Prediction Of Peak Ground Acceleration Research Articles

Hybrid Empirical Ground-Motion Models for the Island of Hawaii Based on an Updated Strong Ground-Motion Database

ABSTRACT In this study, we develop ground-motion models (GMMs) for the Island of Hawaii. This area has been the site of several significant earthquake events with a growing database of strong ground-motion observations. Ground-motion modeling on the Island of Hawaii is challenging due to different anelastic attenuation characteristics, the volcanic origin of some of the events, and event depth distribution. Only a few GMMs have been developed for the Island of Hawaii. In this study, we apply a hybrid empirical method (HEM) to develop two separate GMMs for shallow (hypocentral depth ≤ 20 km) and deep (hypocentral depth > 20 km) earthquakes on the Island of Hawaii. We utilize the ratio of the stochastic point-source model in the target and host regions as an appropriate adjustment factor. We apply these adjustment factors to convert the GMMs from the host (western North America) to the target (Island of Hawaii) region. We considered five GMMs proposed in the Next Generation Attenuation Phase 2 project by the Pacific Earthquake Engineering Research Center to model ground motions in the host region. We developed GMMs to predict peak ground acceleration and 5%-damped pseudospectral acceleration at periods T = 0.01–10 s, for moment magnitudes (M) in the range of 3–7.5, and for Joyner–Boore distances in the RJB≤200 km range. The applicability of HEM to develop GMMs for the Island of Hawaii and the growing strong ground-motion data result in further improvements in the capability of GMMs.

Peak ground acceleration prediction for on-site earthquake early warning with deep learning

Rapid and accurate prediction of peak ground acceleration (PGA) is an important basis for determining seismic damage through on-site earthquake early warning (EEW). The current on-site EEW uses the feature parameters of the first arrival P-wave to predict PGA, but the selection of these feature parameters is limited by human experience, which limits the accuracy and timeliness of predicting peak ground acceleration (PGA). Therefore, an end-to-end deep learning model is proposed for predicting PGA (DLPGA) based on convolutional neural networks (CNNs). In DLPGA, the vertical initial arrival 3–6 s seismic wave from a single station is used as input, and PGA is used as output. Features are automatically extracted through a multilayer CNN to achieve rapid PGA prediction. The DLPGA is trained, verified, and tested using Japanese seismic records. It is shown that compared to the widely used peak displacement (Pd) method, the correlation coefficient of DLPGA for predicting PGA has increased by 12–23%, the standard deviation of error has decreased by 22–25%, and the error mean has decreased by 6.92–19.66% with the initial 3–6 s seismic waves. In particular, the accuracy of DLPGA for predicting PGA with the initial 3 s seismic wave is better than that of Pd for predicting PGA with the initial 6 s seismic wave. In addition, using the generalization test of Chilean seismic records, it is found that DLPGA has better generalization ability than Pd, and the accuracy of distinguishing ground motion destructiveness is improved by 35–150%. These results confirm that DLPGA has significant accuracy and timeliness advantages over artificially defined feature parameters in predicting PGA, which can greatly improve the effect of on-site EEW in judging the destructiveness of ground motion.

Peak ground acceleration prediction using supervised machine learning algorithm for the seismically hazardous Kachchh rift zone, Gujarat, India

Peak ground acceleration prediction using supervised machine learning algorithm for the seismically hazardous Kachchh rift zone, Gujarat, India

Real-time prediction of distance and PGA from P-wave features using Gradient Boosting Regressor for on-site earthquake early warning applications

SUMMARY On-site earthquake early warning (EEW) systems represent an important way to reduce seismic hazard. Since these systems are fast in providing an alert and reliable in the prediction of the ground motion intensity at targets, they are particularly suitable in the areas where the seismogenic zones are close to cities and infrastructures, such as Central Italy. In this work, we use Gradient Boosting Regressor (GBR) to predict peak ground acceleration (PGA), and hypocentral distance (D) starting from P-wave features. We use two data sets of waveforms from two seismic sequences in Central Italy: L'Aquila sequence (2009) and the Amatrice–Norcia–Visso sequence (2016–2017), for a total of about 80 000 three-component waveforms. We compute 60 different features related to the physics of the earthquake using three different time windows (1 s, 2 s and 3 s). We validate and train our models using the 2016–2017 data sets (the bigger one) and we test it on the 2009 data set. We study the performances of GBR predicting D and PGA in terms of prediction scores, finding that the models can well predict both targets even using 1 s window, and that, as expected, the results improve using longer time windows. Moreover, we perform a residual analysis on the test set finding that the PGA can be predicted without any bias, while the D prediction presents a correlation with the moment magnitude. In the end, we propose a prototype for a probabilistic on-site EEW system based on the prediction of D and PGA. The proposed system is a threshold-based approach and it releases an alert on four possible levels, from 0 (far and small event) to 3 (close and strong event). The system computes the probability related to each alert level. We test two different set of thresholds: the Felt Alert and the Damage Alert. Furthermore, we consider the lead time (LT) of the PGA to distinguish between useful alerts (positive LT) and Missed Alerts (MA). In the end, we analyse the performance of such a system considering four possible scenarios: Successful Alert (SA), Missed Alert (MA), Overestimated Alert (OA) and Underestimated Alert (UA). We find that the system obtains SA rate about 80 per cent at 1 s, and that it decreases to about 65 per cent due to the increase in MA. This result shows how the proposed system is already reliable at 1 s, which would be a huge advantage for seismic prone regions as Central Italy, an area characterized by moderate-to-large earthquakes (Mw < 7).

Machine learning‐based peak ground acceleration models for structural risk assessment using spatial data analysis

AbstractPredicting peak time‐domain ground‐motion parameters, such as peak ground acceleration (PGA), peak ground velocity, and peak ground displacement at a specific location, is challenging because of the limited number of recorded ground motions and the complexity of ground‐motion prediction equations. This study presents a novel approach that integrates a geographic information system with a spatial data analysis‐based machine learning PGA prediction model to overcome these challenges and predict PGA classes as a function of the PGA of the respective seismic stations, interstation distance of the seismic stations, and time‐average shear‐wave velocity in the upper 30 m of the target station. The proposed spatial data analysis‐based machine learning approach demonstrated the ability to generate satisfactory results in a short period. To account for the spatial dependencies of the variables, a feature selection method for spatial data using mutual information‐based feature selection was proposed, which provides a well‐prepared spatial matrix for machine learning algorithms. This study evaluates the performance of the model using various machine learning algorithms, including Random Forest, Naïve Bayes, K‐Nearest Neighbors, Decision Tree, AdaptiveBoost, Random Undersampling Boost, Extreme Gradient Boost (XGBoost), and Categorical Boost (CatBoost). Among these, XGBoost and CatBoost performed better than the other methods and yielded fairly accurate results. The models were validated using K‐Fold cross‐validation, and the Wilcoxon signed‐rank test was used for comparison. The spatial data analysis‐based machine learning models, particularly XGBoost and CatBoost, achieved high‐accuracy rates in classifying the PGA levels of 99.1% and 98.9%, respectively. Hyperparameters for the XGBoost model were tuned through GridSearchCV. Tree‐based models outperformed parametric models, indicating complex non‐linear spatial relationships, and by combining spatial feature selection with machine learning models demonstrated improved performance. Additionally, real‐time applications of spatial data analysis‐based machine learning PGA prediction models were used to estimate the seismic vulnerability of postulated concrete box‐girder bridges in San Fernando, thus providing insights into damage probabilities based on predicted PGA values.

An Efficient Method for Probability Prediction of Peak Ground Acceleration Using Fourier Amplitude Spectral Model

ABSTRACT The probabilistic prediction of peak ground acceleration (PGA) using the Fourier amplitude spectral (FAS) model has many advantages in regions lacking strong ground-motion records. Currently, the implementation of this approach for the calculation of annual exceedance rate of PGA relies on Monte Carlo simulations (MCSs). However, adopting MCS requires many times calculations of PGA from FAS, and each time of calculation includes complicated integrals, the computational cost is too high to be acceptable for practical applications. Therefore, this study proposes an efficient method for the probabilistic prediction of PGA using the FAS model. For this purpose, a probabilistic analysis method, referred to as the moment method, was introduced to improve computational efficiency. The probability distribution of PGA was approximated using a three-parameter distribution defined according to the first three moments. The first three moments of the PGA were obtained based on the point-estimate and dimension-reduction integration method. Numerical examples were conducted to verify the proposed method. It was found that the proposed method not only performed much more efficiently than using MCS in calculating the annual exceedance rate of PGA to obtain the hazard curve but also provides nearly the same accuracy as MCS.

BTRPP: A Rapid PGA Prediction Model Based on Machine Learning

Peak ground acceleration (PGA) is a critical parameter in the postearthquake intensity snapshot. In this article, we propose a Bagged Tree for Rapid PGA Prediction (BTRPP) based on velocity waveforms recorded at stations around the epicenter, as well as a feature pruning algorithm based on BTRPP to predict PGA and analyze the relationship between PGA and manually predefined features. The research not only aims to discover the best model for forecasting PGA but also examines the impact of features using the interpretability of some models. As inputs to the model, BTRPP uses and mixes features in the time and frequency domains. Bayesian optimization (BO) is used to find the optimum hyperparameters of BTRPP. The results demonstrate that, using the data within 5 s after the first arrival of the P-wave, the BTRPP with BO can reduce the RMSE to 0.2076 and enhance the R-square to 0.91. It is, finally, found that the PGA is most closely related to the average positive amplitude, average negative amplitude, and spectral slope through the feature pruning algorithm in this article, which indicates that the results are plausible.

Earthquakes and Seismic Hazard in Southern New Caledonia, Southwest Pacific

AbstractWe use 12 temporary and 9 permanent broadband seismometers that were operating for ∼400 days from October 2018 to November 2019 to generate the first published earthquake catalog and local magnitude function for southern New Caledonia (SNC). Local hypocenters mostly have depths <20 km, but east of the New Hebrides‐Vanuatu subduction zone they deepen to >100 km. Our local magnitude estimates ML for 107 earthquakes in the subduction zone are consistently 1.1 units smaller than Mw and mb over a range of Mw from 4.5 to 7.5, as determined by the United States Geological Survey. Our catalog has 460 earthquakes with Mw≥3.7 in the subduction zone and the largest event in SNC has ML 3.8. Seismicity rates in SNC are low, but ML> 5 earthquakes are 2.7 times more frequent than elsewhere in the northern Australian plate. The probability of an ML> 5 event in 50 years is 0.6 in SNC. The hazard of damaging seismic shaking in SNC is dominated by local moderate‐magnitude earthquakes, rather than large‐magnitude subduction events. The predicted peak ground acceleration for Nouméa at 10% probability of exceedance in 50 years is 0.08 g. Residual analysis of ground accelerations demonstrates that the hazard for Nouméa from subduction zone earthquakes is currently overestimated and that new regionally specific ground motion prediction equations (GMPEs) are needed. Our results highlight the inadequacy of current GMPEs in subduction zone footwall settings and the need for additional studies of this type of setting.

Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo

The 1992 Cairo earthquake, with a moment magnitude of 5.8, is the most catastrophic earthquake to shock the Greater Cairo (GC) in recent decades. According to the very limited number of seismological stations at that time, the peak ground acceleration (PGA) caused by this event was not recorded. PGA calculation requires identification of nature of the earthquake source, the geologic setting of the path between the source and site under investigation and soil dynamic properties of the site. Soil dynamic properties are acquired by geotechnical investigations and/or geophysical survey. These two methods are costly and are not applicable in regional studies. This study presents an adaptive and reliable PGA prediction model using machine learning (ML) along with six standard geographic information system (GIS) interpolation methods (IDW, Kriging, Natural, Spline, TopoToR, and Trend) to predict the spatial distribution of PGA caused by this event over the GC. The model is employed to estimate the exposure of the urban area and population in the GC based on the available geotechnical and geophysical investigations. The exposure (population) data is from free and easy-access sources, e.g., Landsat images and the Global Human Settlement Population Grid (GHS-POP). The results show that Natural, Spline, and Trend are not suitable GIS interpolation techniques for generating seismic hazard maps (SHMs), while the Kriging Method shows sufficient prediction. Interestingly, with an accuracy of 96%, the ML model outperforms the classical GIS methodologies.

Peak ground acceleration prediction using artificial neural networks for Kachchh, Gujarat, India

The uncertainty in the empirical ground motion prediction models (GMMs) for any region depends on several parameters. In the present work, we apply an artificial neural network (ANN) to design a GMM of peak ground acceleration (PGA) for Kachchh, Gujarat, India, utilizing independent input parameters viz., moment magnitudes, hypocentral distances, focal depths and site proxy (in terms of average seismic shear-wave velocity from the surface to a depth of 30 m (Vs30)). The study has been performed using a PGA dataset consisting of eight engineering seismoscope records of the 2001 Mw7.7 Bhuj earthquake and 237 strong-motion records of 32 significant Bhuj aftershocks of Mw3.3–5.6 (during 2002–2008) with epicentral distances ranging from 1.0 to 288 km. We apply a feed-forward back propagation ANN method with 8 hidden nodes, which is found to be optimal for the selected PGA database and input–output mapping. The standard deviation of the error has been utilized to examine the performance of our model. We also test the ground motion predictability of our ANN model using real recordings of the 2001 Bhuj mainshock, two Mw5.6 Kachchh aftershocks and the 1999 Mw6.4 Chamoli mainshock. The standard deviation of PGA prediction error estimates in log10 units is found to be ± 0.2554. Also, the model predictability of our ANN model suggests a good prediction of the PGA for earthquakes of Mw5.6–7.7, which are occurring in Kachchh, Gujarat, India.

Prediction of Peak Ground Acceleration by Artificial Neural Network and Adaptive Neuro-fuzzy Inference System

An attenuation relationship model belonging to a region with a high earthquake hazard is important. It is used for engineering studies to know how the peak ground acceleration (PGA) value depends on the distance where there are no stations. This study used earthquakes with magnitudes greater than 4 that IzmirNET recorded between 2009 and 2017 to determine the PGA through an artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), which are widely applied in engineering seismology studies. For this purpose, 2925 records from 62 earthquakes were analysed in the ANN and ANFIS applications. Magnitude, focal depth, hypocentral distance (Rhyp), and site conditions comprise the inputs, and PGA values are the outputs. Using the Karaburun earthquake, we compared the ANN and ANFIS models using different ground motion prediction equations (GMPE) and the appropriate criteria. We determined the proximate values to PGA values measured at IzmirNET stations of the Karaburun earthquake, which was M = 6.2 in 2017, were used to test the ANN and ANFIS. The results were examined and indicated that the ANN and ANFIS are good candidates for obtaining PGA values for future earthquakes in the studied area. In addition, the PGA values of subsequent earthquakes can be calculated more quickly without any preliminary evaluation using an ANN and ANFIS.

Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques

Peak ground acceleration (PGA) is frequently used to describe ground motions accurately to defined the zone is critical for structural engineering design. This study developed a novel models for predicting the PGA using Artificial Neural Networks-Gravitational Search Algorithm (ANN-GSA) and Response Surface Methodology (RSM). This paper grants the prediction of PGA for the seismotectonic of Iraq, which is considered the earlier attempt in Iraqi region. The magnitude of the earthquake, the average shear-wave velocity, the focal depth, the distance between the station, and the earthquake source were used in this study. The proposed models are constructed using a database of 187 previous ground motion records, this dataset is also utilized to evaluate the effect of PGA’s parameters. In general, the results demonstrate that the newly proposed models exhibit a high degree of correlation, perfect mean values, a low coefficient of variance, fewer errors, and an acceptable performance index value compared to actual PGA values. However, the composite ANN-GSA model performs better than the RSM model.

Explainable Machine learning on New Zealand strong motion for PGV and PGA

Estimating ground motion characteristics at various locations as a function of fault characteristics is useful for the proper damage assessment and risk mitigation strategies. This paper explores the application of machine learning approaches to predict peak ground acceleration (PGA) and peak ground velocity (PGV) using New Zealand’s strong motion data. Five machine learning algorithms, namely linear regression, kNN, SVM, Random Forest, and XGBoost, are used in this study. Using the New Zealand flat-file database, the geometric mean of the peak ground motion parameters is used as predictor variables in training the machine learning algorithms. The performance of the chosen algorithms and how they work on PGV and PGA are discussed. The best prediction for PGA is obtained using random forest but for PGV XGboost worked best. The relative importance of various features in the flat file is also presented for the best-performing machine learning algorithm. Although the magnitude of an earthquake is found to be most influential for PGV, rupture distance showed the highest impact for PGA. Finally, the predictions are also explained using SHApley Additive exPlanations (SHAP) for the overall dataset as well as on a sample by sample basis, for a few samples. Pairwise dependency of some features with the highest feature importance is also presented using SHAP.

Ground-Motion Model for Crustal Events in Italy by Applying the Multisource Geographically Weighted Regression (MS-GWR) Method

ABSTRACT In this article, we implement a new approach to calibrate ground-motion models (GMMs) characterized by spatially varying coefficients, using the calibration dataset of an existing GMM for crustal events in Italy. The model is developed in the methodological framework of the multisource geographically weighted regression (MS-GWR, Caramenti et al., 2020), which extends the theory of multiple linear regression to the case with model coefficients that are spatially varying, thus allowing for capturing the multiple sources of nonstationarity in ground motion related to event and station locations. In this way, we reach the aim of regionalizing the ground motion in Italy by specializing the model in a nonergodic framework. Such an attempt at regionalization also addresses the purpose of capturing the regional effects in the modeling, which is needed for the Italian country, where ground-motion properties vary significantly across space. Because the proposed model relies on the italian GMM (ITA18) (Lanzano et al., 2019) dataset and functional form, it could be considered the ITA18 nonstationary version, thus allowing one to predict peak ground acceleration and velocity, as well as 36 ordinates of the 5%-damped acceleration response spectra in the period interval T=0.01–10 s. The resulting MS-GWR model shows an improved ability to predict the ground motion locally, compared with stationary ITA18, leading to a significant reduction of the total variability at all periods of about 15%–20%. The article also provides scenario-dependent uncertainties associated with the median predictions to be used as a part of the epistemic uncertainty in the context of probabilistic seismic hazard analyses. Results show that the approach is promising for improving the model predictions, especially on densely sampled areas, although further studies are necessary to resolve the observed trade-off inherent to site and path effects, which limits their physical interpretation.

Particle swarm optimization technique-based prediction of peak ground acceleration of Iraq’s tectonic regions

Peak ground acceleration (PGA) is one of the critical factors that affect the determination of earthquake intensity. PGA is generally utilized to describe ground motion in a particular zone and is able to efficiently predict the parameters of site ground motion for the design of engineering structures. Therefore, novel models are developed to forecast PGA in the case of the Iraqi database, which utilizes the particle swarm optimization (PSO) approach. A data set of 187 historical ground-motion recordings in Iraq’s tectonic regions was used to build the explicit proposed models. The proposed PGA models relate to different seismic parameters, including the magnitude of the earthquake (Mw), average shear-wave velocity (VS30), focal depth (FD), and nearest epicenter distance (REPi) to a seismic station. The derived PGA models are remarkably simple and straightforward and can be used reliably for pre-design purposes. The proposed PGA models (i.e., models I and II) obtained via the explicit formula produced using the PSO method are highly correlated to the actual PGA records owing to low coefficients of variation (CoV) of approximately 2.12% and 2.06%, and mean values (i.e., close to 1.0) of approximately 1.005 and 1.004. Lastly, high-frequency, low absolute relative error (ARE), which is below 5%, is recorded for the proposed models, thereby showing an acceptable error distribution.

Onsite Early Prediction of PGA Using CNN With Multi-Scale and Multi-Domain P-Waves as Input

Although convolutional neural networks (CNN) have been applied successfully to many fields, the onsite earthquake early warning by CNN remains unexplored. This study aims to predict the peak ground acceleration (PGA) of the incoming seismic waves using CNN, which is achieved by analyzing the first 3 s of P-wave data collected from a single site. Because the amplitude of P-wave data of large and small earthquakes can differ, the multi-scale input of P-wave data is proposed in this study in order to let the CNN observe the input data in different scales. Both the time and frequency domains of the P-wave data are combined into multi-domain input, and therefore the CNN can observe the data from different aspects. Since only the maximum absolute acceleration value of the time history of seismic waves is the target output of the CNN, the absolute value of the P-wave time history data is used instead of the raw value. The proposed arrangement of the input data shows its superiority to the one directly inputting the raw P-wave data into the CNN. Moreover, the predicted PGA accuracy using the proposed CNN approach is higher than the one using the support vector regression approach that employed the extracted P-wave features as its input. The proposed CNN approach also shows that the accuracy of the predicted PGA and the alert performances are acceptable based on data from two independent and damaging earthquakes.

Seismic attenuation model for data gap regions using recorded and simulated ground motions

In this study, seismic attenuation model is derived using recorded and simulated ground motions covering the data gap region of the Indo-Gangetic Plains (IGP), employing artificial neural networks (ANN) methodology. Four independent variables moment magnitude (Mw), focal depth, epicentral distance (Repi), and average shear wave velocity up to 30 m depth (Vs30) are selected to predict peak ground acceleration (PGA) and pseudo-spectral acceleration (PSA) (5% damping) between periods 0.01 to 4 s (twenty-five periods in total), utilizing 926 recordings (PESMOS, CIGN and synthetic database). A feed-forward ANN with Levenberg–Marquardt training algorithm is employed for training the network of input and output dataset. The optimal network architecture obtained in this study consists of 4–9–26 input, hidden and target nodes, respectively. In spite of the absence of presumed functional dependencies in ANN methodology, our model captured a number of sound physical features of earthquake ground motion: magnitude scaling, attenuation with distance and radiation damping. Further, the performance of the model is measured by the standard deviation of the error, σ(e), and compared with the four widely used conventional GMPEs applicable for IGP region of India. The standard deviations for our model varied between 0.208 and 0.263 which is less than the classical GMPEs at all twenty-six periods of PSA. Finally, the ANN model performance is compared with recorded ground motions at four stations and conventional GMPEs, and the results affirm that this model is competent to predict the response spectrum with good accuracy for IGP region.

Peak Ground Acceleration Analysis using Past Earthquake Data

Peak Ground Acceleration (PGA) is one of the important parameters in the design of seismic resistant structures. In order to predict the value of PGA for a particular place, the past seismic history of that place in terms of data sets is required. The seismic data analysis of regions or nations becomes mandatory due to the frequent occurrence of earthquakes worldwide. This paper presents a study to predict peak ground acceleration of Maharashtra, India by using Excel and MATLAB tools. The available parameters used for the prediction are PGA, epicentral distance, depth and moment magnitude from the year 1912 to 2009. Both the tools have good fit in the prediction of PGA of Maharashtra (R2 >0.9); however Excel may be preferred because of its simplicity and flexibility in handling the data sets.

The Realization of an Earthquake Early Warning System for Schools and Its Performance during the 2019 ML 6.3 Hualien (Taiwan) Earthquake

AbstractThis article discusses the earthquake early warning system (EEWS) for schools of the National Center for Research on Earthquake Engineering, Taiwan (NCREE’s EEWS, earthquake early warning system [NEEWS]) that was recently completed. The system consists of 98 seismic stations with a complete set of system capabilities and 3514 broadcast stations with only the associated alert broadcast system capabilities. The broadcast stations receive both any on-site alerts issued by the seismic stations and any regional alerts issued by the Central Weather Bureau and then broadcast whatever alert is received earliest. Shortly after the establishment of the NEEWS, the ML 6.3 Hualien earthquake, which had a maximum measured peak ground acceleration (PGA) of 515.17 Gal, struck Taiwan on 18 April 2019. During the earthquake, the performance of both the seismic stations and the broadcast stations of the system was documented. The current study analyzes and discusses the accuracy of the PGA predictions, lead times, and classification performance at both the seismic stations and the broadcast stations of the NEEWS. The results show that the NEEWS is a cost-effective and promising system of EEW.

Prediction of Peak Ground Acceleration for Maharashtra, India using Excel and MATLAB

Prediction of Peak Ground Acceleration for Maharashtra, India using Excel and MATLAB