Seismic Hazard Analysis Research Articles

Smoothing-Based Aftershock Probabilistic Seismic Hazard Assessment Using the Spatiotemporal ETAS Model

ABSTRACT Probabilistic seismic hazard analysis (PSHA) is generally based on computing time-invariant occurrence rates of mainshocks using the Poisson process. However, aftershock probabilistic seismic hazard analysis (APSHA) allows for assessing time-varying aftershock occurrence rates within a short-term seismic hazard mitigation framework. Our proposed methodology of APSHA develops a smoothing-based analytical formulation to capture the spatial distribution and temporal evolution of aftershock sequences using the spatiotemporal epidemic-type aftershock sequence model. This approach is tested on case studies of the 2013 Bushehr, 2021 and 2022 Hormozgan seismic events, and characterizes the aftershocks’ hierarchical structure to improve the reliability of aftershock hazard assessments. Then, the results of APSHA (aftershock ground-motion hazard at specific sites) based on smoothing are compared with conventional PSHA (pre-mainshock ground-motion hazard at specific sites). This comparative analysis highlights the importance of considering aftershock effects when assessing ground-motion hazards because PSHA does not fully account for aftershock hazard increases following major earthquakes.

Study of Earthquake Hazard Probability Based on PGA (Peak Ground Acceleration) Using the PSHA Method in Semarang Regency, Indonesia

Semarang Regency was one of the areas affected by the earthquake. This happens because it is located north of the subduction zone and has active faults, so research is needed to identify the impact of earthquakes in Semarang Regency as an initial action in disaster mitigation efforts. This research aims to determine the distribution of PGA values in the Semarang Regency area and to assess the seismic hazard due to earthquakes based on a map of the distribution of PGA values in the Semarang Regency area. The method used is the Probabilistic Seismic Hazard Analysis (PSHA) method, which uses earthquake catalogues from BMKG, USGS, IRIS, and ISC. The earthquake source data was the n processed using ZMAP and R-CRISIS software. The results of seismic hazard analysis in the Semarang Regency area for a probability of exceeding 10% in 50 years or an earthquake return period of 475 years in bedrock with a period of 0 seconds obtained a PGA value ranging from 0.368–0.844 g, for a short period of 0.2 seconds the PGA value obtained ranged from 0.647. –1.773 g, for 1 second, the PGA value obtained ranges from 0.320–0.852 g. The highest PGA value is in West Ungaran District, with a value of 0.844 g. The lowest PGA value is in Kaliwungu District, which has a value of 0.458 g. The active fault zones influence the danger of earthquakes in the Semarang Regency area in Semarang Regency.

Performance-based liquefaction analysis and probabilistic liquefaction hazard mapping using CPT data within the Fraser River delta, Canada

Since the Fraser River delta is affected by multiple seismic sources, the selection of a single combination of magnitude and maximum ground acceleration to evaluate liquefaction initiation is challenging. In this study, we use a probabilistic seismic hazard analysis to account for all earthquake scenarios and also consider the liquefaction model uncertainty and soil resistance uncertainty using 787 CPT data across the study area. Hazard curves are generated for the factor of safety against liquefaction (FSL) and the amount of required soil improvement to prevent liquefaction (ΔqL) which provide a comprehensive assessment of liquefaction triggering. We derive the FSL and ΔqL values corresponding to return periods of 475 and 2475 years and generate the first probabilistic liquefaction hazard mapping for the region. Most of the lowland areas of Metro Vancouver correspond to low FSL and therefore soil improvement is needed for liquefaction mitigation.

Seismic Site Characterization of Coimbatore city, Tamil Nadu, India using the Multi-channel Analysis of Surface Waves (MASW) test and Correlations between shear-wave velocity and SPT - N

Seismic Site Characterization involves the qualitative assessment of top-soil properties that have the capability of amplifying the generated earthquake ground motions. The geotechnical properties of topsoil refer to the top 30-m subsurface profile which plays a vital role in seismic microzonation and Ground Response Analysis (GRA) studies. Among various geotechnical parameters, shear-wave velocity (Vs) of the top 30 m subsurface is mainly linked to seismic site characterization and amplification studies. The average shear-wave velocity of the top 30 m subsurface, Vs(30), has been used for seismic site classification in accordance with the National Earthquake Hazard Reduction Program (NEHRP) and various building codes. In this study, an attempt has been made to retrieve the geospatial variation of average shear-wave velocity for Coimbatore city using the active Multi-channel Analysis of Surface Waves (MASW) test which is one of the non-destructive geophysical tests. To retrieve the spatial distribution of shear-wave velocity (Vs), the test was carried out at 35 locations in the vicinity of important structures, schools, colleges, and hospitals within the city. The seismic records have been acquired in the field and analyzed using the winMASW software. From the one-dimensional MASW test, the study area has an average Vs(30) in the range of 640 m/s to 909 m/s and has been classified as site-class “BC” (soft rock) according to NEHRP standards. These test results have been validated using the collected SPT bore log data from various locations, including 40 sites in the vicinity of the conducted MASW tests. The site-specific correlation between the shear-wave velocity (Vs) and the corrected SPT N- Value, N1(60), and between Vs and shear modulus (G) have been developed for Coimbatore city with a regression coefficient of 0.79 and 0.83 respectively. From the fundamental site period map, the study area has a site period in the range of 0.1 to 0.2 s, which indicates that 1- to 2- storey buildings that are densely distributed throughout the city may lead to damage in case of probable future earthquakes. This study bridges the connectivity from the evaluated bedrock acceleration using the Seismic Hazard Analysis (SHA) and provides insights for evaluating surface acceleration using GRA studies.

Probing the evolution of fault properties during the seismic cycle with deep learning

We use seismic waves that pass through the hypocentral region of the 2016 M6.5 Norcia earthquake together with Deep Learning (DL) to distinguish between foreshocks, aftershocks and time-to-failure (TTF). Binary and N-class models defined by TTF correctly identify seismograms in test with > 90% accuracy. We use raw seismic records as input to a 7 layer CNN model to perform the classification. Here we show that DL models successfully distinguish seismic waves pre/post mainshock in accord with lab and theoretical expectations of progressive changes in crack density prior to abrupt change at failure and gradual postseismic recovery. Performance is lower for band-pass filtered seismograms (below 10 Hz) suggesting that DL models learn from the evolution of subtle changes in elastic wave attenuation. Tests to verify that our results indeed provide a proxy for fault properties included DL models trained with the wrong mainshock time and those using seismic waves far from the Norcia mainshock; both show degraded performance. Our results demonstrate that DL models have the potential to track the evolution of fault zone properties during the seismic cycle. If this result is generalizable it could improve earthquake early warning and seismic hazard analysis.

A Monte Carlo simulation approach to Aftershock Probabilistic Seismic Hazard Analysis (APSHA): methodology and verification

A Monte Carlo simulation approach to Aftershock Probabilistic Seismic Hazard Analysis (APSHA): methodology and verification

On the use of displaced river terraces to characterize active tectonics of the Zagros orogenic belt, SW Iran

Investigating fault deformation in Late-Quaternary sediments provide valuable data to seismic hazard analysis especially relevant for the urban areas. Integration of displacement of geomorphic markers and ages of Quaternary sediment is needed to measure deformation rates accurately. In order to produce data on intermediate-term deformation rates along the Sabzpoushan Fault Zone (SFZ) situated in the Zagros Mountains of south-west Iran, the optically stimulated luminescence (OSL) dating technique was applied to the tectonically influenced terraces of the Mianrod River passing through the fault zone. Field evidence and satellite imagery revealed that geomorphic markers include strike-slip displacement of terraces, different levels of the fluvial markers, shifting in Bakhtiary Formation, fast change in trend of the river and expansion of erosional gully, deformed horizontally along the Fault Zone. These geomorphic indices suggest that Mianrod River deflected along the SFZ as a consequence of faulting during multiple faulting cycles. The OSL dates obtained from the study area indicate that strike-slip deformation rate along the Sabzpoushan Fault Zone is 1–2.8 ± 0.2 mm y−1 (for ∼100 ka). It is suggestive from the research that a significant part of this estimated rate of deformation results from aseismic crustal deformation, but the areas with the highest likelihood of the Sabzpoushan near-surface deformation, as well as the west parts of the city of Shiraz, should be considered as high seismic hazard areas.

Seismic hazard analysis of China’s islands based on Bayesian network

Seismic hazard analysis of China’s islands based on Bayesian network

A novel physics-constrained neural network: An illustration of ground motion models

Ground motion model (GMM) is essential for seismic hazard analysis and can be developed using either empirical or machine learning approaches. The former often results in suboptimal predictive performance, and the latter frequently faces challenges related to interpretability and explainability although providing more accurate results. To overcome these limitations, this study proposes a novel physics-constrained neural network (PCNN) that incorporates constraints on the hypothesis space through designing specialized neural network architectures informed by physical domain knowledge. This approach enables the updating or derivation of biased or unknown components through data-driven learning. Using the development of GMMs as an illustration, the PCNN is constructed to maintain the mathematical form consistent with established empirical models by reconfiguring parameters, activation functions, and layer connections within a conventional neural network. This physics-constrained approach enhances both the interpretability of the network's architecture and the explainability of its outputs. By leveraging both the advanced machine learning techniques and the domain-specific physical constraints, the PCNN refines the suboptimal coefficients in empirical models, which could achieve the globally optimal model coefficients and improve predictive performance.

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.

Comparative Study of Seismic Hazard Analysis Using Classical PSHA and PSHA Method in Java Island, Indonesia

In general, seismic hazard analysis is conducted using a probabilistic approach (PSHA), whilst seismic risk analysis is computed using a stochastic approach. To figur out more differences between the two methods, a comparative study of those two approaches needs to be conducted. The study was conducted in Java Island which is the most populated island in Indonesia, and prone to earthquakes, particularly in the southern part due to the subduction of the Australian Plate and many active shallow faults along the island. To find out whether the hazards occurred in the risk analysis were closer to the results of Classical PSHA, it is necessary to examine the comparison of the two methods. The difference between the Event-Based and Classical methods is the use of a synthetic catalog that depends on the input value of SES (Stochastic Event Set). The research began with hazard computation with both methods, where the Event-Based PSHA method was given varying SES values. Determining the SES value in the Event-Based method is an important stage to conduct as a significant input parameter in a risk analysis, particularly the loss analysis. The comparison results conclude that the Event-Based PSHA with SES= 200,000 produced a wellconfirmed hazard map compared to the Classical PSHA result with the smallest standard deviation and variation, i.e. 0.0172 and 0.0003 respectively.

Development of a 3D Geologic Model Used in the Seismic Hazard and Liquefaction Hazard Analysis of Madison County, Tennessee

ABSTRACT Madison County of western Tennessee is approximately 100 km southeast of the New Madrid seismic zone and, thus, is subject to potentially dangerous seismic shaking and liquefaction events. Holocene floodplain alluvium and associated Pliocene/Pleistocene terraces of the Forked Deer and Hatchie Rivers cover a large part of Madison County. Underlying these unconsolidated sediments are approximately ∼650 m of poorly consolidated Paleogene and Cretaceous terrestrial and marine sediments, which are underlain by Paleozoic limestone bedrock. Uniform regional geology used in the 2014 United States Geological Survey seismic hazard maps assumes a homogenous seismic velocity structure above bedrock. Three-dimensional geologic mapping of the ∼700 m of Quaternary, Neogene, Paleogene, and Cretaceous sediments conducted in this study provides a more detailed velocity structure, resulting in a more accurate and detailed map of the expected ground motions in Madison County during future earthquakes. In addition, the surface geologic map reveals regions that are susceptible to liquefaction. This paper discusses the methods used to construct the surface geology map, subsurface structure contour maps, isopach maps, and 3D geologic model used in the seismic hazard and liquefaction hazard analyses of Madison County, Tennessee, and summaries of the results of these analyses are presented.

Analyzing the dependency of earthquake insurance premium on the intensity measure used in probabilistic seismic hazard analysis and fragility curves

Considering the uncertainties associated with earthquake, seismic response of structures, damage caused by the response and the repair costs, determining Earthquake Insurance Premium (EIP) is a challenging task that requires a framework for modeling all inherent risks. Most of the models proposed for EIP determination have two basic components. The first component indicates the probability of seismic hazard (generally obtained from the probabilistic seismic hazard analysis), and the second component indicates the probability of occurrence of a given damage to the structure with respect to different seismic hazard levels (generally obtained from the fragility curves). These two components are interconnected through an intermediate parameter, known as the Intensity Measure (IM), which is of great importance in calculations and has hardly been considered so far. In this study, the dependency of EIP on the IM used in the probabilistic seismic hazard analysis and the fragility curves is analyzed. To this end, the EIP was determined for five types of buildings at low, medium and high seismic hazard levels using two IMs (peak ground acceleration and first mode spectral acceleration). The results of this study warn that the EIP can be highly dependent on the IM, so that for all studied structures, the EIP determined based on peak ground acceleration at a high seismic hazard level is nearly three times as many as the one determined by the first mode spectral acceleration. A significant finding is that when the first mode spectral acceleration is used as the IM, the ratio of EIP at different seismic hazard levels closely matches the ratio of IM at the same levels. This result can be valuable in developing insurance codes and regulations. However, this is not the case for the currently used IM, i.e., peak ground acceleration.

Site-specific design response spectra proposed for Sarcheshmeh Copper Mine in Iran and seismic hazard analysis

Site-specific design response spectra proposed for Sarcheshmeh Copper Mine in Iran and seismic hazard analysis

Correlation models for next-generation amplitude and cumulative intensity measures using artificial neural networks

Various next-generation intensity measures (IMs) have emerged in recent years. For instance, filtered incremental velocity, FIV3, was shown to be efficient in predicting the collapse of structures, or average spectral acceleration, Saavg, shown to be well-correlated with a wide range of response, from initiation of structural damage yielding to structural collapse. Different ground motion models (GMMs) are available to predict the probabilistic distribution of these IMs, given a set of seismological parameters. Nonetheless, a few key correlation models are still missing in the literature, which are elemental for a more holistic approach to ground motion record selection. These models are vital when performing seismic hazard analysis and ground motion record selection using state-of-the-art approaches like the conditional spectrum or generalized conditional intensity measure (GCIM) methods. This study uses a single machine-learning-based generalized ground motion model (GGMM) to estimate all the IMs. The model is based on the Next-Generation Attenuation (NGA)-West2 database and was utilized to quantify the correlations between the residuals. Correlations were calculated for intra- and inter-event residuals, but only those for total residuals are presented here, given their utility. Since the calculation of residuals comes from the same GMM and the same database of ground motions, this produces more consistent correlation coefficients between all IMs. To facilitate the usage of these correlation coefficients, predictive models of the empirical data were developed using machine-learning-based techniques, namely artificial neural networks (ANNs). It was found that FIV3 is strongly correlated with Sa(~1 s) and itself across all periods and has a weak negative correlation with duration at short periods and near-zero correlation for longer periods. Also, a stronger negative correlation between Sa and significant duration was found, compared with other prominent existing models. Direct correlation models between Sa and Saavg are also proposed.

The maximum magnitude of natural and induced earthquakes

A key element in the assessment of seismic hazard is estimation of the maximum possible earthquake magnitude, Mmax. A great deal of effort has been invested in developing approaches to estimate Mmax for natural (tectonic) earthquakes, especially in regions of relatively low seismicity where it is difficult to associate observed seismicity with known geological faults. In probabilistic seismic hazard analysis, there has been a tendency to assign a narrow range of large values to Mmax. This results in the impression that hazard results are insensitive to this parameter, which is not the case when the Mmax distribution captures the full range of possible values. For induced seismicity, Mmax estimates can have far-reaching implications both in terms of quantitative assessments of the resulting seismic hazard and risk, and in terms of the public and regulatory perception of this risk. Estimates of Mmax for induced seismicity need to distinguish between driven earthquakes, for which magnitudes are largely controlled by operational parameters, and triggered tectonic earthquakes, together with estimates of the likelihood of such triggering. Distributions of triggered Mmax may be limited to smaller magnitudes than distributions for natural seismicity due to the shallow depth of most injection/extraction wells. For the management of induced seismic risk, the expected largest event magnitude (which may be influenced by a Traffic Light Scheme in operation) may be more relevant than any physical upper bound truncating the recurrence relationship.

Novel application of unsupervised machine learning for characterization of subsurface seismicity, tectonic dynamics and stress distribution

Our study pioneers an innovative use of unsupervised machine learning, a powerful tool for navigating unclassified data, to unravel the complexities of subsurface seismic activities and extract meaningful patterns. Our central objective is to comprehensively characterize seismicity within an active region by identifying distinct seismic clusters in spatial distribution, thereby gaining a deeper understanding of subsurface stress distribution and tectonic dynamics. Employing a diverse range of clustering algorithms, with particular emphasis on Fuzzy C-Means (FCM), our research meticulously dissects the intricate physical processes that govern a complex tectonic zone. This technique effectively delineates distinct tectonic zones, aligning seamlessly with established seismological knowledge and underscoring the transformative potential of Artificial Intelligence (AI) in analyzing regional subsurface phenomena, even under conditions of data scarcity. Moreover, associating earthquakes with specific seismogenic structures significantly enhances seismic hazard analyses, potentially paving the way for autonomous insights that inform engineering hazard assessments.

Simplified site response analysis for regional seismic risk assessments

Seismic risk assessment on a regional scale requires an estimation of ground motion intensity and its spatial variation over large geographical areas. This task is particularly challenging in the presence of very soft soil deposits where significant amplification occurs at specific frequencies that are often referred to as local resonances. It is often not feasible to conduct a nonlinear site response analysis at thousands of sites of interest across a region such as an urban area due to the computational effort involved and the required detailed information on soil profiles at each site. Thus, in regional seismic risk analyses the hazard is typically represented by a field of response spectral ordinates estimated by ground motion models. This study proposes a simplified site response analysis procedure to improve the characterization of spectral ordinates to be used in regional seismic risk assessments. In the proposed procedure, reduced-order models are used to conduct site response analysis using very few parameters to transform response spectra at rock outcrop into response spectra at the surface of each of the sites. A particular emphasis is given to soft-soil sites characterized by low shear-wave velocities overlaid on rock or firm soils. A non-uniform continuous shear beam model with a parabolic variation of shear modulus along the depth and modal damping is used in combination with Random Vibration Theory. It is shown that the proposed approach provides better estimates than those of ground motion models or physics-based ground motion simulation models which often cannot capture the local resonances. Site-specific validation of the shear beam model and overall simplified site response analysis is performed at four soft-soil sites in San Francisco, California. Despite its simplicity, it is shown that the proposed procedure adequately captures the amplitudes and spectral shapes of the motions. It is also shown that in conjunction with ground motion models or physics-based models, the proposed simplified site response analysis procedure can be used to efficiently simulate the seismic hazard on a regional scale through an example of a regional seismic hazard analysis of 160 softer soil sites in Downtown San Francisco during the Loma Prieta earthquake.

A Methodologic Approach to Study Large and Complex Landslides: An Application in Central Apennines

The evaluation of landslide hazards in seismic areas is based on a deterministic analysis, which is unable to account for various uncertainties in the analysis process. This paper focuses on the probabilistic local seismic hazard analysis and extends the results to the landslide hazard analysis to consider both the uncertainties of the ground deformations and the strengths. The work studies the areas between Nibbiano and Sant’Erasmo hamlets in the Camerino municipality located in central Italy, where all constructions present evidence of damage caused by both the seismic sequence of 2016–2017 and the slope instability. An exhaustive geological and geophysical investigation has clarified the geological, geomorphological, and hydrogeological characteristics of the area, enabling a new characterization of material stress-strain behaviour. The study reveals that the low stiffness of the debris covers, and their fair degree of permeability contribute to potential instability scenarios triggered by both intense rainfall and the effects of strong earthquakes. The goal was to utilize the results to support local urban planning because in-depth knowledge of the possible evolutionary scenarios of the slopes is fundamental to the management of the degree of danger for structures, especially for people. Moreover, it was shown once again how a multi-source approach, with different investigation techniques, cannot be ignored for the study of the evolution of complex landslides.

Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone

Abstract Based on probabilistic seismic hazard analysis, the seismic landslide hazard research considers the spatial and temporal distribution characteristics of seismic peak ground acceleration, which integrates the factors such as seismic intensity, location, and recurrence time. The occurrence of future earthquakes has certain randomness. This article presents the landslide hazard zoning of the eastern Kunlun fault zone and its surrounding faults, which is carried out under the action of horizontal ground motion with certain exceeding probability. According to the geological structure and seismicity characteristics of the study area, the potential source is divided. Based on the seismic hazard analysis and Newmark cumulative displacement evaluation model, the seismic landslide hazard in the study area is analyzed. The landslide probability is taken as the risk index. The seismic landslide hazard can be divided into five grades: extremely low-prone area, low-prone area, medium-prone area, high-prone area, and extremely high-prone area. In the results of seismic landslide risk zoning given in this article, the surrounding areas of Tazang fault and Minjiang fault are high-risk areas, which should be paid attention to.