Marianna Siino

In this work, we propose a statistical approach to evaluate the coverage of a network based on the spatial distribution of its nodes and the target information, including all those data related to the final objectives of the network itself. This statistical approach encompasses descriptive spatial statistics in combination with point pattern techniques. As case studies, we evaluate the spatial arrangements of the stations within the Italian National Seismic Network and the Italian Strong Motion Network. Seismic networks are essential tools for observing earthquakes and assessing seismic hazards, while strong motion (accelerometric) networks allow us to describe seismic shaking and to measure the expected effects on buildings and infrastructures. The capability of both networks is a function of an adequate number of optimally distributed stations. We compare the seismic network with the spatial distributions of historical and instrument seismicity and with the distribution of well-known seismogenic sources, and we compare the strong motion station distribution with seismic hazard maps and the population distribution. This simple and reliable methodological approach is able to provide quantitative information on the coverage of any type of network and is able to identify critical areas that require optimization and therefore address areas of future development.

Radon (²²²Rn) is a radioactive gas, which originates everywhere in rocks of Earth's crust through a process of natural decay of other radioactive elements. The monitoring of soil radon can be useful to address a multitude of geological and environmental issues. However, some difficulties arise when trying to use radon as earthquake precursor because the earthquake‐related anomaly cannot be easily and univocally discriminated from other anomalies of different origin. To explore the relationship between soil radon emission and seismicity, a local network is operating in southeastern Sicily (Italy). Its peculiarities are the uniform geological condition of the monitoring sites and the long data record with simultaneous measures of radon and the main climate variables. In this paper, we applied continuous wavelet transformation to a ≈ 3.5 years long soil radon time series to detect periodic variations. Results indicate the occurrence of cycles at annual, semiannual, and monthly periodicity, which are ascribable to the effects of the climatic‐environmental parameters. The periodic components have been modeled and the signal conveniently filtered. We show as the characterization of the long‐term behavior of radon signals is essential to recognize anomalies in radon emission, which can be related to geological/environmental phenomena. The methodology proposed in this work provides a reliable characterization of the radon time series and can be applied at various spatial/temporal scales, depending on the scientific objectives to be achieved. This approach can also represent the base for further analysis like the investigation of the modulation between the periodic components and short‐term forecasts.

We consider the problem of detection of features in the presence of clutter for spatio‐temporal point patterns. In previous studies, related to the spatial context, Kth nearest‐neighbor distances to classify points between clutter and features. In particular, a mixture of distributions whose parameters were estimated using an expectation‐maximization algorithm. This paper extends this methodology to the spatio‐temporal context by considering the properties of the spatio‐temporal Kth nearest‐neighbor distances. For this purpose, we make use of a couple of spatio‐temporal distances, which are based on the Euclidean and the maximum norms. We show close forms for the probability distributions of such Kth nearest‐neighbor distances and present an intensive simulation study together with an application to earthquakes.

The dynamics governing the movement of the radon are complex and dependent on many factors. In the present study, we characterise the nature of temporal variations of 2-hourly and daily radon measurements in several monitoring sites of the Italian Radon mOnitoring Network (IRON) in Italy. By means of continuous wavelet transformation, a spectral analysis in time-frequency domain is performed. The results reveal that there are sub-daily, daily and yearly persistent periodicities that are common for all the stations. We observe structural seasonal breaks, that occur at the same frequency but at distinct time. Variations in radon concentration and local temperature are studied in terms of frequency contents and synchronicity. When analysing several long time series together, it is evident that the phase difference at low frequency movements (365-day period) between the radon and local temperature time series is depending on the sites’ location and therefore strongly controlled by local factors. This could at least partially explain the apparently contrasting results available in the literature obtained investigating smaller dataset about the relationships between temperature and radon variations. On the other hand, results show that all radon time series are characterised by marked cycles at 1 and 365-days and less evident cycles at 0.5-day and 180-days. They would be all ascribable to environmental-climatic factors: the short-period cycles to temperature and pressure variations, the long-period cycles also to seasonal rainfall variations.

The detection of clustering structure in a point pattern is one of the main focuses of attention in spatiotemporal data mining. Indeed, statistical tools for clustering detection and identification of individual events belonging to clusters are welcome in epidemiology and seismology. Local second‐order characteristics provide information on how an event relates to nearby events. In this work, we extend local indicators of spatial association (known as LISA functions) to the spatiotemporal context (which will be then called LISTA functions). These functions are then used to build local tests of clustering to analyse differences in local spatiotemporal structures. We present a simulation study to assess the performance of the testing procedure, and we apply this methodology to earthquake data.