Hypocentral Distance Research Articles

Dependence of horizontal PGA prediction on site conditions at critically short hypocentral distances: an analysis based on Japanese strong motion data

A fundamental element in seismic hazard assessment studies is the prediction of maximum values of ground accelerations. This is achieved through an attenuation relationship, also known as the ground motion prediction equation (GMPE). Seismic design of structures generally assumes the occurrence of a large earthquake on a nearby fault. The predictions from the GMPE are linked to the magnitude, distance, and local site conditions. Critical near‑fault ground motions have unique characteristics such as magnitude saturation and distance saturation. To investigate the impact of local site conditions in the near‑fault region, we analyzed the attenuation relations using the accelerations recorded in Japan by the Kiban Kyoshin network (KiK‑net) from 2000 to 2023. All events with a magnitude equal to or greater than 5.5, and with focal depth and epicentral distance less than 20 km, were considered. In this study, the hypocentral distance ranges from 5 to 23 km. Ordinary least squares regression analysis was conducted for the maximum horizontal vector of ground accelerations. The attenuation relation coefficients for horizontal vector peak ground acceleration were estimated using an effective distance and saturation effect. An additional independent term, the average shear wave velocity of the soil to a depth of 30 meters below the ground surface, and more cost‑effective shallower depths were included in the regression model. The analysis revealed that the site condition is statistically non‑significant at very short hypocentral distances of less than 23 kilometers.

Earthquake Magnitude Estimation through Mixed-Effects Ground-Motion Modeling of Early P-Wave Arrivals

Abstract A regional earthquake early warning system (EEWS) warrants potential predictive models to accurately extract earthquake parameters like magnitude and intensity from the first few seconds of a P-wave arrival. In this study, a maiden predictive model depicting the relationship between peak displacement amplitude (Pd) and magnitude (ML) is proposed for the western Himalayan region through a mixed-effects regression and compared with those from similar tectonic regimes. This model for EEWS is derived from the vertical-component waveforms with a high signal-to-noise ratio, using three different time-window lengths (Td) of 1, 2, and 3 s, just after the P onset. Waveforms from 83 earthquakes in the magnitude (ML) range of 3 and 5.5 registered at 27 strong motion seismic stations are used for this purpose. The hypocentral distance range varies between 5 and 264 km. A comparative analysis between the models obtained through linear and linear mixed-effects (lme) regression reveals that the latter is robust. It is observed that the intra-event uncertainties are significantly reduced after site corrections and contribute more toward the total variabilities, compared to the inter-event uncertainties. Based on the results from this study, it is emphasized that the local site effects should be incorporated while developing the predictive models for EEWS. Importantly, the displacement magnitude (Mpd) derived from Pd values, accurately matches with ML, even for the data not used to derive the model, lending credence to the final model. A scaling relation between the peak ground velocities (PGV) and Pd values is also established to evaluate the seismic hazard levels. Advocating that the adapted models should be calibrated for a targeted region, the dissimilarities among different models and the implications from epistemic uncertainties are also discussed in the present study.

Development and use of semi-empirical spectral ground motion models for GPP-induced micro-earthquakes in Southern Germany

This study provides a comprehensive exploration of ground motions associated with micro-earthquakes induced by geothermal power plants (GPP) in Southern Germany and proposes corresponding ground motion prediction equations (GMPE). Initiating with a statistical analysis of recorded seismic data from the GPP in Insheim, the study is extended to the greater Munich area. For the latter, the scarce recorded data are merged with physics-based simulation data. The recorded data in Insheim, Poing, Unterhaching and the simulated data in Munich are compared to existing GMPEs for GPP-induced events, highlighting the need of new region-specific prediction equations. The proposed GMPEs are expressed in terms of peak quantities, spectral accelerations and velocities, separating the horizontal and vertical direction. The regression curves exhibit a good alignment with both recorded and simulated data, within an acceptable range. Notably, the results reveal higher spectral quantities at shorter periods (<0.1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$<0.1$$\\end{document} s), underscoring the importance of this characteristic in seismic assessment. The article shows an exemplary application for a low-rise residential building, located at a hypocentral distance of 3 km. While the building meets serviceability standards for an MW\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$M_W$$\\end{document} up to 2.5, the verification fails at MW=3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$M_W=3$$\\end{document}, emphasizing the need for robust risk assessment. These findings contribute to the understanding of ground motions of GPP-induced events, offering practical implications for serviceability verifications and aiding informed decision-making in geothermal energy projects.Graphical abstract

A local magnitude scale (ML) for Northern Algeria

This study presents a local magnitude scale (ML) based on the original Richter definition and designed for use within the Algerian Digital Seismic Network (ADSN). The magnitude scale is derived from the analysis of 17,377 zero-peak maximum amplitude traces extracted from the vertical component, simulated as Wood-Anderson seismograms. These traces are taken from a dataset of 1901 earthquakes recorded between January 1, 2010, and June 1, 2022, at a minimum of five stations in the ADSN network. To better account for the attenuation of direct and refracted waves in northern Algeria, amplitude decay analysis reveals the presence of two transition distances at 90 and 190 km, resulting in three segments. A distance correction term, −log10(A0), is introduced and described by the following trilinear function:−logA0=0.6747∗log10R+0.0002∗R+1.6306R≤901.7736∗log10R+0.0002∗R−0.516990<R≤1902.4580∗log10R+0.0002∗R−2.0765R>190R represents the hypocentral distance in kilometers. The derived distance correction formula provides a well-constrained ML relationship for northern Algeria that is valid over a distance range of 5 to 600 km. Compared to other local magnitude relationships, the methodology proposed in this study consistently gives ML values slightly higher than those calculated by the Southern California relationship over all distances, with an average difference of 0.2 units. We computed corrections for 72 stations by minimizing the ML residuals. These corrections range from −0.50 to 0.54, highlighting the influence of local site effects on the amplitude of the seismic signal. The magnitude residuals using our magnitude relationship and incorporating the station corrections, show that the standard deviation has improved significantly, from 0.34 to 0.24. An ML relationship specific to the northern Algerian region provides a valuable tool for seismic monitoring, hazard assessment, and earthquake research in the region.

A SHAP-enhanced XGBoost model for interpretable prediction of coseismic landslides

In the coseismic landslide hazard assessment (CLHA), advanced machine learning (ML) models have garnered significant attention due to their effectiveness in handling the complex relationships between landslides and various influencing factors. However, explaining the decision-making mechanisms of machine learning models that predict landslide spatial distribution based on these influencing factors remains challenging. This study compares the predictive performance of four models—XGBoost, RF, LR, and SVM—optimized using the TPE algorithm, and introduces the SHAP algorithm into the XGBoost model to achieve both global and local interpretations of CLHA. In various tests, the optimized XGBoost model demonstrated the best predictive performance, achieving an accuracy of 0.864 and an AUC value of 0.886. Global interpretations indicate that the occurrence of coseismic landslides is primarily influenced by triggering factors such as hypocentral distance and distance from the seismogenic fault. Terrain roughness and elevation, on the other hand, make significant contributions among the conditioning factors. Single-factor dependence plots indicate that the contribution of individual factors to landslides varies across different ranges of their feature values. Analysis of two-factor dependence plots reveals that interactions between factors are also crucial in influencing the occurrence of landslides. Combining field surveys with local interpretations confirms significant variations in the contributions of influencing factors within local ranges. The main innovation of this study lies in the integration of the SHAP algorithm into the CLHA model, revealing the decision-making mechanisms of the model for spatial prediction of coseismic landslides.

Direct Estimation of the Source Corner Frequency of Minor to Moderate Earthquakes from Fourier Phase Spectra Fitting

ABSTRACT Estimating the corner frequency (fc) of an earthquake is of fundamental importance to improving our knowledge of the physics of the rupture that gives rise to a seismic event, while also having important implications by providing information on the high-frequency radiation for seismic hazard studies. However, the estimation of fc through spectral-fitting methods suffers from trade-offs with the estimation of seismic wave attenuation, making the obtained values precise but not necessarily accurate. For this reason, after a review of the source model proposed by Brune (1970), a new method of estimating fc, based on the phase fitting of Fourier spectra of the Brune’s seismic pulse related to S-waves, called Fourier phase spectra fitting (FPS), is proposed and evaluated in this study. The method can be applied in cases in which Brune’s model may be appropriate, while also considering the effects of propagation on impulse deformation, for which the synthetic tests have been conducted. The results, obtained first using synthetic seismograms generated under controlled conditions and then on a data set of recordings of real seismic events collected at the Groningen gas field in the Netherlands, showed the method to be promising (being accurate and precise) and at the same time pointed out its limitations, with its applicability being restricted to short hypocentral distances of &amp;lt;20 km.

Correction Factors to Account for Seismic Directionality Effects: Case Study of the Costa Rican Strong Motion Database

This article presents the findings of a study on the directionality effect observed in strong motion records. We set out to establish ratios between several seismic intensity measures that depend on sensor orientation (e.g., GMar, Larger) and others that are orientation-independent (e.g., RotDpp, GMRotDpp, and GMRotIpp), with the intention of proposing multiplicative correction factors. The analysis included an evaluation of the impact of site conditions, ground motion intensity, earthquake magnitude, and hypocentral distance on these ratios. Following a concise overview of the directionality effects and the associated intensity measures, the Costa Rican Strong Motion Database, comprising a total of 4199 horizontal accelerograms (two components), was employed to determine the correction factors. The analysis was carried out for 5% damped response spectra within the 0.01–5 s period range. The study focuses on orientation-independent intensity measures that are derived by combining the maximum values from the recorded motions. In the comprehensive analysis of the complete database, a trend was observed between these intensity measures and the magnitude of the earthquake along with the hypocentral distance. Specifically, records from earthquakes with greater magnitudes exhibited a lower maximum spectral response to the geometric mean of the response spectra of the as-recorded (ar) components ratio (RotD100/GMar), similar to records from earthquakes with larger hypocentral distances. Based on these findings, a proposal was put forth to estimate RotD100 values using GMar values. This ratio can prove useful in transforming data from previous seismic hazard studies, including those applied in many seismic codes, and in defining the maximum anticipated seismic intensity for design purposes in a more straightforward manner.

Introducing ViDA3, An Earthquake Early Warning Algorithm for Offshore Hypocenter Determination Using Onshore Seismic Networks

ABSTRACT The objective functions adopted by earthquake early warning (EEW) location algorithms are inadequate for out-of-network earthquakes. As a result, the real-time locations of these earthquakes are often erroneous. The consequences of mislocating out-of-network earthquakes are that their magnitudes are miscalculated, and the loci of their shaking predictions map are shifted. Given that the largest earthquakes occur in subduction settings, improving real-time out-of-network earthquake location is of great importance. In this study, the Virtual Dynamically Assembled Array Algorithm (ViDA3) is introduced, which addresses the location issue of offshore and off-network earthquakes. The guiding principle underlying the new EEW location algorithm is that standard seismic networks may be viewed as a collection of medium-sized seismic arrays, with each array consisting of three or more network stations. The potential of array seismology for EEW against out-of-network earthquakes stems primarily from the slowness vector, which points at the direction of the epicentral region. Thus, this region may be constrained merely by intersecting two or more such vectors. In addition, the length of the slowness vector depends on the hypocentral distance and depth and is thus vital for addressing an acute problem in a subduction setting—discriminating between upper crust and deep slab earthquakes. Furthermore, when the slowness of the P phase is known, the slowness of the S phase is deduced, and the S-phase arrival is searched for using the shift-and-sum practice. What makes ViDA3 so attractive is that, in locations where a real-time network is already in place, these added values may be achieved without extra hardware or substantial budget requirements. We present the result of ViDA3 real-time operation on a shallow earthquake offshore Vancouver Island and the result of its replay on a deep slab earthquake in northern Chile. ViDA3 performance is further assessed using a dataset of seismograms from the Mendocino Triple Junction area. It is concluded that ViDA3 location scheme outperforms currently available EEW location algorithms for out-of-network earthquakes.

On-Site Earthquake Early Warning Model for Selected Records in the NGA-West2 Dataset Using S- and P-Wave Spectral Ratios

ABSTRACT On-site earthquake early warning (EEW) requires the best estimates of earthquake magnitude and distance parameters within a few seconds after the P-wave arrival for estimating the subsequent S-wave parameters. The errors in the estimates of the earthquake P-wave parameters will propagate into the estimates of the S-wave parameters. To solve this problem, we used the methodology by Zhao and Zhao (2019), which uses the spectral ratio R3P between the response spectral values of the first 3 s of the S wave and that of the first 3 s of the P wave, referred to as the R3P model. The modeling presented here was based on strong-motion records from the Next Generation Attenuation (NGA)-West2 dataset. We also used the spectral ratio RFP between the response spectral values from the full records from the S-wave arrival time to the end of the record and that of the first 3 s P wave to develop a second EEW model (the RFP model). An advantage of these two models is that the magnitude and hypocentral distance are not required in considerable magnitude and distance ranges. This means that the errors in the estimated source and path parameters from the first 3 s of the P wave will not affect the model predictions. A theoretical justification for these results is that the magnitude and the distance scaling rates for the first 3 s of the P wave are similar to those of the first 3 s of the S wave. This may also apply to the full S-wave window within useful EEW magnitude and distance ranges. We also found that when the estimated magnitude and distance for a record are necessary, the effect of the corresponding errors would be smaller than using a ground-motion prediction equation (GMPE), because the magnitude and distance scaling rates from this study are smaller than those of many GMPEs.

Ground-Motion Model for Small-to-Moderate Potentially Induced Earthquakes Using an Ensemble Machine Learning Approach for CENA

ABSTRACT Ground-motion models (GMMs) are vital in assessing probabilistic seismic hazards and uncertainties. This study develops new GMMs benefiting from nonparametric machine learning algorithms, including artificial neural networks, kernel ridge, random forest, and gradient boosting regression techniques for small-to-moderate potentially induced earthquakes in central and eastern North America (CENA). As part of this study, we evaluate the performance of different machine learning models in estimating peak ground acceleration (PGA) and 17 spectral accelerations based on the moment magnitude (Mw), hypocentral distance (Rhypo), and the timed-average shear-wave velocity of the upper 30 m of soil (VS30). To train the algorithms, we have utilized a database of nearly 31,000 ground motions with small and moderate moment magnitudes ranging from 3.0 to 5.8, recorded within a hypocentral distance of less than 200 km in CENA. Typically, for GMM development, analysts employ linear regression-based models with predefined functional forms. The requirement for predefined functional forms can restrict the use of complicated and nonlinear equations to improve performance. Although the conventional regression model is more interpretable, machine learning can achieve a better result given sufficient training data. The results of error metrics reveal that gradient-boosting regression provides a better performance. Furthermore, a machine learning ensemble method is used to combine the regression results of four machine learning algorithms. The ensemble method improves the GMM performance and provides smoother results.

MONITORING OF CHANGES IN THE STRESS-STRAIN STATE OF GEOENVIRONMENT AT THE PETROPAVLOVSK GEODYNAMIC TESTING SITE BASED ON THE MULTI-INSTRUMENTAL BOREHOLE AND GPS DATA DURING THE ACTIVE PHASE OF PREPARING THE ZHUPANOVSKY EARTHQUAKE (JANUARY 30, 2016, Mw 7.2)

The paper presents the results of comparative analysis of the data regarding the multi-instrumental borehole monitoring data of changes in the geoenvironment stress-strain state and GPS data obtained in the time vicinity of the close strong Zhupanovsky earthquake. The purpose this study was to assess the feasibility of using GPS measurements on routine basis for effective monitoring of strong Kamchatka earthquakes in the area of Petropavlovsk-Kamchatsky. The Zhupanovsky earthquake active phase was chosen as a "test" time interval for comparing GPS and borehole monitoring data. This earthquake has been the strongest seismic event since 2000 in terms of the ratio of the earthquake focus length to the hypocentral distance. The time series borehole electromagnetic and geoacoustic data were compared with the dilatation series reflecting the relative changes in the area of a triangle composed of Kamchatka GPS observation network located in the Petropavlovsk geodynamic testing site. The analysis indicates a high degree of consistency in these time series. GPS data obtained during the active phase of the earthquake preparation is agree with the results of mathematical modeling expected values of volumetric strain on the daylight surface on the eve of the earthquake. A joint analysis of borehole and GPS data made it possible to specify the time limits for the stages of change in the stress-strain state of geoenvironment and to resolve ambiguity of interpretation of the electromagnetic and geoacoustic borehole measurement results at the final stage of preparing the Zhupanovsky earthquake.

Ground motion prediction model for shallow crustal earthquakes in Japan based on XGBoost with Bayesian optimization

Ground motion prediction is an important and complex research subject in earthquake engineering and traditional approaches based on statistical regression have much room for improvement in prediction accuracy. Utilizing the 67,164 ground motion records from KiK-net and K-Net for 777 shallow crustal earthquakes between 1997 and 2019 in Japan, this paper proposes a ground motion prediction model XGBoost-SC based on the machine learning algorithm of eXtreme Gradient Boosting (XGBoost) for Japan. Magnitude, focal depth, hypo-central distance, Vs30, site altitude, and focal mechanism were used as the feature parameters, and XGBoost, Random Forest, and Deep Neural Networks (DNN) algorithms were selected for model training while Bayesian optimization was used to search for optimized hyperparameters to improve the prediction accuracy. XGBoost algorithm was selected for further study based on the comparison of results from the three algorithms. Residual change with magnitude and hypo-central distance, the probability distribution of residuals, residual standard deviation (σ), residual mean squared error (MSE), and Pearson correlation coefficient (R) were used as the evaluation parameters, and a comparison study was performed against the ground motion prediction equation based on traditional approaches. Actual earthquake events were selected to compare the prediction results against the observation records. To further validate and explain the proposed model, SHapley Additive exPlanations (SHAP) analysis was performed to explain the impact of selected feature parameters on the proposed model. The results demonstrate that the proposed XGBoost-SC model has good prediction stability for all periods, and its residual errors are smaller than those of other models. Therefore, the proposed model can better reflect the ground motion attenuation for shallow crustal earthquakes in Japan and can serve as a better model for ground motion prediction in future aseismic design and earthquake disaster mitigation efforts.

Attenuation Relationships for the Horizontal Component of Peak Ground Acceleration (PGAH) Using Gene Expression Programing (GEP) and Group Method of Data Handling (GMDH)

Earthquake is one of the natural disasters that has always influenced human life. It is not currently possible to predict exactly when and where an earthquake will occur, nor how large it will be. It is also impossible to prevent an earthquake. However, by designing seismic-resistant structures, the amount of financial losses and casualties can be reduced. This resistant design requires the use of earthquake risk analysis. By using the earthquake risk analysis, it will be possible to estimate the parameters of the strong ground motion, including acceleration, velocity, and displacement in each area. Estimating the parameters of strong ground motion will be possible just by obtaining the appropriate attenuation relationship. The aim of this paper is to present an appropriate attenuation relationship to estimate the horizontal component of the possible occurrence of peak ground acceleration in each region. Two methods were used to calculate attenuation relationship: gene expression programing (GEP) and group method of data handling (GMDH). In the first step, an up-to-date and comprehensive catalog consisting of 1,185 earthquake records that occurred around the world has been prepared. In the next step, the parameters of magnitude, hypocentral distance, and shear wave velocity of these records have been used as variables of the attenuation relationship. Then, the fitness function (f) was determined, and attenuation relationships were calculated using GEP and GMDH. The amount of fitness function (f) was obtained 766.12 from 1,000 in the GEP method and 767.77 from 1,000 in the GMDH method. The values of the fitness function, residuals and comparison plots showed a high-agreement between “the values predicted by the attenuation relationships” and “the actual values observed in the earthquakes.” Finally, according to the results of this research, it can be said that the use of GEP and GMDH methods has provided better results than the other similar researches. Also, the use of up-to-date records makes the results of this research more reliable than the previous researches.

COMPARISON OF EARTHQUAKE HYPOCENTER RELOCATION METHODS IN THE JAVA REGION, INDONESIA

Java is a frequent island of earthquakes caused by the subduction of the Indo-Australian plate under the Eurasian plate. The mitigation of earthquake disasters is by relocating the hypocenter. Hypocenter relocation was conducted in order to obtain a more accurate earthquake position, which can be used to interpret the structure under the earth's surface. The Modified Joint Hypocenter Determination (MJHD) and Double Difference (DD) methods are required when applying earthquake hypocenter relocation. The objective of research is comparing the relocation results in the Java region with the Modified Joint Hypocenter Determination (MJHD) and Double Difference (DD) Methods for obtaining the distribution of earthquake hypocenters before and after relocation. The study has obtained arrival time data in the form of BMKG catalog data from the period of January 1, 2022 through May 30, 2022 in the Java area with the coordinates of 6.0 LS - 12.0 LS and 106.0 BT - 114.0 BT. The MJHD method compares the travel time difference of a set of data that occurs simultaneously in order to obtain a more accurate hypocenter position, while the DD method compares the hypocenter distance of the two earthquakes with the station distance, so that Ray Path and Waveform are considered equal. The relocation of the initial earthquake has a depth of 10 km, resulting in a shift of shallow earthquakes clustered in the active fault section, while deep earthquakes are clustered in the subduction zone section. The results of relocation with the MJHD method obtained 637 earthquakes.

Macroseismic intensity attenuation models calibrated in Mw for Italy

This study aims at developing new macroseismic intensity attenuation models valid for Italy by exploiting the most updated macroseismic dataset and earthquakes catalogue, as well as the information obtained from a critical analysis of the most recent models in the literature. Several different attenuation models have been calibrated as a function of the moment magnitude (Mw) and epicentral distance from 16,260 intensity data points, that are related to 119 earthquakes occurred after 1900. According to trends and residuals analysis, the preferred calibrated intensity attenuation function is a Log-Linear model for epicentral distance (Repi in km) and a linear model for Mw as:IMCS=1.81-2.61LogR-0.0039R+1.42Mw\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$I\\left( {MCS} \\right) = {1}.{81} - {2}.{61}LogR - 0.00{39}R + {1}.{42}Mw$$\\end{document}with pseudo hypocentral distance R=Repi2+9.872\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R = \\sqrt {R_{epi}^{2} + \\left( {9.87} \\right)^{2} }$$\\end{document}; the estimated standard deviation is σ = 0.75. Also noteworthy is another model for macroseismic intensity attenuation that proved to be as good as the best model and shows higher sensitivity to physical parameters, such as focal depth and magnitude, especially in the epicentral area. Performance of all calibrated models was also checked on an independent set of 15 post-1900 Italian earthquakes. One of the results of the present work is the opportunity to define earthquake scenarios (e.g. probabilistic seismic hazard maps) in terms of Macroseismic Intensity and its related standard deviation, avoiding the uncertainties due to the conversion of various ground shaking parameters into intensity values.

Double distance dependence in high-frequency ground motion along the plate boundary in Northern Chile

In subduction zones, the forearc crust transitions from a highly fractured wedge near the trench to a less fractured basement near the volcanic arc. Here we study the role of wedge integrity on the frequency content of strong ground motion produced by subduction earthquakes in Northern Chile. Our data includes aftershocks from the 2014 Mw 8.1 Iquique earthquake and intermediate-depth seismicity, including the 2005 Mw 7.6 Tarapacá earthquake. We focus on the S-wave spectral decay parameter κ obtained from strong ground motion in the frequency band 2−40Hz, paying particular attention to how κ varies with hypocentral distance. We report two distinct trends of κ versus hypocentral distance. One trend applies to events near the trench, where we observe a rapid increase of κ with hypocentral distance. Another trend applies to events near the bottom of the megathrust and greater depths, where we observe a relatively slow increase of κ with hypocentral distance. We interpret this difference as resulting from a heterogeneous tectonic structure with lateral variations in anelastic attenuation (i.e., attenuation seems stronger in the fractured wedge near the trench). Our results improve our understanding of the role of high-frequency ground motion in seismic hazard analysis near the Chilean coastline. In addition, our results stress the need for a better characterization of anelastic attenuation in the Chilean subduction zone, specially close to the highly fractured wedge near the trench.

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 &amp;lt; 7).

Ground motion prediction equation for NW Himalaya and its surrounding region

An attenuation relationship, also known as a Ground Motion Prediction Equation (GMPE), has been developed for the Northwest Himalaya and its surrounding region. This GMPE utilizes strong motion data recorded by two networks: the National Center for Seismology Network and the strong motion network installed by Indian Institute of Technology Roorkee named PESMOS. For the development of this GMPE, 309 strong motion records from 86 earthquakes that occurred between 2005 and 2021 were considered. The selected earthquakes have magnitudes ranging from 3.0 to 6.9. In this study, the average of Peak Ground Acceleration (PGA) of two horizontal components was used to develop the new attenuation relationship. A multi-regression model was chosen for the analysis. The proposed attenuation relationship is.Log10 PGA = 1.889 + 0.3996 × M - 0.95736 log10 (HD + exp (0.4114 × M)) ± 0.3646Where, PGA is peak horizontal acceleration in gal of strong ground motion, M is the magnitude, and HD is the hypocentral distance from the source. The standard error of the proposed relationship is 0.3646. This newly developed attenuation relationship holds significance for various applications, including site-specific studies, seismic hazard estimation, ground motion simulation, earthquake early warning (EEW), and engineering applications. It provides valuable insights into the behavior of ground motion in the Northwest Himalayan region, allowing for better preparedness and mitigation strategies against seismic hazards in the area.

Regional spectral characteristics, quality factor and site responses in western-central Sichuan, China (I): Application of parametric generalized inversion technique

To study the source, site amplification and seismic wave attenuation characteristics in the Sichuan area, 1242 ground motion recordings corresponding to 62 earthquake events with Ms 4.3–6.4 recorded by 55 observation stations in the study area were selected. The source spectra, site effects and frequency-related quality factors in the western Sichuan area were inverted by the generalized inversion technique. Then, the source parameters, such as corner frequency, seismic moment and stress drop, were estimated by a grid-searching tool. In the generalized inversion method, a bedrock station was used to remove the trade-off between the site and source components. The inverted quality factor of 0.25–20 Hz frequency band is characterized as Q(f) = 219.4f0.7383. The results indicate that the inverted source spectrum is well matched with the ω2 source model, in which the stress parameter values range between 0.2 MPa and 3.5 MPa, with an average value of 1.2 MPa and source radii between 0.3 and 4 km. In addition, a comparison between the inversion results obtained from the horizontal-to-vertical spectral ratio and the generalized inversion methods indicates that these two approaches actually describe the same level of site effect, while the averaged site effect obtained by the generalized inversion method is always greater than the result obtained by the horizontal-to-vertical spectral ratio method. Finally, a residual defined as a function of hypocentral distance and moment magnitude was used to investigate whether there was any bias in the inversion results. The results indicate that the geometric spreading and source model used in this study resulted in an underestimation of the Fourier amplitude level in the far field and for large earthquakes. The results of this study can be applied in ground motion simulation and prediction to help the study of seismic disasters and risk assessment in the study area.

Regional spectral characteristics, quality factor and site responses in western-central Sichuan, China (II): Application to stochastic ground motion simulation

The stochastic finite-fault approach was used to model the 2022 Luding earthquake with a magnitude of Mw 6.6 in China at 16 selected near-field stations. To investigate the impact of the site effect on the synthetic results, two models were taken into account: One model was obtained from the generalized inversion technique, and the other model was obtained from the horizontal-to-vertical spectral ratio method. The high-frequency attenuation parameter was estimated to be 0.040 s and the value of 4.0 MPa for the stress parameter was adopted. Comparisons of the recorded and simulated Fourier amplitude spectrum, pseudospectral acceleration, peak ground acceleration, and peak ground velocity were performed to investigate the capability of the selected input parameters. In addition, the model bias, simulated peak ground acceleration and peak ground velocity were calculated by the local site amplification estimated by the two methods. The results show that the site effects estimated by the generalized inversion technique can well simulate the high-frequency response spectra and Fourier amplitude spectrum and can provide peak ground acceleration and peak ground velocity values consistent with the recorded values, while the local site amplification roughly calculated by the horizontal-to-vertical ratio method underestimates the main ground motion characteristics. Furthermore, strong-motion records with the same magnitude but different hypocentral distance ranges in the generalized inversion technique are used to estimate near-surface site effects, which shows that the distance range has a small impact on the estimation of local site amplification. Model parameters that performed well in this study provide confidence in understanding and quantifying seismic hazards in the Luding area from earthquake scenarios with different magnitudes.