ABSTRACT
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.
Subject(s)
Earthquakes , Italy , Motion , SoftwareABSTRACT
Social media and crowdsourcing (SMCS) are increasingly proving useful for addressing the effects of natural and human-made hazards. SMCS allow different stakeholders to share crucial information during disaster management processes and to strengthen community resilience through engagement and collaboration. To harvest these opportunities there is a need for better knowledge on SMCS for diverse disaster scenarios. These challenges are being addressed within the LINKS Horizon 2020 project. The project aims at strengthening societal resilience by producing advanced learning on the use of SMCS in disasters. This is done through an in-depth study across three knowledge domains (disaster risk perception and vulnerability, disaster management processes, SMCS technologies), the establishment of an interactive framework, and an online platform in which a community of relevant stakeholders can learn and share knowledge and experiences. This paper provides an overview of the project objectives and approaches and a summary of the initial results.
ABSTRACT
In archaeological applications the accurate reconstruction of buried structures is mandatory. Electrical resistivity tomography is widely used for this purpose. Nevertheless, resistivity errors could be generated by wrong placement of electrodes. Papers in the literature do not discuss the influence of errors connected with the electrode position location (GPS-error). In this paper the first results of a Monte Carlo simulation analysis of data acquired on a tumulus are presented. The main research questions were: (i) if it is correct to ignore the GPS-error collect, and (ii) if a minimum threshold, that significantly affect the inversion, exists. Results, obtained considering planimetric GPS-errors of about one third of the fixed electrode distances, show that the GPS-errors affect resistivity, but the generated errors/anomalies: (a) are lower than that obtained without considering the topography, and (b) are significant from a numerical point of view, but do not affect the interpretation, being compatible with the soil resistivity ranges.
