Peak Ground Displacement Research Articles

Earthquake magnitude estimation based on peak ground displacements recorded by high-rate GNSS for February 6, 2023, earthquake sequence in Turkiye

Earthquake magnitude estimation based on peak ground displacements recorded by high-rate GNSS for February 6, 2023, earthquake sequence in Turkiye

Spatial correlation assessment of multiple earthquake intensity measures using physics-based simulated ground motions

Predictive models for spatial correlation play an effective role in the assessment of seismic risk associated with distributed infrastructure and building portfolios. However, existing models often rely on simplified approaches, assuming isotropy and stationarity. This paper verifies these assumptions by presenting a comprehensive study using a database of 3D physics-based simulated broadband ground motions for Istanbul, generated by the SPEED software. The results reveal significant event-to-event variability and nonstationary and anisotropic characteristics of spatial correlation influenced by source, path, and site effects. The development of nonstationary correlation models requires exploring influential metrics beyond spatial proximity and gaining a deep understanding of their impact, which is the focus of this study. Analysis of the spatial correlations of peak ground displacement, peak ground velocity, peak ground acceleration, and response spectral accelerations at different periods, employing both stationary and nonstationary correlation modelling methods and considering the finite fault model, indicates that the slip distribution pattern, direction and distance of station pairs relative to earthquake rupture, soil softness, and homogeneity of soil properties significantly influence the spatial correlations of near-field earthquake ground motions. Implementation of the introduced parameters in predictive spatial correlation models enhances the precision of regional seismic hazard assessments.

A non-parametric model of ground motion parameters for shallow crustal earthquakes in Europe

The current study focuses on deriving ground motion models (GMMs) for 21 ground motion parameters derived from data sourced from the Engineering Strong Motion (ESM) database. These parameters include Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), Peak Ground Displacement (PGD), PGV-to-PGA ratio, (V/H) PGA ratio Predominant Frequency (Fp), Central Frequency (Ω), Spectral Parameter (q), Significant Duration (TSig), Root Mean Square Acceleration (Arms), Arias Intensity (Ia), Cumulative Absolute Velocity (CAV), Characteristic Intensity (IC), Acceleration Spectrum Intensity (ASI), Velocity Spectrum Intensity (VSI), Total Energy (Eacc), Spectral Centroid (Ew), Spectral Standard Deviation (Sw), Temporal Centroid (Et), Temporal Standard Deviation (St), and Correlation between time and frequency [ρ(t,ω)]. Both horizontal and vertical components are considered in this study. The inherent random effects within ground motion regression, encompassing inter-event, inter-site, inter-locality, and inter-region variabilities, are addressed using cross-nested mixed effect regression utilizing a non-parametric GMM approach employing Artificial Neural Network (ANN). Quantitative assessment of the models involves correlation coefficients for regression through the origin and error measures like mean squared error and mean absolute error. These findings of the assessment confirm reliable estimates of Ground Motion Parameters (GMPs). A comparison of GMPs computed using the proposed model and those reported in the literature indicated model's superior performance. Furthermore, satisfactory performance of the proposed GMM in ground motion simulation for the ESM region is demonstrated.

Machine learning-driven feature importance appraisal of seismic parameters on tunnel damage and seismic fragility prediction

This study proposes a machine learning-driven approach for the analysis of the feature importance of seismic parameters on tunnel damage and seismic fragility prediction. The Incremental Dynamic Analysis (IDA) method serves as the fundamental database for vulnerability analysis. Strength and deformation yield criteria are chosen to comprehensively assess the impact of different seismic parameters on the vulnerability of tunnels to seismic events. Three machine learning algorithms, namely Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM), are utilized to develop models for classifying and regressing tunnel damage under seismic conditions. Following parameter tuning, the models' performance in multi-classification, binary classification, and regression prediction is assessed, with XGBoost and RF models exhibiting outstanding performance. Feature importance analysis of seismic parameters in XGBoost and RF models for multi-classification, binary classification, and regression is performed using Shapley additive explanations (SHAP). The correlation analysis between SHAP-based feature values and predictions reveals that Peak Ground Displacement (PGD) has the highest influence in the regression model. Utilizing the interaction dependencies among crucial features in the regression model, fragility curves for tunnels based on these key features are effectively derived. The predicted fragility curves closely align with those derived from IDA, illustrating the time-saving and high-performance capabilities of machine learning in nonlinear dynamic computations.

Estimating S-wave amplitude for earthquake early warning in New Zealand: Leveraging the first 3 seconds of P-Wave

This study addresses the critical question of predicting the amplitude of S-waves during earthquakes in Aotearoa New Zealand (NZ), a highly earthquake-prone region, for implementing an Earthquake Early Warning System (EEWS). This research uses ground motion parameters from a comprehensive dataset comprising historical earthquakes in the Canterbury region of NZ. It explores the potential to estimate the damaging S-wave amplitude before it arrives, primarily focusing on the initial P-wave signals. The study establishes nine linear regression relationships between P-wave and S-wave amplitudes, employing three parameters: peak ground acceleration, peak ground velocity, and peak ground displacement. Each relationship’s performance is evaluated through correlation coefficient (R), coefficient of determination (R²), root mean square error (RMSE), and 5-fold Cross-validation RMSE, aiming to identify the most predictive empirical model for the Canterbury context. Results using a weighted scoring approach indicate that the relationship involving P-wave Peak Ground Velocity (Pv) within a 3-second window strongly correlates with S-wave Peak Ground Acceleration (PGA), highlighting its potential for EEWS. The selected empirical relationship is subsequently applied to establish a P-wave amplitude (Pv) threshold for the Canterbury region as a case study from which an EEWS could benefit. The study also suggests future research exploring complex machine learning models for predicting S-wave amplitude and expanding the analysis with more datasets from different regions of NZ.

An earthquake spectra parameters near the new capital administrative city, Egypt

Seven Strong Motion stations recorded an earthquake of Mw 3.8 on December, 31 2018 on the Cairo- Suez southern road. The furthest NUB station is about 302 km from the epicenter, whereas the closest KOT station is 10 km away. This event is considered the first recorded acceleration event near the new capital administrative city. The KOT station has the highest recorded acceleration, measuring 16.38 Gal. The peak ground velocity (PGV), peak ground displacement (PGD), and Pseudo acceleration values for the same station are calculated to be 0.00109 m/s, 0.114 cm, and 0.0731 g, respectively. An intensity map was created for this event as a questionnaire gathered information from Facebook and EMSC. In the vicinity of Qattamiya Observatory, the maximum observed intensity is (IV). According to the geological information and average shear wave velocity calculated from previous studies, We classified the station sites into soil types B, C, and D. We correlated them with the HVSR analysis obtained from S-wave earthquake data. The maximum amplification factor (A0) was found for a BANH station equal to 5.8 and equivalent to the Fundamental frequency (F0) of 3 Hz. The minimum amplification factor was found to be for stations of ISM & NUB with A0 equal to 2.8 and 1.6, respectively, while Fundamental frequency (F0) was 0.7 and 2 Hz, respectively. At the KOT station, the response design spectrum indicates a maximum value of 70 Gal. The new Egyptian capital administrative city is strongly advised to set up an early warning system and structural earth monitoring to manage the risk assessment of any potential seismic activities in the future.

Seismic hazard prediction of the Hunhe Fault in the Shen-Fu New District

Earthquake prevention and disaster mitigation are crucial aspects of social welfare that significantly impact national public security. This paper presents a seismic risk assessment and hazard prediction of the Hunhe Fault in the Shengyang-Fushun (Shen-Fu) New District. The target area is at risk of seismic damage due to two major branch ruptures, namely, F9 and F1; these ruptures have the potential to generate maximum earthquakes with a magnitude of 6.0 in the next 50 to 100 years. A three-dimensional underground velocity structure and asperity source model were established for the target faults. Subsequently, a hybrid technique combining deterministic and empirical approaches was employed to simulate the broadband strong ground motion of the target region in anticipation of the occurrence of expected scenario earthquakes. The distributions of peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD) for the area are provided, and the results indicate that densely populated urban areas could experience PGA values close to 280 cm/s2 along the fault traces. This study provides a reliable basis for engineering construction and urban planning in the Shen-Fu New District.

The Impact of the Three-Dimensional Structure of a Subduction Zone on Time-dependent Crustal Deformation Measured by HR-GNSS

Accurately modeling time-dependent coseismic crustal deformation as observed on high-rate Global Navigation Satellite System (HR-GNSS) lends insight into earthquake source processes and improves local earthquake and tsunami early warning algorithms. Currently, time-dependent crustal deformation modeling relies most frequently on simplified 1D radially symmetric Earth models. However, for shallow subduction zone earthquakes, even low-frequency shaking is likely affected by the many strongly heterogeneous structures such as the subducting slab, mantle wedge, and the overlying crustal structure. We demonstrate that including 3D structure improves the estimation of key features of coseismic HR-GNSS time series, such as the peak ground displacement (PGD), the time to PGD (tPGD), static displacements (SD), and waveform cross-correlation values. We computed synthetic 1D and 3D, 0.25 Hz and 0.5 Hz waveforms at HR-GNSS stations for four M7.3+ earthquakes in Japan using MudPy and SW4, respectively. From these synthetics, we computed intensity-measure residuals between the synthetic and observed GNSS waveforms. Comparing 1D and 3D residuals, we observed that the 3D simulations show better fits to the PGD and SD in the observed waveforms than the 1D simulations for both 0.25 Hz and 0.5 Hz simulations. We find that the reduction in PGD residuals in the 3D simulations is a combined effect of both shallow and deep 3D structures; hence incorporating only the upper 30 km of 3D structure will still improve the fit to the observed PGD values. Our results demonstrate that 3D simulations significantly improve models of GNSS waveform characteristics and will not only help understand the underlying processes, but also improve local tsunami warning.

Optimal earthquake intensity measures for seismic vulnerability assessment of concrete-faced rockfill dams

Earthquake intensity measure (IM) selection is crucial for developing probabilistic seismic demand models (PSDMs). This study explored the optimal IMs that can be utilized in PSDMs for concrete-faced rockfill dams (CFRDs). A two-dimensional nonlinear dynamic analysis of a CFRD was performed to assess its response to earthquake ground motion. A numerical model of CFRD was developed using the commercial software LS-DYNA with an advanced hysteretic soil model. A total of 20 earthquake IMs were selected for regression analysis. The regression analysis was performed between IMs and the damage index (DI). The IMs were evaluated through a selection process based on the goodness of fit as well as their efficiency, practicality, and proficiency. Accordingly, a range of optimal IMs was determined. The results indicated that effective design acceleration (EDA) is the best earthquake IM correlating with the seismic response of dam settlement and can be considered the optimal IM, whereas peak ground displacement (PGD), ratio of peak ground velocity to peak ground acceleration (PGV/PGA), and predominant period (Tp) are the less efficient. The fragility curves and surfaces were developed employing both scalar and vector IMs for CFRD.

GSeisRT: A Continental BDS/GNSS Point Positioning Engine for Wide-Area Seismic Monitoring in Real Time

Precise coseismic displacements in earthquake/tsunamic early warning are necessary to characterize earthquakes in real time in order to enable decision-makers to issue alerts for public safety. Real-time global navigation satellite systems (GNSSs) have been a valuable tool in monitoring seismic motions, allowing permanent displacement computation to be unambiguously achieved. As a valuable tool presented to the seismic community, the GSeisRT software developed by Wuhan University (China) can realize multi-GNSS precise point positioning with ambiguity resolution (PPP-AR) and achieve centimeter-level to sub-centimeter-level precision in real time. While the stable maintenance of a global precise point positioning (PPP) service is challenging, this software is capable of estimating satellite clocks and phase biases in real time using a regional GNSS network. This capability makes GSeisRT especially suitable for proprietary GNSS networks and, more importantly, the highest possible positioning precision and reliability can be obtained. According to real-time results from the Network of the Americas, the mean root mean square (RMS) errors of kinematic PPP-AR over a 24 h span are as low as 1.2, 1.3, and 3.0 cm in the east, north, and up components, respectively. Within the few minutes that span a typical seismic event, a horizontal displacement precision of 4 mm can be achieved. The positioning precision of the GSeisRT regional PPP/PPP-AR is 30%–40% higher than that of the global PPP/PPP-AR. Since 2019, GSeisRT has successfully recorded the static, dynamic, and peak ground displacements for the 2020 Oaxaca, Mexico moment magnitude (Mw) 7.4 event; the 2020 Lone Pine, California Mw 5.8 event; and the 2021 Qinghai, China Mw 7.3 event in real time. The resulting immediate magnitude estimates have an error of around 0.1 only. The GSeisRT software is open to the scientific community and has been applied by the China Earthquake Networks Center, the EarthScope Consortium of the United States, the National Seismological Center of Chile, Institute of Geological and Nuclear Sciences Limited (GNS Science Te Pū Ao) of New Zealand, and the Geospatial Information Agency of Indonesia.

Prediction for underground seismic intensity measures using conditional generative adversarial networks

With the escalating development and utilization of subterranean spaces, the seismic hazards faced by underground structures are progressively increasing. However, owing to the challenges associated with acquiring underground seismic data and historical seismic design norms, pertinent regulations and research in this domain are scarce. This study focused on three crucial intensity measures in the seismic design process of underground structures peak ground acceleration (PGA), peak ground velocity (PGV), and peak ground displacement (PGD). The research leverages seismic data obtained from the California Strong Motion Instrumentation Program (CSMIP) to train and evaluate a conditional generative adversarial network (CGAN) model. This model was employed to establish a multivariate joint conditional probability distribution among the intensity measures at varying depths, facilitating the stochastic prediction of shallow intensity measures. In contrast to empirical formulas, the CGAN model eliminates the need for a predefined equation structure and enables the simultaneous prediction of multiple intensity measures. The performance of the model was evaluated by comparing the predictive accuracy of the CGAN model and empirical fitting formulas across diverse site conditions and depth intervals using metrics such as relative error coefficients. It can be concluded that the proposed CGAN model can accurately predict shallow seismic intensity measures, and the predictions conform to a specific conditional distribution while retaining the stochastic nature of seismic motion. Compared with empirical formula models, the CGAN model exhibited an enhanced predictive capability.

Assessing seismic displacements and early warning using kinematic PPP with MADOCA real-time products: A case study of 2023 Kahramanmaraş (Türkiye) earthquakes

Early warning and emergency hazard response studies in large earthquakes require rapid and reliable determination of earthquake magnitude. The magnitude of an earthquake can be estimated based on peak ground displacement (PGD) values captured from high-rate GNSS records. In this study, two major earthquakes that hit Kahramanmaraş Province on February 6, 2023, were investigated by using the real-time Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) technique to confirm the usability of GNSS in early warning. Data from high-rate (1-Hz) GNSS stations were evaluated with the real-time kinematic PPP approach utilizing the multi-GNSS advanced demonstration tool for orbit and clock analysis (MADOCA) products and JPL's GipsyX software. In addition, static PPP was also used to identify the permanent displacements caused by earthquakes. Static PPP results demonstrated that significant displacements were detected at stations close to the epicenters, particularly the 446.86 cm horizontal displacement in the EKZ1 station. Considering the kinematic PPP results revealed that when PGD values calculated from seismic waves, functional model, and updated coefficients were implemented, the moment magnitude of the Pazarcık earthquake was estimated as Mw 7.78, which was reported as Mw 7.8 by USGS and Mw 7.7 by AFAD, whilst the magnitude of the Elbistan earthquake was computed as Mw 7.56, which was reported as Mw 7.5 by USGS and 7.6 by AFAD. Overall, this study validates the effectiveness of the real-time GNSS PPP as an attractive alternative for analyzing coseismic events and supporting early warning.

Applying the BeiDou Navigation Satellite System in the acquisition of the coseismic deformation and the high-frequency dynamic displacement of the 2021 MW 7.4 Maduo earthquake

SUMMARY This study acquires the coseismic deformation field and the high-frequency dynamic displacement of the MW 7.4 earthquake that occurred in Maduo, China, on 2021 May 22, based on the BeiDou Navigation Satellite System (BDS), and the comparison with the results obtained by the Global Positioning System (GPS) reveals that the two systems are certain differences in their ability to acquire the coseismic deformation field. The maximum difference in the horizontal coseismic deformation is <5 mm, and the maximum difference in the vertical coseismic deformation is 8.7 mm. The dynamic displacement waveforms of the 2021 MW 7.4 Maduo earthquake acquired by BDS and GPS are very similar, which confirms that BDS can acquire ground-shaking images with an accuracy comparable to that of GPS. Based on the empirical relationship equation of the peak ground displacement (PGD) and moment magnitude (MW), this study verifies and calculates both the MW of the 2021 MW 7.4 Maduo earthquake and the error and finds that the MW can be quickly and accurately obtained by using the empirical PGD and MW equations, and this MW value can be used as a supplementary means of calibrating the MW of the large earthquake early warning systems, which can be quickly determined by seismic wave data. Finally, by comparing the slip distributions inverted from the BDS and GPS coseismic deformation fields, this study finds that BDS is equally effective as GPS.

New Baseline Correction Method for Near-Fault Ground-Motion Records Based on Continuous Wavelet Transform

Abstract On 6 February 2023, a magnitude 7.8 earthquake struck south-central Türkiye. It was followed by many aftershocks, the largest of which was a magnitude 7.5 aftershock. This earthquake caused considerable loss of life and property. Numerous near-fault ground-motion records were collected during the 2023 Türkiye earthquake. Most existing baseline correction methods for near-fault ground motions disregard the commonly used filtering techniques due to their potential elimination of actual permanent displacement. To address this, a new automatic baseline correction method is proposed based on the continuous wavelet transform, which incorporates filtering while preserving permanent displacement. This method is compatible with existing filtering techniques and demonstrates good applicability. In this approach, the raw record is decomposed into a pulse signal containing permanent displacement and a nonpulse signal. The nonpulse signal is high-pass filtered to obtain the corrected nonpulse signal, and then the true ground motion is obtained by combining the pulse signal with the corrected nonpulse signal. The effectiveness of the proposed method is evaluated across five aspects using the 1999 Chi-Chi earthquake records: time histories, baseline offsets, response spectra, peak ground velocities, and peak ground displacements. Furthermore, the method is applied to 36 station records (108 components) of the 2023 Türkiye earthquake. The analysis results indicate that the corrected displacement time histories exhibit sustained flatness in their tails and demonstrate a closer agreement with the Global Navigation Satellite System (GNSS) measurements than the previous methods. This method can also incorporate static GNSS offsets to accurately determine true ground motions with precise permanent displacements. The corrected results can be utilized for nonlinear seismic calculations and structural damage analysis of fault-crossing structures such as bridges and tunnels.

Displacement analysis of the October 30, 2020 (M w = 6.9), Samos (Aegean Sea) earthquake

Abstract Destructive earthquakes with high deformations have occurred in the Aegean region since the historical period. The most destructive of these earthquakes in recent years is the October 30, 2020 (M w = 6.9) Samos (Aegean Sea) earthquake. This earthquake affected a wide area and caused numerous losses of lives and property especially in Izmir city. For examining the effects of the earthquake, Global Navigation Satellite System (GNSS) data before, during, and after the earthquake were processed, and coseismic and postseismic displacement evaluations were made. Interferometric Synthetic Aperture Radar (InSAR) ascending, descending interferograms, line of sight velocity, and displacement maps were obtained for the earthquake-affected area. The GNSS and InSAR data were evaluated together, and the areas with subsidence and uplift were determined in conjunction with the fault zone. In addition, the horizontal displacements were analyzed by using Coulomb failure criteria, and peak ground displacements were obtained from the strong motion stations located in the study region. As a result, from all the displacement analyses, it was determined that high-amplitude energy was released, at the regional scale from Ayvalık in the North to Datça in the South after the earthquake, and this earthquake generated permanent deformation in the affected region.

Ensemble Region-Specific GMMs for Subduction Earthquakes

Abstract This study develops data-driven global and region-specific ground-motion models (GMMs) for subduction earthquakes using a weighted average ensemble model to combine four different nonparametric supervised machine-learning (ML) algorithms, including an artificial neural network, a kernel ridge regressor, a random forest regressor, and a support vector regressor. To achieve this goal, we train individual models using a subset of the Next Generation Attenuation-Subduction (NGA-Sub) data set, including 9559 recordings out of 153 interface and intraslab earthquakes recorded at 3202 different stations. A grid search is used to find each model’s best hyperparameters. Then, we use an equally weighted average ensemble approach to combine these four models. Ensemble modeling is a technique that combines the strengths of multiple ML algorithms to mitigate their weaknesses. The ensemble model considers moment magnitude (M), rupture distance (Rrup), time-averaged shear-wave velocity in the upper 30 m (VS30), and depth to the top of the rupture plane (Ztor), as well as tectonic and region as input parameters, and predicts various median orientation-independent horizontal component ground-motion intensity measures such as peak ground displacement, peak ground velocity, peak ground acceleration, and 5%-damped pseudospectral acceleration values at spectral periods of 0.01–10 s in log scale. Although no functional form is defined, the response spectra and the distance and magnitude scaling trends of the weighted average ensemble model are consistent and comparable with the NGA-Sub GMMs, with slightly lower standard deviations. A mixed effects regression analysis is used to partition the total aleatory variability into between-event, between-station, and event-site-corrected components. The derived global GMMs are applicable to interface earthquakes with M 4.9–9.12, 14≤Rrup≤1000 km, and Ztor≤47 km for sites having VS30values between 95 and 2230 m/s. For intraslab events, the derived global GMMs are applicable to M 4.0–8.0, 28≤Rrup≤1000 km, and 30≤Ztor≤200 km for sites having VS30 values between 95 and 2100 m/s.

Prediction of ground vibration under combined seismic and high-speed train loads considering earthquake intensity and site category

Based on a two-and-half-dimensional finite element model (2.5D FEM), the layered ground vibration under combined seismic and high-speed train loads was investigated. On this basis, the effect of site category and earthquake intensity on ground vibration under the combined action of two dynamic loads was analyzed. Numerical examples indicated that ground vibration displacement due to combined loads decreases with the increase of soil stiffness, while the influence of soil stiffness on the ground vibration is small when the hardness of the subsoil is large. The peak ground displacement (PGD) is a reasonable seismic intensity index for predicting the ground vibration displacement at the track center under the combined loads, which has a higher accuracy for hard ground. In view of this, an equivalent shear wave velocity and PGD-based prediction formula was proposed to estimate the ground vibration under combined seismic and high-speed train loads. Reliability of the prediction formula was verified through comparison with results of numerical tests, indicating that the prediction formula has good applicability to different site conditions and seismic events. Compared with the previous study, it demonstrated that the prediction method provided an effective means for estimating ground vibration caused by a high-speed train load during earthquakes.

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.

From ground motion simulations to landslide occurrence prediction

Ground motion simulations solve wave equations in space and time, thus producing detailed estimates of the shaking time series. This is essentially uncharted territory for geomorphologists, for we have yet to understand which ground motion (synthetic or not) parameter, or combination of parameters, is more suitable to explain the coseismic landslide distribution. To address this gap, we developed a method to select the best ground motion simulation using a combination of Synthetic Aperture Radar Interferometry (InSAR) and strong motion data. Upon selecting the best simulation, we further developed a method to extract a suite of intensity parameters, which we used to analyse coseismic landslide occurrences taking the Gorkha earthquake (M7.8, 25th April 2015) as a reference. Our results show that beyond the virtually unanimous use of peak ground acceleration, velocity, or displacement in the literature, different shaking parameters could play a more relevant role in landslide occurrences. These parameters are not necessarily the product of peak ground motion but are linked to the total displacement, frequency content, and shaking duration; elements too often neglected in geomorphological analyses. This, in turn, implies that we have yet to fully acknowledge the complexity of the interactions between full waveforms and hillslope responses.

Incorporation of Real-Time Earthquake Magnitudes Estimated via Peak Ground Displacement Scaling in the ShakeAlert Earthquake Early Warning System

ABSTRACTThe United States earthquake early warning (EEW) system, ShakeAlert®, currently employs two algorithms based on seismic data alone to characterize the earthquake source, reporting the weighted average of their magnitude estimates. Nonsaturating magnitude estimates derived in real time from Global Navigation Satellite System (GNSS) data using peak ground displacement (PGD) scaling relationships offer complementary information with the potential to improve EEW reliability for large earthquakes. We have adapted a method that estimates magnitude from PGD (Crowell et al., 2016) for possible production use by ShakeAlert. To evaluate the potential contribution of the modified algorithm, we installed it on the ShakeAlert development system for real-time operation and for retrospective analyses using a suite of GNSS data that we compiled. Because of the colored noise structure of typical real-time GNSS positions, observed PGD values drift over time periods relevant to EEW. To mitigate this effect, we implemented logic within the modified algorithm to control when it issues initial and updated PGD-derived magnitude estimates (MPGD), and to quantify MPGD uncertainty for use in combining it with estimates from other ShakeAlert algorithms running in parallel. Our analysis suggests that, with these strategies, spuriously large MPGD will seldom be incorporated in ShakeAlert’s magnitude estimate. Retrospective analysis of data from moderate-to-great earthquakes demonstrates that the modified algorithm can contribute to better magnitude estimates for Mw>7.0 events. GNSS station distribution throughout the ShakeAlert region limits how soon the modified algorithm can begin estimating magnitude in some locations. Furthermore, both the station density and the GNSS noise levels limit the minimum magnitude for which the modified algorithm is likely to contribute to the weighted average. This might be addressed by alternative GNSS processing strategies that reduce noise.