Intensity Conversion Equations Research Articles

Studying Past Earthquakes with Modern Techniques: Ground-Motion Simulations for the 11 January 1693 Noto Earthquake in Italy

Abstract The 1693 Noto earthquake, which struck on 11 January, is one of the Italy’s largest and most devastating earthquakes. According to the Italian Parametric Earthquake Catalogue, it reached a maximum intensity of 11 on the Mercalli–Cancani–Sieberg scale and had an estimated magnitude of M 7.3. Nevertheless, its precise location and source definition remain subjects to debate due to the complexity of the seismic sequence and lack of geological evidence. A series of potential seismic sources differing in location, dimension, and kinematics have been proposed in the literature based on seismotectonic data and interpretations. The goal of this work is to perform a retrospective experiment to verify which of the proposed seismic sources have a better fit with the observed intensity data. To do so, novel simulation techniques are used to study this historical earthquake. We generated ground-motion scenarios for each proposed source model through a stochastic finite-fault simulation approach. Then, the simulated ground-motion parameters were converted to intensities using two different ground-motion intensity conversion equations for Italy. Finally, we compared these converted intensities with the observed intensity data in terms of normalized root mean square errors and converted intensities from ground-motion models. Our results generally show good consistency between converted intensities from the simulated and predicted ground motions, whereas the observed intensities fit better to converted ones from the peak ground velocity rather than peak ground acceleration. Our analysis reveals that the source model reproducing the best of the macroseismic data of the Noto earthquake is the Canicattì–Villasmundo fault system with a magnitude of 7.1.

The ShakeMap Atlas of Historical Earthquakes in Italy: Configuration and Validation

Abstract Italy has a long tradition of studies on the seismic history of the country and the neighboring areas. Several archives and databases dealing with historical earthquake data—primarily intensity data points—have been published and are constantly updated. Macroseismic fields of significant events are of foremost importance in assessing earthquake effects and for the evaluation of seismic hazards. Here, we adopt the U.S. Geological Survey (USGS)-ShakeMap software to calculate the maps of strong ground shaking (shakemaps) of 79 historical earthquakes with magnitude ≥6 that have occurred in Italy between 1117 and 1968 C.E. We use the macroseismic data published in the Italian Macroseismic Database (DBMI15). The shakemaps have been determined using two different configurations. The first adopts the virtual intensity prediction equations approach (VIPE; i.e., a combination of ground-motion models [GMMs] and ground-motion intensity conversion equations [GMICEs]; Bindi, Pacor, et al., 2011; Oliveti et al., 2022b). The second exploits the intensity prediction equations (IPE; Pasolini, Albarello, et al., 2008; Lolli et al., 2019). The VIPE configuration has been found to provide more accurate results after appraisal through a cross-validation analysis and has been applied for the generation of the ShakeMap Atlas. The resulting maps are published in the Istituto Nazionale di Geofisica e Vulcanologia (INGV) ShakeMap (see Data and Resources; Oliveti et al., 2023), and in the Italian Archive of Historical Earthquake Data (ASMI; see Data and Resources; Rovida et al., 2017) platforms.

Uncertainties in Intensity-Based Earthquake Magnitude Estimates

Abstract Estimating the magnitude of historical earthquakes is crucial for assessing seismic hazard. Magnitudes of early-instrumental earthquakes can be inferred using a combination of instrumental records, field observations, and the observed distribution of shaking intensity determined from macroseismic observations. For earthquakes before 1900, shaking intensity distributions often provide the only information to constrain earthquake magnitude. Considerable effort has been made to develop methods to estimate the magnitude of moderate-to-large historical earthquakes using shaking intensities derived from macroseismic data. In this study, we consider earthquakes in California with known instrumental magnitudes to explore uncertainties in estimating the magnitude of historical earthquakes from intensity information alone. We use three California-specific intensity prediction equations (IPEs) and an IPE based on a global ground-motion model (GMM) to determine optimum intensity-based magnitudes for 33 moderate-to-large California earthquakes between 1979 and 2021. Intensity-based magnitudes are close to instrumental magnitudes on average. However, intensity-based magnitudes for individual events differ by as much as 2.2 magnitude units from instrumental magnitudes. This result reflects the weak dependence of ground motions and shaking intensities on moment magnitude and their strong dependence on stress drop. Considering the intensity distributions of the 1906 San Francisco and 1989 Loma Prieta earthquakes, we show that information that could constrain rupture length is discarded when considering only the 2D decay of intensity with distance. We also show that ground-motion intensity conversion equations used in a GMM-based approach may cause a systematic overestimation of large historical earthquake magnitudes. This study underscores both the reducible and potentially irreducible uncertainties associated with using intensity data to estimate magnitudes of historical earthquakes using IPEs and highlights the value of using additional information to constrain rupture dimensions. Using intensity observations alone, moment magnitude uncertainties are typically on the order of a full unit.

New Methodology for Unbiased Ground-Motion Intensity Conversion Equations

ABSTRACT Ground-motion intensity conversion equations (GMICEs) allow for conversions between ground-motion amplitude and shaking intensity. The current methods used to develop GMICEs model the dependence of the intensity on the peak ground-motion (PGM) parameter. For some models, there is a second step that models the magnitude and distance dependence of the residuals from the initial regression. We show that this approach for developing GMICEs works well for estimating the intensity from median ground motions, but for ground-motion values away from the medians, the intensities estimated by the GMICE can have large bias, with overprediction for positive PGM residuals and underprediction for negative PGM residuals. The bias is due to an implicit assumption in the current approach that there is a direct causal relation between intensity and the ground-motion parameter and that the residuals of the intensity and ground-motion parameter are fully correlated. We present two alternative methodologies for developing GMICEs that do not suffer from this bias. The first method includes the magnitude and distance scaling of the GMICE in the initial regression for the scaling with the PGM. The second method excludes the magnitude and distance terms but includes the number of standard deviations of the PGM (ϵ) as an additional parameter in the GMICE. Using a synthetic data set of intensity and peak ground acceleration values, we show that the GMICE developed using the proposed method leads to more accurate estimates of the intensity than current methods. We also discuss implications of using GMICEs based on the proposed methods for the evaluation of probabilistic hazard maps and as input to ShakeAlert estimates.

INGe: Intensity-ground motion data set for Italy

In this paper we present an updated and homogeneous earthquake dataset for Italy compiled by joining the intensities available in the Italian Macroseismic Database DBMI15 and the peak ground motion (PGM) parameters present in the Engineering Strong-Motion (ESM) accelerometric data bank. The database has been compiled through an extensive procedure of evaluation and revision based on two main steps: 1) the selection of the earthquakes in DBMI15 with homogeneous macroseismic intensities in terms of data sources and 2) the extraction of all the localities reporting intensity data which are located within 3 km from the accelerograph stations that recorded the data. The final dataset includes 519 intensity-PGM data pairs from 65 earthquakes and 227 stations in the time span 1972–2016. The reported intensities are expressed either in the Mercalli-Cancani- Sieberg (MCS) or the European macroseismic (EMS-98) scales. The events are characterized by magnitudes in the range 4.1–6.8 and depths in the range 0–55 km. Here, we illustrate the data collection and the properties of the database in terms of recording, event and station distributions as well as macroseismic intensity points. Furthermore, we discuss the most relevant features of engineering interest showing several statistics with reference to the most significant metadata (such as moment magnitude, several distance metrics, style of faulting etc). The dataset is expected to be useful for benchmarking existing and for developing new ground motion intensity conversion equations offering a common basis, and sparing the time and effort required for assembling to the interested researchers. The dataset is available at https://zenodo.org/record/4623732#.YNX-AZMzbdc.

Field Insights and Analysis of the 2018 Mw 7.5 Palu, Indonesia Earthquake, Tsunami and Landslides

A devastating Mw 7.5 earthquake and tsunami struck northwestern Sulawesi, Indonesia on 28 September 2018, causing over 4000 fatalities and severe damage to several areas in and around Palu City. Severe earthquake-induced soil liquefaction and landslides claimed hundreds of lives in three villages within Palu. The mainshock occurred at 18:03 local time at a depth of 10 km on a left-lateral strike-slip fault. The hypocenter was located 70 km north of Palu City and the rupture propagated south, under Palu Bay, passing on land on the west side of Palu City. The surface rupture of the earthquake has been mapped onshore along a 30 km stretch of the Palu-Koro fault. We present results of field surveys on the effects of the earthquake, tsunami and liquefaction conducted between 1–3 and 12–19 of October 2018. Seismic intensities on the Modified Mercalli Intensity (MMI) scale are reported for 375 sites and reach a maximum value of 10. We consolidate published tsunami runup heights from several field studies and discuss three possible interrelated tsunami sources to explain the variation in observed tsunami runup heights. Due to limited instrumentation, PGA and PGV values were recorded at only one of our field sites. To compensate, we use our seismic intensities and Ground Motion to Intensity Conversion Equations (GMICEs) and Ground Motion Prediction Equations (GMPEs) developed for similar tectonic regions. Our results indicate that the maximum predicted PGAs for Palu range from 1.1 g for GMICEs to 0.6 g for GMPEs.

New Ground Motion to Intensity Conversion Equations for China

In this study, we use the strong motion records and seismic intensity data from 11 moderate-to-strong earthquakes in the mainland of China since 2008 to develop new conversion equations between seismic intensity and peak ground motion parameters. Based on the analysis of the distribution of the dataset, the reversible conversion relationships between modified Mercalli intensity (MMI) and peak ground acceleration (PGA), peak ground velocity (PGV), and pseudo-spectral acceleration (PSA) at natural vibration periods of 0.3 s, 1.0 s, 2.0 s, and 3.0 s are obtained by using the orthogonal regression. The influence of moment magnitude, hypocentral distance, and hypocentral depth on the residuals of conversion equations is also explored. To account for and eliminate the trends in the residuals, we introduce a magnitude-distance-depth correction term and obtain the improved relationships. Furthermore, we compare the results of this study with previously published works and analyze the regional dependence of conversion equations. To quantify the regional variations, a regional correction factor for China, suitable for adjustment of global relationships, has also been estimated.

A Seismic Intensity Survey of the 16 April 2016 Mw 7.8 Pedernales, Ecuador, Earthquake: A Comparison with Strong-Motion Data and Teleseismic Backprojection

AbstractWe conducted a seismic intensity survey in Ecuador, following the 16 April 2016 Mw 7.8 Pedernales earthquake, to document the level of damage caused by the earthquake. Our modified Mercalli intensities (MMIs) reach a maximum value of VIII along the coast, where single, two, and multistory masonry and concrete designed buildings partially or completely collapsed. The contours of our MMI maps are similar in shape to the contour maps of peak ground acceleration (PGA) and peak ground velocity (PGV). A comparison of our seismic intensities with the recorded PGA and PGV values reveals that our MMI values are lower than predicted by ground-motion intensity conversion equations that are based on shallow crustal earthquakes. The image of the earthquake rupture obtained using teleseismic backprojection at 0.5–2.0 Hz is coincident with the region of maximum MMI, PGA, and PGV values, Thus, rapid calculation of backprojection may be a useful tool for guiding the deployment of emergency response teams following large earthquakes. The most severe damage observed was, primarily, due to a combination of poorly constructed buildings and site conditions.

Estimation of MCS intensity for Italy from high quality accelerometric data, using GMICEs and Gaussian Na\xefve Bayes Classifiers

Macroseismic intensity provides a qualitative description of seismic damage. It can be associated with Ground Motion Parameters (GMPs), which are extracted in near real-time from instrumental recordings during an earthquake. Several formulations of this empirical association exist in literature for Italy, mainly focusing on the relationship between intensity expressed on the Mercalli-Cancani-Sieberg (MCS) scale and peak ground acceleration or velocity. They are usually in the form of Ground Motion to Intensity Conversion Equations (GMICEs), which treat intensity as a continuous quantity. We propose an alternative approach, the Gaussian Naïve Bayes (GNB) classifiers, which allows to correctly treat intensity according to its ordinal definition. As a comparison, we also implement a modified version of the standard GMICE approach. We expand the existing database of GMP/MCS-intensity points with new, high-quality accelerometric data recorded in Italy in the period from 2002 to 2016 and resample the database by treating the intermediate intensities with half integer values (which are not meaningful in the MCS description) as both belong to the above and below full integer classes with an assigned weight. As a result, we estimate a new set of regression relations and GNB probability distributions between integer MCS intensity classes and eight GMPs (peak acceleration, velocity, displacement, Arias and Housner intensities, spectral acceleration at 0.3, 1.0 and 3.0 s). Results based on PGA and PGV are the most stable on the whole intensity scale. GNB models score better than GMICEs in terms of performance on unseen data and classification scores.

Revisiting California’s Past Great Earthquakes and Long-Term Earthquake Rate

ABSTRACTIn this study, we revisit the three largest historical earthquakes in California—the 1857 Fort Tejon, 1872 Owens Valley, and 1906 San Francisco earthquakes—to review their published moment magnitudes, and compare their estimated shaking distributions with predictions using modern ground-motion models (GMMs) and ground-motion intensity conversion equations. Currently accepted moment magnitude estimates for the three earthquakes are 7.9, 7.6, and 7.8, respectively. We first consider the extent to which the intensity distributions of all three earthquakes are consistent with a moment magnitude toward the upper end of the estimated range. We then apply a GMM-based method to estimate the magnitudes of large historical earthquakes. The intensity distribution of the 1857 earthquake is too sparse to provide a strong constraint on magnitude. For the 1872 earthquake, consideration of all available constraints suggests that it was a high stress-drop event, with a magnitude on the higher end of the range implied by scaling relationships, that is, higher than moment magnitude 7.6. For the 1906 earthquake, based on our analysis of regional intensities and the detailed intensity distribution in San Francisco, along with other available constraints, we estimate a preferred moment magnitude of 7.9, consistent with the published estimate based on geodetic and instrumental seismic data. These results suggest that, although there can be a tendency for historical earthquake magnitudes to be overestimated, the accepted catalog magnitudes of California’s largest historical earthquakes could be too low. Given the uncertainties of the magnitude estimates, the seismic moment release rate between 1850 and 2019 could have been either higher or lower than the average over millennial time scales. It is further not possible to reject the hypothesis that California seismicity is described by an untruncated Gutenberg–Richter distribution with a b-value of 1.0 for moment magnitudes up to 8.0.

New Ground Motion to Intensity Conversion Equations (GMICEs) for New Zealand

Abstract Macroseismic intensities play a key role in the engineering, seismological, and loss modeling communities. However, at present, there is an increasing demand for instrumental data-based loss estimations that require statistical relationships between intensities and strong-motion data. In New Zealand, there was an urgent need to update the ground motion to intensity conversion equation (GMICE) from 2007, developed prior to a large number of recent earthquakes including the 2010–2011 Canterbury and 2016 Kaikōura earthquake sequences. Two main factors now provide us with the opportunity to update New Zealand’s GMICE: (1) recent publication of New Zealand’s Strong-Motion Database, corresponding to 276 New Zealand earthquakes with magnitudes 3.5–7.8 and 4–185 km depths; and (2) recent generation of a community intensity database from GeoNet’s “Felt Classic” (2004–2016) and “Felt Detailed” (2016–2019) questionnaires, corresponding to around 930,000 individual reports. Ground-motion data types analyzed are peak ground velocity (PGV) and peak ground acceleration (PGA). The intensity database contains 67,572 felt reports from 917 earthquakes, with magnitudes 3.5–8.1, and 1797 recordings from 247 strong-motion stations (SMSs), with hypocentral distances of 5–345 km. Different regression analyses were tested, and the bilinear regression of binned mean strong-motion recordings for 0.5 modified Mercalli intensity bins was selected as the most appropriate. Total least squares regression was chosen for reversibility in the conversions. PGV provided the best-fitting results, with lower standard deviations. The influence of hypocentral distance, earthquake magnitude, and the site effects of local geology, represented by the mean shear-wave velocity in the first 30 m depth, on the residuals was also explored. A regional correction factor for New Zealand, suitable for adjustment of global relationships, has also been estimated.

Ground Motion to Intensity Conversion Equations for Iran

New empirical relations between macroseismic intensity and ground motion parameters including peak ground acceleration and velocity are developed using strong ground motion data and Modified Mercalli Intensity (MMI) information from earthquakes within Iran plateau. The strong motion data consists of 116 three-component waveforms of 23 earthquakes with Mw 5.1–7.3 occurred from 1977 to 2017. The intensity values for each ground motion record were assigned considering the location of accelerograph stations on the isoseismal maps. Simple predictive equations are obtained by fitting a linear model to the mean peak ground motion values applying least squares regression. However, visual inspection of residuals shows magnitude and distance dependency for this set of equations. Improved relationships between ground motion and intensity are derived by comprising magnitude and distance terms as predictive variables. The refined ground motion to intensity conversion equations show smaller variability than simple linear equations in predicting MMI values. Both proposed models are compared with similar relationships in Iran and other regions of the world. The observed discrepancies in relationships may reflect the differences in input data, especially the macroseismic intensity assignments as well as regional variability. The proposed relations can be used for rapid hazard assessments and loss estimation in Iran and surrounding regions.

Quantifying Uncertainties in Ground Motion-Macroseismic Intensity Conversion Equations. A Probabilistic Relationship for Western China

ABSTRACT We analyze the global and local uncertainties of ground motion-macroseismic intensity conversion equations and derive new probabilistic relationships between the macroseismic intensity and peak ground motion parameters (peak ground acceleration and peak ground velocity) for western China based on the assumption that the peak values of ground motion are randomly distributed for each MMI level, bounded by a normal distribution. For this purpose, a strong ground motion database consisting of 37 moderate- to large-magnitude earthquakes that occurred in western China between 1994 and 2017 is employed along with the corresponding modified Mercalli intensity (MMI) information inferred from isoseismal maps and earthquake damage reports. The mean value and standard deviation parameters in the probabilistic formulas are obtained from the result of the analysis of the local uncertainties of the data compiled for Western China. We propose a method for the rapid assessment of computer-generated intensity maps using the new probabilistic formulas. Our method composed of two steps. (1) According to a Bayesian formula, the possibility, expectation and standard deviation of the seismic intensity are calculated by the ground motion parameters recorded at a site station. (2) Furthermore, expectation and standard deviation maps of the seismic intensity throughout an area are obtained through spatial interpolation. Finally, an application is illustrated to validate this new methodology by presenting rapidly estimated (computed) intensity maps of the 8 December 2016 Hutubi (Ms 6.2) earthquake in western China.

The 1895 Ljubljana earthquake: can the intensity data points discriminate which one of the nearby faults was the causative one?

The earthquake (Mw 6 from the SHEEC defined by the MDPs) that occurred in the central part of Slovenia on 14 April, 1895, affected a broad region, causing deaths, injuries, and destruction. This event was much studied but not fully explained; in particular, its causative source model is still debated. The aim of this work is to contribute to the identification of the seismogenic source of this destructive event, calculating peak ground velocity values through the use of different ground motion prediction equations (GMPEs) and computing a series of ground motion scenarios based on the result of an inversion work proposed by Jukić in 2009 and on various fault models in the surroundings of Ljubljana: Vič, Želimlje, Borovnica, Vodice, Ortnek, Mišjedolski, and Dobrepolje faults. The synthetic seismograms, at the basis of our computations, are calculated using the multi-modal summation technique and a kinematic approach for extended sources, with a maximum peak ground velocity value of 1 Hz. The qualitative and quantitative comparison of these simulations with the macroseismic intensity database allows us to discriminate between various sources and configurations. The quantitative validation of the seismic source is done using ad hoc ground motion to intensity conversion equations (GMICEs), expressly calculated for this study. This study allows us to identify the most probable causative source model of this event, contributing to the improvement of the seismotectonic knowledge of this region. The candidate fault that has the lowest values of average differences between observed and calculated intensities and chi-squared is a strike slip fault with a toward-north rupture as the Ortnek fault.

Do French macroseismic intensity observations agree with expectations from the European Seismic Hazard Model 2013?

Probabilistic seismic hazard assessments are the basis of modern seismic design codes. To test fully a seismic hazard curve at the return periods of interest for engineering would require many thousands of years’ worth of ground-motion recordings. Because strong-motion networks are often only a few decades old (e.g. in mainland France the first accelerometric network dates from the mid-1990s), data from such sensors can be used to test hazard estimates only at very short return periods. In this article, several hundreds of years of macroseismic intensity observations for mainland France are interpolated using a robust kriging-with-a-trend technique to establish the earthquake history of every French mainland municipality. At 24 selected cities representative of the French seismic context, the number of exceedances of intensities IV, V and VI is determined over time windows considered complete. After converting these intensities to peak ground accelerations using the global conversion equation of Caprio et al. (Ground motion to intensity conversion equations (GMICEs): a global relationship and evaluation of regional dependency, Bulletin of the Seismological Society of America 105:1476–1490, 2015), these exceedances are compared with those predicted by the European Seismic Hazard Model 2013 (ESHM13). In half of the cities, the number of observed exceedances for low intensities (IV and V) is within the range of predictions of ESHM13. In the other half of the cities, the number of observed exceedances is higher than the predictions of ESHM13. For intensity VI, the match is closer, but the comparison is less meaningful due to a scarcity of data. According to this study, the ESHM13 underestimates hazard in roughly half of France, even when taking into account the uncertainty in the conversion from intensity to acceleration. However, these results are valid only for the acceleration range tested in this study (0.01 to 0.09 g).

A Seismic Intensity Survey of the 1 April 2014 M 8.2 Iquique, Chile, Earthquake and Tsunami, and a Comparison with Strong‐Motion Data

ABSTRACT We conducted a regional seismic intensity survey in northern Chile following the 1 April 2014 M 8.2 Iquique earthquake to document the level of damage caused by the earthquake and tsunami. We observed very limited damage at the 204 sites surveyed. The modified Mercalli intensities (MMIs) reached a maximum value of VI adjacent to the earthquake rupture plane. The contours of our MMI map are similar in shape to the contour maps of peak ground acceleration (PGA) and peak ground velocity (PGV). A comparison of our seismic intensities with the recorded PGA and PGV values reveals that our MMI values are one to two units lower than predicted by ground‐motion intensity conversion equations (GMICEs). We propose that this is due to the well‐engineered buildings and moderate levels of high‐frequency (0.5–5 Hz) ground shaking experienced during this earthquake. Whether existing GMICEs consistently overpredict seismic intensities for large subduction‐zone earthquakes deserves further study.

Modified Mercalli intensities for the M7.8 Kaikōura (New Zealand) 14 November 2016 earthquake derived from ‘felt detailed’ and ‘felt rapid’ online questionnaires

This paper describes the shaking intensity levels caused by the M7.8 Kaikōura earthquake of 14/11/2016 according to the information from the two current GeoNet online questionnaires, ‘Felt Detailed’ and ‘Felt RAPID’. A recently developed method to extract intensity levels at a community scale using ‘Felt Detailed’ data is used. These are compared with individual intensities from ‘Felt RAPID’ survey, instrumental intensities from two recent ground motion to intensity conversion equations, and traditional intensity assignments. While maximum Modified Mercalli instrumental, traditional, ‘Felt RAPID’ and individual ‘Felt Detailed’ intensities go up to 8, community intensities using ‘Felt Detailed’ mostly only go up to 5, with only four communities with MM 6-7. Reasons for this discrepancy include a) lack of data around the epicentre; b) few reports from this event compared to other smaller recent earthquakes; and c) lack of public awareness of ‘Felt Detailed” surveys, released shortly after the earthquake. In addition, only 47% of reports were used to calculate community intensities, based on a minimum requirement for robust calculation of 5 reports. Although ‘Felt RAPID’ provided a much larger number of reports (more than 15,000) for this earthquake compared to ‘Felt Detailed’ (3500), the reliability of the former may be compromised by their lack of detail. Results from this paper suggest that, when enough reports are submitted, ‘Felt Detailed’ can provide good quality data that can be used in tools such as the near-real time shaking intensity maps provided in ShakeMapNZ.

Empirical fatality model for Indonesia based on a Bayesian approach

Abstract An empirical fatality model for Indonesia has been developed by relating the macroseismic intensity to the fatality rate using compiled sub-district level fatality rate data and the numerically simulated ground-shaking intensity for four recent damaging events. The fatality rate data were compiled by collecting population and fatality statistics of the regions affected by the selected events. The ground-shaking intensity was numerically estimated by incorporating a finite-fault model of each event and local site conditions approximated by topographically based site amplifications. The macroseismic intensity distribution of each event was generated using ShakeMap software, combining a selected pair of ground motion prediction equations and ground motion to intensity conversion equations. The developed fatality model is a Bayesian generalized linear model in which the fatality rate is assumed to follow a mixture of Bernoulli and gamma distributions. The model was validated by calculating the fatalities in past events from the EXPO-CAT catalogue and comparing the estimates with the EXPO-CAT fatality records. Although the model can provide an estimate of the range of fatalities for future events, it needs ongoing refinement by the incorporation of additional fatality rate data from past and future events.

An Open‐Source Earthquake Early Warning Display

We present the earthquake early warning display (EEWD), an effort to build a free and open‐source software to display earthquake early warning (EEW) information. The EEWD design and development builds on the experience of the Swiss Seismological Service at ETH Zurich in running the ShakeAlert UserDisplay developed by Caltech in California. The EEWD is a client‐side end‐user software capable of (1) supporting alerts generated by the main EEW algorithms used in Europe, starting with the Virtual Seismologist and the PRobabilistic and Evolutionary early warning SysTem (PRESTo); (2) allowing configuration for regionalization of shaking parameter predictions, such as local ground‐motion prediction equations (GMPEs), ground‐motion intensity conversion equations (GMICEs), and amplification due to local site effects; and (3) supporting future developments for configuration according to particular end‐user requirements. In addition to real‐time operations, the EEWD can replay recorded real‐time earthquake alerts and play scenarios. The EEWD, including its source code, is freely distributed to the community of interested users, who are also welcome to contribute to further developments, in particular to the inclusion of custom GMPEs, GMICEs, and intensity prediction equations.

Ground Motion to Intensity Conversion Equations (GMICEs): A Global Relationship and Evaluation of Regional Dependency

We analyzed the regional dependence of ground motion to intensity conversion equations and derived a new global relationship to improve ground motion and intensity estimates for earthquake hazard applications, including those related to the ShakeMap system. For this purpose, we merged several databases collected by other authors in different geographical regions to highlight any systematic regional effects in the relationship between macroseismic intensities and both peak ground velocity and peak ground acceleration. Our database contains macroseismic intensities derived from expert assignments or from the “Did You Feel It?” database, paired with peak ground motions (PGM) from seismic stations. We constrain our intensity–ground‐motion pairs to those with a maximum 2 km separation. For each region, we derived invertible relationships between intensities and ground motion using an orthogonal regression. We also derived a global relationship to quantify the regional differences. We investigated the dependence of intensity on predictor variables such as PGM, magnitude, and hypocentral distance. Our analyses indicate that PGM is the most robust predictor variable of intensity. Within one standard deviation, our regional and global results are in agreement with the relations of Worden et al. (2012) for California, Faenza and Michelini (2010) for Italy, Tselentis and Danciu (2008) for Greece, and Atkinson and Kaka (2007) for central–eastern United States. The earthquakes in the study ranged in magnitude from 2.5 to 7.3, and the distances ranged from less than a kilometer to about 200 km from the epicenter. Online Material: Table summarizing published relationships and figures showing the relationship between peak ground acceleration and intensity.