Geodynamics of the Calabrian Arc area (Italy) inferred from a dense GNSS network observations

The tectonics and geodynamics of the Calabria region are presented in this study. These are inferred by precise computation of Global Navigation Satellite Systems （GNSS） per~ manent station velocities in a stable Eurasian reference framework. This allowed computation of the coordinates, variance and covariance matrixes, and horizontal and vertical velocities of the 36 permanent sites analyzed, together with the strain rates, and using different techniques. Interesting geodynamic phenomena are presented, including compressional, and deformational fields in the Tyrrhenian coastal sites of Calabria, extensional trends of the Ionian coastal sites, and sliding movement of the Crotone Basin. Conversely, on the northern Tyrrhenian side of the network near the Cilento Park area, the usual extensional tectonic perpendicular to the Apennine chain is observed. The large- scale pattern of the GNSS height velocities is shown, which is characterized by general interesting geodynamic vertical effects that appear to be due to geophysical movement and anthropic activity. Finally, the strain-rate fields computed through three different tech- niques are compared.

Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale

Machine learning is becoming increasingly important in scientific and technological progress,due to its ability to create models that describe complex data and generalize well.The wealth of publicly-available seismic data nowadays requires automated,fast,and reliable tools to carry out a multitude of tasks,such as the detection of small,local earthquakes in areas characterized by sparsity of receivers.A similar application of machine learning,however,should be built on a large amount of labeled seismograms,which is neither immediate to obtain nor to compile.In this study we present a large dataset of seismograms recorded along the vertical,north,and east components of 1487 broad-band or very broad-band receivers distributed worldwide;this includes 629,0953-component seismograms generated by 304,878 local earthquakes and labeled as EQ,and 615,847 ones labeled as noise(AN).Application of machine learning to this dataset shows that a simple Convolutional Neural Network of 67,939 parameters allows discriminating between earthquakes and noise single-station recordings,even if applied in regions not represented in the training set.Achieving an accuracy of 96.7,95.3,and 93.2% on training,validation,and test set,respectively,we prove that the large variety of geological and tectonic settings covered by our data supports the generalization capabilities of the algorithm,and makes it applicable to real-time detection of local events.We make the database publicly available,intending to provide the seismological and broader scientific community with a benchmark for time-series to be used as a testing ground in signal processing.

A Damage Scenario for the 2012 Northern Italy Earthquakes and Estimation of the Economic Losses to Residential Buildings

In May 2012 a seismic sequence occurred in Northern Italy that was characterized by two main shocks with a magnitude range between 5.5 and 6. These shocks represent a good case study by which to quantify the monetary losses caused by a moderate earthquake in a densely populated and economically well-developed area.The loss estimation accounts for damage to residential buildings, and considers the full effect of all the seismic aftershock events that lasted for nearly a month. The building damage estimation is based on the European Macroseismic Scale(EMS-98) definitions, which depict the effects of an earthquake on built-up areas in terms of observed intensities. Input data sources are the residential building census provided by Istituto Nazionale di Statistica—the Italian National Institute of Statistics(ISTAT)—and the official market value of real estate assets, obtained from the Osservatorio del Mercato Immobiliare—the Real Estate Market Observatory(OMI). These data make it possible to quantify the economic losses due to earthquakes, an economic indicator updated yearly. The proposed multidisciplinary method takes advantage of seismic,engineering, and economic data sets, and is able to provide a reasonable after the event losses scenario. Data are not gathered for each single building and the intensity values are not a simple hazard indicator, but, notwithstanding its coarseness, this method ensures both robust and reproducible results. As the local property value is availablethroughout the Italian territory, the present loss assessment can be effortlessly repeated for any area, and may be quickly reproduced in case of future events, or used for predictive economic estimations.