Earthquake Probability Research Articles

Effect of permanent stress on the time-dependent earthquake probability: implication for seismic hazard assessment in the African continent

SUMMARY Earthquake hazard assessment is the first step towards implementing prevention, preparedness and response or faster recovery actions to reduce the risk of seismic disasters. In this paper, we present a comprehensive study on present-day seismicity in terms of the estimated waiting time and conditional probability in Africa by 2022–2072 using four competing distribution models (Brownian passage time (BPT), gamma, lognormal and Weibull). We also investigate how much Coulomb stress change $\ ( {\Delta {\rm CFF}} )$ induced by previous earthquakes that occurred in neighbouring active sources should revise the probability of occurrence at the location of the next events. We analyse large earthquakes with moment magnitude, ${M}_{\rm w} \ge 6.0$, collating data from the Global Centroid Moment Tensor and from several published literature that list fault plane solutions of large African earthquakes since 1900. We assume that the data set is stationary and consists of independent events. First, the model's parameters are estimated and the results of the statistical analysis of the interevent times show clear evidence of quasi-periodic recurrence behaviour for large earthquakes ${M}_{\rm w} \ge $ 6.0 in different seismotectonic regions in Africa. Next, a comparison among the distribution models is performed with the aim of selecting the most suitable one. The results in terms of the maximum likelihood criterion and its extension (Akaike Information Criterion) indicate that, in general, the BPT and Weibull models had similar fits to each other, and performed slightly better than gamma and lognormal models. Then, we use Gaussian random distributions to treat parameter uncertainties (e.g. aperiodicity, maximum expected magnitude, slip rate and mean recurrence time) of the distribution models associated with each seismotectonic region. From repeated Monte Carlo draws, we assess uncertainties of the 50-yr conditional probability values for the next earthquake obtained from two distribution models (BPT and BPT + $\Delta {\rm CFF}$) related to the 50th percentile. The results of the BPT distribution indicate very high chances of future earthquakes in the study region where the conditional probability of a large earthquake reaches 99.5, 95.6, 83.1 and 82.2 per cent for the western branch of the East African Rift System (EARS), northwest Africa, the Afar region, and the eastern branch of EARS, respectively. Taking into account the effect of stress change from interacting sources (BPT + $\Delta {\rm CFF}$), these probabilities are slightly modified to 99.8, 98.4, 89.9 and 87.3 per cent for the western branch of EARS, northwest Africa, the Afar region and the eastern branch of EARS, respectively. These marginal increases suggest that the estimated effect of the earthquake interaction introduced by the coseismic slip of previous earthquakes on neighbouring active sources is minor if compared with the uncertainties affecting the renewal models used for the basic time-dependent conditional probability assessment.

Climatic and seismic data-driven deep learning model for earthquake magnitude prediction

The effects of global warming are felt not only in the Earth’s climate but also in the geology of the planet. Modest variations in stress and pore-fluid pressure brought on by temperature variations, precipitation, air pressure, and snow coverage are hypothesized to influence seismicity on local and regional scales. Earthquakes can be anticipated by intelligently evaluating historical climatic datasets and earthquake catalogs that have been collected all over the world. This study attempts to predict the magnitude of the next probable earthquake by evaluating climate data along with eight mathematically calculated seismic parameters. Global temperature has been selected as the only climatic variable for this research, as it substantially affects the planet’s ecosystem and civilization. Three popular deep neural network models, namely, long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and transformer models, were used to predict the magnitude of the next earthquakes in three seismic regions: Japan, Indonesia, and the Hindu-Kush Karakoram Himalayan (HKKH) region. Several well-known metrics, such as the mean absolute error (MAE), mean squared error (MSE), log-cosh loss, and mean squared logarithmic error (MSLE), have been used to analyse these models. All models eventually settle on a small value for these cost functions, demonstrating the accuracy of these models in predicting earthquake magnitudes. These approaches produce significant and encouraging results when used to predict earthquake magnitude at diverse places, opening the way for the ultimate robust prediction mechanism that has not yet been created.

Kentsel dirençlilikte kırılganlık analizine bir yaklaşım önerisi

This study proposes a spatial multicriteria analysis that can be implemented by the micro zonation technique for any city for the assessment of the vulnerability to the physical environment. Niğde, as the case study, recently had pecuniary damages after a medium-sized earthquake. Although it is not located in the earthquake zone, its damage made it necessary to analyze the vulnerability of the city center, where the population density is the highest. This proposed approach aims that the interventions needed to increase urban resilience in a practical way conducted by the institutions responsible for urban planning so that the damage is reduced in a probable earthquake. According to the results obtained, areas with high vulnerability and areas that need urgent intervention to be made resilient were identified. The population living in the areas that need urgent intervention was determined to constitute approximately one-fifth of the total population of the province.

Utilization of Crowdsourced Felt Reports to Distinguish High-Impact from Low-Impact Earthquakes Globally within Minutes of an Event

Abstract Rapid assessment of an earthquake’s impact on the affected society is a crucial step in the early phase of disaster management, navigating the need for further emergency response measures. We demonstrate that felt reports collected via the LastQuake service of the European Mediterranean Seismological Center can be utilized to rapidly estimate the probability of a felt earthquake being high impact rather than low impact on a global scale. Our data-driven, transparent, and reproducible method utilizing Bayes’ theorem and kernel density estimation provides results within 10 min for 393 felt events in 2021. Although a separation of high- and low-impact events remains challenging, the correct and unambiguous assessment of a large portion of low-impact events is a key strength of our approach. We consider our method as an inexpensive addition to the pool of earthquake impact assessment tools, one that is fully independent of seismic data and can be utilized in many populated areas on the planet. Although practical deployment of our method remains an open task, we demonstrate the potential to improve disaster management in regions that currently lack expensive seismic instrumentation.

Probabilistic Analysis of the Tsunami Disaster on the Vulnerability Level of Buildings in Painan City, West Sumatra based on the Earthquake Ratio with the Logic Tree Method

Indonesia is an archipelagic country stretching from Sabang to Merauke and is located at the convergence of the most complex and active tectonic plates in the world, namely Eurasia, Indo-Australia, and the Pacific. One of the regions with a high probability of earthquakes and high tsunamis is the island of Sumatra, which lies between the Eurasian and Indo-Australian plates. Painan city is located in West Sumatra Province, where it is surrounded by three megathrust zones: the Nias-Simeulue segment, Mentawai-Siberut segment, and Mentawai-Pagai segment. These three megathrust zones, namely Nias-Simeulue with an estimated earthquake magnitude of 8.7 Mw, Mentawai-Siberut with an earthquake magnitude of 8.9 Mw, and Mentawai-Pagai with an earthquake magnitude of 8.9 Mw, can potentially cause tsunamis that may reach Painan city. The aim of this study is to investigate the influence of seismic activity level variables and the ratio between large and small earthquakes on tsunami wave height using the logic tree method, assess the tsunami hazard potential using Probabilistic Tsunami Hazard Assessment (PTHA), and evaluate the vulnerability of existing buildings in Painan city. Tsunami simulations in this study were conducted using the Cornell Multigrid Coupled Tsunami (COMCOT) program, which applies the Shallow Water Equation (SWE). Additionally, the Building Tsunami Vulnerability (BTV) equation was used to calculate the vulnerability index of buildings based on their conditions and tsunami wave heights. The calculation of the BTV value for the tsunami height parameter was modified using fragility curves that depict the relationship between force and the probability of tsunami wave damage. From the simulation results, the tsunami height was obtained, which in turn determines the probability of tsunami hazard on buildings with return periods of 1000 and 4000 years. After the simulations, the Building Tsunami Vulnerability (BTV) calculation was performed to determine the vulnerability level of buildings to tsunamis.

A More Realistic Earthquake Probability Model Using Long-Term Fault Memory

ABSTRACTForecasts of the probability of a large earthquake occurring on a fault during a specific time interval assume that a probability distribution describes the interevent times between large earthquakes. However, current models have features that we consider unrealistic. In these models, earthquake probabilities remain constant or even decrease after the expected mean recurrence interval, implying that additional accumulated strain does not make an earthquake more likely. Moreover, these models assume that large earthquakes release all accumulated strain, despite evidence for partial strain release in earthquake histories showing clusters and gaps. As an alternative, we derive the necessary equations to calculate earthquake probabilities using the long-term fault memory (LTFM) model. By accounting for partial strain release, LTFM incorporates the specific timing of past earthquakes, which commonly used probability models cannot do, so it can forecast gaps and clusters. We apply LTFM to the southern San Andreas fault as an example and show how LTFM can produce better forecasts when clusters and gaps are present. LTFM better forecasts the exceptionally short interevent time before the 1857 Fort Tejon earthquake. Although LTFM is more complex than existing models, it is more powerful because (unlike current models) it incorporates fundamental aspects of the strain accumulation and release processes causing earthquakes.

Multi-Resolution Grids in Earthquake Forecasting: The Quadtree Approach

ABSTRACTThe Collaboratory for the Study of Earthquake Predictability (CSEP) is an international effort to evaluate probabilistic earthquake forecasting models. CSEP provides the cyberinfrastructure and testing methods needed to evaluate earthquake forecasts. The most common way to represent a probabilistic earthquake forecast involves specifying the average rate of earthquakes within discrete spatial cells, subdivided into magnitude bins. Typically, the spatial component uses a single-resolution Cartesian grid with spatial cell dimensions of 0.1° × 0.1° in latitude and longitude, leading to 6.48 million spatial cells for the global testing region. However, the quantity of data (e.g., number of earthquakes) available to generate and test a forecast model is usually several orders of magnitude less than the millions of spatial cells, leading to a huge disparity in the number of earthquakes and the number of cells in the grid. In this study, we propose the Quadtree to create multi-resolution grid, locally adjusted mirroring the available data for forecast generation and testing, thus providing a data-driven resolution of forecasts. The Quadtree is a hierarchical tree-based data structure used in combination with the Mercator projection to generate spatial grids. It is easy to implement and has numerous scientific and technological applications. To facilitate its application to end users, we integrated codes handling Quadtrees into pyCSEP, an open-source Python package containing tools for evaluating earthquake forecasts. Using a sample model, we demonstrate how forecast model generation can be improved significantly in terms of information gain if constrained on a multi-resolution grid instead of a high-resolution uniform grid. In addition, we demonstrate that multi-resolution Quadtree grids lead to reduced computational costs. Thus, we anitcipate that Quadtree grids will be useful for developing and evaluating earthquake forecasts.

Aligning sustainability and regional earthquake hazard mitigation planning: integrating greenhouse gas emissions and vertical equity

Concerns about the potential economic consequences of earthquakes have increased in recent years as scientifically based probabilities of future earthquakes in many large urban areas have risen. These hazards disproportionately impact low-income communities as wealth disparities limit their capacity to prepare and recover from potentially disastrous events. In addition to major economic losses, the activities related to building recovery result in significant greenhouse gas emissions contributing to climate change. This article develops a framework that quantifies the complex relationships between pre-earthquake retrofit activities and their economic, environmental and equity implications to promote informed decision-making, using the city of San Francisco, California as a case study. This research consists of two sections. In the first section, a bi-objective optimization model is proposed to identify optimal earthquake risk mitigation policies to minimize total earthquake-related economic and environmental costs, simultaneously. Decisions entail the seismic retrofit, combined seismic and energy retrofit or complete reconstruction of building-type groups. The benefits of increased energy efficiency of the upgraded buildings are incorporated to evaluate decisions from a holistic perspective. In the second section, the model is extended to address the issue of inequitable budget allocation from a public-sector perspective. Vertical equity considerations are incorporated as an optimization constraint to distribute available resources aiming to limit the discrepancy of expected losses as a fraction of income between households across income groups. The tradeoff between equity and economic efficiency is explored. Results show that life-cycle environmental impacts constitute an informative performance metric to regional risk mitigation decision-makers, in addition to the more customarily used monetary losses. Although construction costs primarily dictate optimal decisions from an economic perspective, energy considerations largely impact optimal decisions from an environmental perspective.

Fractal Dimension Analysis and Earthquake Repeated Period Estimation in the Southern Part of Sumatra Subduction Zone (Bengkulu-Lampung-Sunda Segment)

DOI:10.17014/ijog.9.3.383-394An earthquake seismicity parameter study has been conducted in the southern part of the Sumatra Subduction Zone (SSZ) for the period of 1919 to 2019 with a hypocentre depth of 120 km and magnitude of ≥4Mw. The researched area is divided into three segments, Bengkulu, Lampung, and Sunda segments, respectively. This study aims to evaluate the seismicity parameters in the form of a-value, b-value, fractal dimension, and probabilities of earthquake recurrence period. Frequency-magnitude correlation statistical approach introduced by Gutenberg-Richter was used to calculate the seismicity parameters based on catalogue data obtained from USGS. An a-value variation of 5.11±1.84 to 5.85±2.46 obtained indicates the most dominant seismic intensity. b-value of 0.69±0,.17 to 0.83±0.35 correlates with high-stress levels, the level of spatial heterogeneity on the SFZ, and the probabilities of bigger earthquakes in the future. The calculation of fractal dimension of 1.4±0.33 to 1.7±0.71 indicates an earthquake distribution pattern was caused by a single fracture zone along the southern part of SSZ. The fracture zone is located on the left and right sides of the southern part of SSZ, which may not have been connected. In addition, the calculation result of earthquake recurrence period with magnitude ≥6.5Mw indicates that there will be an 5 - 6 earthquake event in the Bengkulu segment, 2 - 3 event in the Lampung segment, and 1 - 2 event in the Sunda segment, while the probabilities of an earthquake with magnitude ≥7.5Mw along the southern part of SSZ is 1 - 2 times.

Seismic Damage Index Spectra Considering Site Acceleration Records: The Case Study of a Historical School in Kermanshah

The frequency content and time duration of earthquakes are as effective as the peak ground acceleration on structural damage. Therefore, using rapid seismic vulnerability assessment methods that consider the earthquake acceleration time history is noticeable. Kermanshah is a historical city that is generally affected by far-field earthquakes. Therefore, it is necessary to consider the effect of the low-frequency shocks in evaluating the vulnerability of buildings in this city. Herein, a historic school in Kermanshah is assumed as a case study and two well-known damage index formulas are used for determining the damage index spectra of this structure, considered as a single degree of freedom system. Then, the effective parameters of the damage index, including ductility, relative degradation of stiffness, and dissipated energy are determined from a nonlinear analysis of the structure under the effect of the most probable earthquake acceleration records. Finally, the damage index spectra can be used for rapid seismic vulnerability assessment of masonry buildings on similar sites with various fundamental periods for large-scale assessments. The result shows that the building tends to collapse at a peak ground acceleration of 0.15 g. Furthermore, results confirm the seismic resistance reduction effect of flexible floors.

A new method to promptly evaluate spatial earthquake probability mapping using an explainable artificial intelligence (XAI) model

Machine learning (ML) models have been extensively used in several geological applications. Owing to the increase in model complexity, interpreting the outputs becomes quite challenging. Shapley additive explanation (SHAP) measures the importance of each input attribute on the model’s output. This study implemented SHAP to estimate earthquake probability using two different types of ML approaches, namely, artificial neural network (ANN) and random forest (RF). The two algorithms were first compared to evaluate the importance and effect of the factors. SHAP was then carried out to interpret the output of the models designed for the earthquake probability. This study aims not only to achieve high accuracy in probability estimation but also to rank the input parameters and select appropriate features for classification. SHAP was tested on earthquake probability assessment using eight factors for the Indian subcontinent. The models obtained an overall accuracy of 96 % for ANN and 98 % for RF. SHAP identified the high contributing factors as epicenter distance, depth density, intensity variation, and magnitude density in a sequential order for ANN. Finally, the authors argued that an explainable artificial intelligence (AI) model can help in earthquake probability estimation, which then open avenues to building a transferable AI model.

Revisiting b-value for extended Kopili region of Northeast India and probabilistic estimation of seismic hazard attributes thereof

Kopili fault and its' surroundings of northeast India have remained witness to some devastating earthquakes. The complex tectonic feature of this region incurs a high seismicity rate relative to other regions. Very recently, an earthquake of magnitude 6.4 (MW) has jolted this region. Considering the persistently rising seismicity pattern of this region, the present study intends to integrate the frequency magnitude distribution of earthquakes with other pertinent seismic hazard parameters. This work presents the estimation of the return period and probability of non-exceedance of medium to large earthquakes for the Kopili fault and its' surroundings via the extreme value method. Two statistical methods, namely the Gutenberg-Richter(G-R) power law and Gumbel extreme value method (GEVM), are applied for the correct estimation of the b-value for the Kopili fault and its' surrounding region. The uniform and homogenous magnitude scale dataset consisting of 550 events observed within a limit of 350 km from the Kopili fault (1964 to 2022) are used in the study. The b-value computed using GR relation was found to be unsatisfactory (0.73) due to an inadequate dataset—thus Gumbel's extreme value method is used for the estimation of the b-value (0.97). Gumbel's extreme value method is used for the calculation of the probability of occurrence of earthquakes, return period, and the most probable earthquake magnitude for the study region. The most probable annual earthquake is found to be ∼ MW 4.99. The return period and probability of occurrence of design-based earthquakes are also calculated. The correlation between b-value, seismic moment, and fractal dimension for seismic hazard analysis is also investigated. The antithetical relationship between seismic moment and b-value is observed for the region while the deep-root character of the Kopili fault and the strong, unfractured character of the Shillong plateau is highlighted with the spatial distribution of fractal dimension in the study region.

Pseudo-prospective testing of 5-year earthquake forecasts for California using inlabru

Abstract. Probabilistic earthquake forecasts estimate the likelihood of future earthquakes within a specified time-space-magnitude window and are important because they inform planning of hazard mitigation activities on different time scales. The spatial component of such forecasts, expressed as seismicity models, generally relies upon some combination of past event locations and underlying factors which might affect spatial intensity, such as strain rate, fault location and slip rate or past seismicity. For the first time, we extend previously reported spatial seismicity models, generated using the open source inlabru package, to time-independent earthquake forecasts using California as a case study. The inlabru approach allows the rapid evaluation of point process models which integrate different spatial datasets. We explore how well various candidate forecasts perform compared to observed activity over three contiguous 5-year time periods using the same training window for the input seismicity data. In each case we compare models constructed from both full and declustered earthquake catalogues. In doing this, we compare the use of synthetic catalogue forecasts to the more widely used grid-based approach of previous forecast testing experiments. The simulated catalogue approach uses the full model posteriors to create Bayesian earthquake forecasts, not just the mean. We show that simulated catalogue based forecasts perform better than the grid-based equivalents due to (a) their ability to capture more uncertainty in the model components and (b) the associated relaxation of the Poisson assumption in testing. We demonstrate that the inlabru models perform well overall over various time periods: The full catalogue models perform favourably in the first testing period (2006–2011) while the declustered catalogue models perform better in the 2011–2016 testing period, with both sets of models performing less well in the most recent (2016–2021) testing period. Together, these findings demonstrate a significant improvement in earthquake forecasting is possible although this has yet to be tested and proven in true prospective mode.

Seismic probabilistic risk estimation of Japanese railway embankments and risk-based design strength of soil and reinforcement

For the rational seismic design of railway embankments considering seismic risk, high-strength geomaterials and reinforcements should be used in areas with high seismic probability, and low-strength geomaterials and reinforcements should be used in areas with low seismic probability. In this study, a seismic probabilistic risk estimation of Japanese railway embankments was conducted using publicly available information on seismic hazards in Japan and previously estimated seismic-fragility curves for Japanese railway embankments. To determine the required average friction angle of backfill soil and average reinforcement tensile strength for satisfying the prescribed allowable seismic risk, a sensitivity analysis of the railway embankment was conducted by changing the average friction angle of the backfill soil and the average reinforcement tensile strength. Using the sensitivity-analysis results, the required average friction angle of the backfill soil and average reinforcement tensile strength of the railway embankment satisfying the prescribed allowable seismic risk considering the occurrence probability of earthquakes were analytically obtained.

Evaluación de la vulnerabilidad sísmica del puente de carretera "Moche" ubicado en la carretera nacional pe1n usando el método de espectro de capacidad

In active seismic zones, such as the Peruvian coast, tools for predicting the potential negative impacts of earthquakes are essential for planning mitigation, emergency, and recovery of transportation facilities. For instance, the capacity of a road bridge network after a seismic event to carry traffic flow depends on the degree of expected damage and the related repair costs and downtime. This paper determines the degree of vulnerability of highway bridges to the action of probable earthquakes during the structure's lifetime by applying the capacity spectrum assessment method known as FRACAS (FRAgility through CApacity Spectrum assessment). This approach allows fragility curves to be obtained from analyzing a structure subjected to a series of seismic records of different characteristics. In this way, the method can explain the effect of variability in seismic demand and structural characteristics on the damage statistics simulated for the type of structure and evaluate the associated uncertainty in predicting fragility. As a case study, it is applied the methodology described in the evaluation of the Moche bridge, located in the region of La Libertad (Peruvian coast), which is in an Operational damage state according to an in situ inspection. From the seismic vulnerability assessment, it is obtained that until earthquakes with accelerations of 1.7g, the Moche bridge has a 60% probability of incursion into a Life Safety damage state, being the probability of incursion into a Collapse damage state less than 10%. This methodology is recommended to determine the reliability of bridges, considering the cumulative damage due to probable seismic events during the bridges' lifetime.

Quantifying the Statistical Relationships Between Flank Eruptions and Major Earthquakes at Mt. Etna Volcano (Italy)

AbstractAt Mt. Etna volcano, flank eruptions are often accompanied by seismic swarms with damaging earthquakes; the most recent case‐history is the 2018 flank eruption, associated with a destructive earthquake (Mw 5.0). In this paper, we analyze the earthquake and eruptive catalogs from 1800 to 2018, to produce quantitative estimates of the earthquake rate under the influence of flank eruptions. We quantify that 30% of the flank eruption onsets precede a major (i.e., damaging, Ix ≥ V–VI EMS) earthquake by 30 days or less; 18% of the major earthquakes follow a flank eruption onset in 30 days or less. Thus, we show that the probability of major earthquakes increases 5–10 times after the onset of flank eruptions and this effect lasts for 30–45 days. This is also observed after the end of the eruptions. Results indicate different relationships depending on the location of the volcano‐tectonic systems considered individually (eruptive fissures, seismogenic faults). For instance, we describe a 10–20 times increased probability of earthquakes for 65–70 days after eruptions on the northeastern flank, and of new flank eruptions for 45–70 days after earthquakes of the Pernicana fault.

PyCSEP: A Python Toolkit for Earthquake Forecast Developers

Abstract The Collaboratory for the Study of Earthquake Predictability (CSEP) is an open and global community whose mission is to accelerate earthquake predictability research through rigorous testing of probabilistic earthquake forecast models and prediction algorithms. pyCSEP supports this mission by providing open-source implementations of useful tools for evaluating earthquake forecasts. pyCSEP is a Python package that contains the following modules: (1) earthquake catalog access and processing, (2) representations of probabilistic earthquake forecasts, (3) statistical tests for evaluating earthquake forecasts, and (4) visualization routines and various other utilities. Most significantly, pyCSEP contains several statistical tests needed to evaluate earthquake forecasts, which can be forecasts expressed as expected earthquake rates in space–magnitude bins or specified as large sets of simulated catalogs (which includes candidate models for governmental operational earthquake forecasting). To showcase how pyCSEP can be used to evaluate earthquake forecasts, we have provided a reproducibility package that contains all the components required to re-create the figures published in this article. We recommend that interested readers work through the reproducibility package alongside this article. By providing useful tools to earthquake forecast modelers and facilitating an open-source software community, we hope to broaden the impact of the CSEP and further promote earthquake forecasting research.

Stress-based forecasting of induced seismicity with instantaneous earthquake failure functions: Applications to the Groningen gas reservoir

In this study we use the Groningen gas field to test a new method to assess stress changes due to gas extraction and forecast induced seismicity. We take advantage of the detailed knowledge of the reservoir geometry and production history, and of the availability of surface subsidence measurements and high quality seismicity data. The subsurface is represented as a homogeneous isotropic linear poroelastic half-space subject to stress changes in three-dimensional space due to reservoir compaction and pore pressure variations. The reservoir is represented with cuboidal strain volumes. Stress changes within and outside the reservoir are calculated using a convolution with semi-analytical Green functions. The uniaxial compressibility of the reservoir is spatially variable and constrained with surface subsidence data. We calculate stress changes since the onset of gas production. Coulomb stress changes are maximum near the top and bottom of the reservoir where the reservoir is offset by faults. To assess earthquake probability, we use the standard Mohr-Coulomb failure criterion assuming instantaneous nucleation and a non-critical initial stress. The distribution of initial strength excess, the difference between the initial Coulomb stress and the critical Coulomb stress at failure, is treated as a stochastic variable and estimated from the observations and the modelled stress changes. The exponential rise of seismicity nearly 30 years after the onset of production, provides constraints on the distribution of initial strength. The lag and exponential onset of seismicity are well reproduced assuming either a generalized Pareto distribution, which can represent the tail of any distribution, or a Gaussian distribution, to describe both the tail and body of the distribution. The Gaussian distribution allows to test if the induced seismicity at Groningen has transitioned to the steady-state where seismicity rate is proportional to the stressing rate. We find no evidence that the system has reached such a steady-state regime. The modeling framework is computationally efficient making it possible to test the sensitivity to modeling assumptions regarding the estimation of stress changes. The forecast is found robust to uncertainties about the ability of the model to represent accurately the physical processes. It does not require in particular a priori knowledge of the location and orientation of the faults that can be activated. The method presented here is in principle applicable to induced seismicity in any setting provided deformation and seismicity data are available to calibrate the model.

Prediction and validation of short-to-long-term earthquake probabilities in inland Japan using the hierarchical space–time ETAS and space–time Poisson process models

A hierarchical space–time version of the epidemic-type aftershock sequence (HIST–ETAS) model was constructed for an optimally adapted fit to diverse seismicity features characterized by anisotropic clustering as well as regionally distinct parameters. This manuscript validates this elaborate model for short-term prediction based on several years of recent inland Japan earthquakes as a testing data set, by evaluating the results using a log-likelihood ratio score. To consider intermediate- and long-term performance, several types of space–time Poisson models are compared with the background seismicity rate of the HIST–ETAS model. Results show first that the HIST–ETAS model has the best short-term prediction results for earthquakes in the range of magnitudes from M4.0 to M5.0, although, for the larger earthquakes, sufficient recent earthquake data is lacking to evaluate the performance. Second, for intermediate-term predictions, the optimal spatial nonuniform Poisson intensity model has a better forecast performance than the seismic background intensity of the HIST–ETAS model, while the uniform rate Poisson model throughout all of inland Japan has the worst forecast performance. For earthquakes of M6 or larger, the performance of retrospective long-term forecasts was tested in two ways. First, a retrospective forecasting experiment divided the entire period from 1885 to the present into two parts, with the recent ~ 30 years as the forecast period. Second, the historical damaging earthquake data (599–1884) were spatially validated using century data from 1885 to the present. In both validations, it was determined that the spatial intensity of the inland background seismic activity of the HIST–ETAS model is much better than the best-fit nonuniform Poisson spatial model, leading to the best results. The findings of this study will be critical for regional earthquake hazard planning in Japan and similar locations worldwide.Graphical

Seismic assessment of high-rise buildings through dynamic nonlinear analysis

This paper present a seismic assessment of high-rise buildings using dynamic nonlinear analysis. The work touches upon an urgent problem of ensuring the stability of high-rise buildings under seismic load and preventing their destruction. A mathematical model has been built to investigate the effect of seismic activity on high-rise buildings in the Sichuan province (China). Pearson’s test was used to compare the statistical dependence between variables in the study period. Values with a statistical significance of p ≤ 0.05 were considered statistically significant. The probability of earthquakes of 8 magnitude and above was found to be 5% with a frequency of recurrence of 3900 ± 400 yrs, p ≤ 0.001. The paper presents the technique of refractive geophysical research. The seismic reliability index was n = 3.2 at t = 5 s. The standard deviation was 2% and the χ2 statistic was 0.95. The overall (0.59, or 59%) and conditional (0.06, or 6%) risks of failure under seismic load were calculated for high-rise buildings with a service life of 100 years. The coefficients of seismic activity were as follows: 3.203 at the conditional risk level of 5%; 2.97 at the conditional risk level of 10%; and 2.523 at the conditional risk level of 30%.