Shaking Maps Based on Cumulative Absolute Velocity and Arias Intensity: The Cases of the Two Strongest Earthquakes of the 2016–2017 Central Italy Seismic Sequence

Costanzo, Antonio

doi:10.3390/ijgi7070244

Cited by 10 publications

(4 citation statements)

References 42 publications

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“…On the other hand, correlation between residuals — which is the focus of this study — generally requires additional modeling. For instance, a few studies, such as Foulser‐Piggott and Stafford, 17 Bradley, 23 Du and Wang, 24 Costanzo, 25 Bullock, 26 and Schiappapietra and Douglas 27 (hereafter denoted as FS2012, B2010, DW2013, C2018, B2019, and SD2020, respectively), have investigated the spatial correlation in integral IM residuals by employing classical geostatistical methods, through semivariograms within short interstation distances. These studies have shown that the spatial correlation model parameters vary greatly from event to event.…”

Section: Introductionmentioning

confidence: 99%

Correlation properties of integral ground‐motion intensity measures from Italian strong‐motion records

Huang

Tarbali

Galasso

2020

Earthq Engng Struct Dyn

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This study investigates the correlation properties of integral ground-motion intensity measures (IMs) from Italian strong-motion records. The considered integral IMs include 5-95% significant duration, Housner intensity, cumulative absolute velocity, and Arias intensity. Both IM spatial correlation and the correlation between different integral and amplitude-based IMs (i.e., cross-IM correlation) are addressed in this study. To this aim, a new Italian ground-motion model (GMM) with spatial correlation for integral IMs is first introduced. Based on the newly developed GMM, the empirical correlation coefficients from interevent and intraevent residuals are investigated and various analytical correlation models between integral IMs and amplitude-based IMs are proposed. The effective range parameter representing spatial correlation properties and the trend in the cross-IM correlations are compared with existing models in the literature. The variability of the effective range parameters with respect to event-specific features is also discussed. Modeling groundmotion spatial and cross-IM correlations is an important step in seismic hazard and risk assessment of spatially distributed systems. Investigating regionspecific correlation properties based on Italian strong-motion records is of special interest as several correlation models have been developed based on global datasets, often lacking earthquakes in extensional regions such as Italy.

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Section: Introductionmentioning

confidence: 99%

Correlation properties of integral ground‐motion intensity measures from Italian strong‐motion records

Huang

Tarbali

Galasso

2020

Earthq Engng Struct Dyn

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“…Figure 4 represents the workflow implemented to generate damage scenarios in case of an earthquake occurs. The workflow can be summarized in these steps: Once THIS detects a new earthquake with potential effects on the monitored areas, the earthquake time-histories are extracted and pre-processed as described in the previous paragraph; At this point, another control verifies if the maximum absolute acceleration is greater than 1 cm/s 2 ; in the affirmative case, the Python script calculates the rotation-invariant parameters of the ground-motion [ 54 ] to take into account frequency, duration, and energy content, which together can contribute to structural damage [ 55 ]: Peak Ground Acceleration, Pseudo-Spectral Accelerations, Arias Intensity [ 56 ], Standard Cumulative Absolute Velocity [ 57 ]. By interpolating the measured PGAs using the inverse distance weighted (IDW) technique [ 58 ] through the ArcPy module for Python [ 59 ], a map covering the whole built-up area is obtained.…”

Section: Methodsmentioning

confidence: 99%

“…Once THIS detects a new earthquake with potential effects on the monitored areas, the earthquake time-histories are extracted and pre-processed as described in the previous paragraph; • At this point, another control verifies if the maximum absolute acceleration is greater than 1 cm/s 2 ; in the affirmative case, the Python script calculates the rotation-invariant parameters of the ground-motion [54] to take into account frequency, duration, and energy content, which together can contribute to structural damage [55]: Peak Ground Acceleration, Pseudo-Spectral Accelerations, Arias Intensity [56], Standard Cumulative Absolute Velocity [57].…”

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confidence: 99%

A Technological System for Post-Earthquake Damage Scenarios Based on the Monitoring by Means of an Urban Seismic Network

Costanzo

Falcone

D’Alessandro

et al. 2021

Sensors

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A technological system capable of automatically producing damage scenarios at an urban scale, as soon as an earthquake occurs, can help the decision-makers in planning the first post-disaster response, i.e., to prioritize the field activities for checking damage, making a building safe, and supporting rescue and recovery. This system can be even more useful when it works on densely populated areas, as well as on historic urban centers. In the paper, we propose a processing chain on a GIS platform to generate post-earthquake damage scenarios, which are based: (1) on the near real-time processing of the ground motion, that is recorded in different sites by MEMS accelerometric sensor network in order to take into account the local effects, and (2) the current structural characteristics of the built heritage, that can be managed through an information system from the local public administration authority. In the framework of the EU-funded H2020-ARCH project, the components of the system have been developed for the historic area of Camerino (Italy). Currently, some experimental fragility curves in the scientific literature, which are based on the damage observations after Italian earthquakes, are implemented in the platform. These curves allow relating the acceleration peaks obtained by the recordings of the ground motion with the probability to reach a certain damage level, depending on the structural typology. An operational test of the system was performed with reference to an ML3.3 earthquake that occurred 13 km south of Camerino. Acceleration peaks between 1.3 and 4.5 cm/s2 were recorded by the network, and probabilities lower than 35% for negligible damage (and then about 10% for moderate damage) were calculated for the historical buildings given this low-energy earthquake.

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“…For each of the waveforms, the peak values of the acceleration can be easily obtained, allowing the construction of urban-scale shake maps. Figure 12 is a shake map obtained from the detected event, obtained using python code; for more clarification, refer to [3,24].…”

Section: Camerino Networkmentioning

confidence: 99%

Urban Seismic Network Based on MEMS Sensors: The Experience of the Seismic Observatory in Camerino (Marche, Italy)

Vitale

D’Alessandro

Benedetto

et al. 2022

Sensors

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Urban seismic networks are considered very useful tools for the management of seismic emergencies. In this work, a study of the first urban seismic network in central Italy is presented. The urban seismic network, built using MEMS sensors, was implemented in the urban district of Camerino, one of the cities in central Italy with the greatest seismic vulnerability. The technological choices adopted in developing this system as well as the implemented algorithms are shown in the context of their application to the first seismic event recorded by this innovative monitoring infrastructure. This monitoring network is innovative because it implements a distributed computing and statistical earthquake detection algorithm. As such, it is not based on the traces received by the stations from the central server; rather, each station carries out the necessary checks on the signal in real time, sending brief reports to the server in case of anomalies. This approach attempts to shorten the time between event detection and alert, effectively removing the dead times in the systems currently used in the Italian national network. The only limit for an instant alarm is the latency in the tcp/ip packages used to send the short reports to the server. The presented work shows the infrastructure created; however, there is not enough data to draw conclusions on this new early warning approach in the field, as it is currently in the data collection phase.

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Shaking Maps Based on Cumulative Absolute Velocity and Arias Intensity: The Cases of the Two Strongest Earthquakes of the 2016–2017 Central Italy Seismic Sequence

Cited by 10 publications

References 42 publications

Correlation properties of integral ground‐motion intensity measures from Italian strong‐motion records

Correlation properties of integral ground‐motion intensity measures from Italian strong‐motion records

A Technological System for Post-Earthquake Damage Scenarios Based on the Monitoring by Means of an Urban Seismic Network

Urban Seismic Network Based on MEMS Sensors: The Experience of the Seismic Observatory in Camerino (Marche, Italy)

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