions for which Subset Simulation performs efficiently. Thereafter we propose an enhancement to the existing Subset Simulation schemes that shows significant potentials for enhancing the strategy for the starting of the Markov Chain Monte Carlo simulations whenever a new level is reached in the Subset Simulation. Finally, the information gathered from the simulations is investigated to verify that Subset Simulation provides meaningful results from a physical point of view.