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A strong motion monitoring network records data that provide an excellent way to study how source, path, and site effects influence the ground motion, specifically in the near-source area. Such data are essential for updating seismic hazard maps and consequently building codes and earthquake-resistant design. This paper aims to present the Italian Strong Motion Network (RAN), describing its current status, employment, and further developments. It has 648 stations and is the result of a fruitful co-operation between the Italian government, regions, and local authorities. In fact, the network can be divided into three sub-networks: the Friuli Venezia Giulia Accelerometric Network, the Irpinia Seismic Network, and all the other stations. The Antelope software automatically collects, processes, and archives data in the data acquisition centre in Rome (Italy). The efficiency of the network on a daily basis is today more than 97%. The automatic and fast procedures that run in Antelope for the real-time strong motion data analysis are continuously improved at the University of Trieste: a large set of strong motion parameters and correspondent Ground Motion Prediction Equations allow ground shaking intensity maps to be provided for moderate to strong earthquakes occurring within the Italian territory. These maps and strong motion parameters are included in automatic reports generated for civil protection purposes.

Two major earthquakes of ML greater than 6.0 occurred in Taiwan in the first half of 2013. The vibrant shaking brought landslides, falling rocks and casualties. This paper presents a seismic network developed by National Taiwan University (NTU) with 401 Micro-Electro Mechanical System (MEMS) accelerators. The network recorded high quality strong motion signals from the two events and produced delicate shaking maps within one minute after the earthquake occurrence. The high shaking regions of the intensity map produced by the NTU system suggest damage and casualty locations. Equipped with a dense array of MEMS accelerometers, the NTU system is able to accommodate 10% signals loss from part of the seismic stations and maintain its normal functions for producing shaking maps. The system also has the potential to identify the rupture direction which is one of the key indices used to estimate possible damage. The low cost MEMS accelerator array shows its potential in real-time earthquake shaking map generation and damage avoidance.

A hazard assessment of the 1976 Guatemala earthquake (M = 7.5) was conducted to achieve a better definition of the seismic hazard. The assessment was based on the environmental effects that had effectively contributed to the high destructive impact of that event. An interdisciplinary approach was adopted by integrating: (1) historical data; (2) co-seismic geological effects in terms of Environmental Seismic Intensity (ESI) scale intensity values; and (3) ground shaking data estimated by a probabilistic/deterministic approach. A detailed analysis of primary and secondary effects was conducted for a set of 24 localities, to obtain a better evaluation of seismic intensity. The new intensity values were compared with the Modified Mercalli Intensity (MMI) and Peak Ground Acceleration (PGA) distribution estimated using a probabilistic/deterministic hazard analysis approach for the target area. Our results are evidence that the probabilistic/deterministic hazard analysis procedures may result in very different indications on the PGA distributions. Moreover, PGA values often display significant discrepancy from the macroseismic intensity values calculated with the ESI scale. Therefore, the incorporation of the environmental earth effects into the probabilistic/deterministic hazard analysis appears to be mandatory in order to achieve a more accurate seismic estimation.

By referring to the two strongest earthquakes of the 2016–2017 Central Italy seismic sequence, this paper presents a procedure to make shaking maps through empirical relationships between macroseismic intensity and ground-motion parameters. Hundreds of waveforms were processed to obtain instrumental ground-motion features which could be correlated with the potential damage intensities. To take into account peak value, frequency, duration, and energy content, which all contribute to damage, cumulative absolute velocity and Arias intensity were used to quantify the features of the ground motion. Once these parameters had been calculated at the recording sites, they were interpolated through geostatistical techniques on the whole struck area. Finally, empirical relationships were used for mapping intensities, i.e., potential effects on the built environment. The results referred to both earthquake scenarios that were analyzed and were also used for assessing the influence of the spatial coverage of the instrumental network. In fact, after the first events, the Italian seismic network was subjected to the addition and thickening of sensors in the epicentral area, especially. The results obtained by models only dependent on ground-motion parameters or even on the epicentral distance were compared with the official ShakeMaps and the observed intensities for assessing their reliability. Finally, some suggestions are proposed to improve the procedure that could be used for rapidly assessing ground shaking and mapping damage potential producing useful information for non-expert audience.

This study presents research toward the development of ground-shaking maps after a real earthquake, or for scenario earthquakes originating from seismic sources within and around the Sultanate of Oman. Major important earthquake sources that are important for the Sultanate of Oman are the Makran zone, the Zagros zone, the Zendan-Minab system, the Oman Mountain zone, the Owen fracture zone and the Gulf of Aden zone. The earthquakes that take place on these zones, particularly those from Makran, already resulted and are likely to result in ground-shaking levels that may be significant for the country. The hazard module of software package ELER was customized for use in the development of shake maps in the Sultanate of Oman. For this purpose, (1) major active faults and systems within and around Oman were defined and implemented; (2) ground-motion prediction equations suitable for use and representative of tectonic conditions in Oman were identified and implemented; (3) the effect of local site conditions in resulting ground-shaking levels was attended by implementing the Vs30 maps into ELER methodology; and (4) scripts were developed for the consideration of ground-motion data coming from strong motion stations and from seismometers in and around Oman. They were used in the adjustment of ground-motion distribution maps, such as peak ground acceleration, peak ground velocity and spectral acceleration maps produced using ground-motion prediction equations. Example runs of different scenarios reflecting the use of newly adopted information are presented.

This paper describes a rapid response and risk mitigation system Istanbul Natural Gas Distribution Network Seismic Risk Reduction Project (IGRAS) for the Istanbul Natural Gas Network (IGDAŞ). Upon the trigger signal received from the earthquake early warning system in Istanbul, the real-time algorithm at IGRAS system district regulators checks the threshold levels of ground-motion parameters and interrupts the gas flow if any exceedance is detected. Then the system: (1) produces almost real-time earthquake hazard maps by using on-line strong-motion data from the strong-motion network in Istanbul: (2) estimates the distribution of damage to the natural gas network; and (3) transfers these damage distribution maps to stakeholders to enable dispatching rapid response teams to high damage areas.

A key aspect of ground-shaking map calculation is represented by ground-motion predictive equations (GMPEs). In fact, ground-shaking maps obtained soon after an earthquake are calculated by integrating observed data and ground-motion estimates from GMPEs for those areas not covered by seismic stations. Empirical ground-motion models that are used to obtain these estimates refer primarily to strong ground-motion due to large earthquakes and cannot be properly used to estimate the effects of small magnitude seismic events. In this paper we calibrated GMPEs for low-magnitude earthquakes from data recorded at the seismographic stations of the Irpinia Seismic Network, in the Campania–Lucania region, Southern Italy. In particular, the available dataset is formed by peak ground acceleration (PGA) and velocity (PGV) parameters coming from 123 earthquakes (local magnitudes ranging between 1.5 and 3.2) recorded at 21 stations of the ISNet network at hypocentral distances from 3 km to about 100 km. The total number of peaks measurements is 875. This study is part of a research project, in collaboration with the Italian Department of Civil Protection and National Institute of Geophysics and Volcanology, aimed at producing ground-motion shaking maps.

Strong ground-shaking mapping soon after a moderate-to-large earthquake is crucial to recognize the areas that have suffered the largest damage and losses. These maps have a fundamental role for emergency services, loss estimation and planning of emergency actions by the Civil Protection Authorities. This is particularly important for areas with high seismic risk levels, such as the Campania-Lucania Region in southern Italy. Taking advantage of the Irpinia Seismic Network (ISNet), a recently installed dense and wide dynamic seismic network, we have developed a procedure for rapid estimation of ground-shaking maps after moderate-to-large earthquakes (GRSmap). This uses an optimal data gridding scheme designed to account for bi-dimensional features of strong ground-motion fields, such as directivity, radiation patterns and focal mechanisms, to which most damage can be correlated. The basis of the mapping technique is a triangulation procedure to locally correct predicted data at the triangle barycentres where their vertices correspond to seismic stations. The method has been tested off-line using a simulated M 6.6 earthquake located at the centre of ISNet and applied to data of the 23 November 1980 Irpina M 6.9 earthquake recorded by a sparse network. This has highlighted its ability to predict peak ground-motion parameters of large magnitude earthquakes with respect to the attenuation relationships.

The main purpose of this study is to obtain the damage scenario for residential buildings in the occurrence of a destructive earthquake (M = 7+) in the city area of Catania, Eastern Sicily, and to illustrate the comparative performance of two alternative methods used for this purpose. The methods are representative of two different approaches to estimating the seismic vulnerability of structures, i.e., an empirical approach based on statistical score assignments (widely used in Italy and other countries) and a more recent, mechanical approach that uses displacement limit states associated with well-defined thresholds of structural damage. A special concern for seismic vulnerability in Catania is caused by the fact that earthquake design norms were enforced in its municipal area only since 1981. We emphasise some typical problems encountered in earthquake scenario work, such as the difficulty of assembling a reliable building inventory, and the uncertainties inherent in the vulnerability assessments through different probabilistic assumptions. Different criteria for the representation of damage are applied and discussed. It is shown that the main scenarios obtained by the two methods are in reasonable agreement, provided a suitable percentile level for damage is chosen in the statistical score assignment approach.

Landslides are a key hazard in high-relief areas around the world and pose a risk to population and infrastructure. It is important to understand where landslides are likely to occur in the landscape to inform local analyses of exposure and potential impacts. Large triggering events such as earthquakes or major rain storms often cause hundreds or thousands of landslides, and mapping the landslide populations generated by these events can provide extensive datasets of landslide locations. Previous work has explored the characteristic locations of landslides triggered by seismic shaking, but rainfall induced landslides are likely to occur in different parts of a given landscape when compared to seismically induced failures. Here we show measurements of a range of topographic parameters associated with rainfall-induced landslides inventories, including a number of previously unpublished inventories which we also present here. We find that average upstream angle and compound topographic index are strong predictors of landslide scar location, while local relief and topographic position index provide a stronger sense of where landslide material may end up (and thus where hazard may be highest). By providing a large compilation of inventory data for open use by the landslide community, we suggest that this work could be useful for other regional and global landslide modelling studies and local calibration of landslide susceptibility assessment, as well as hazard mitigation studies.