Tsunami characteristics along the Peru-Chile trench: analysis of the 2015 Mw8.3 Illapel, the 2014 Mw8.2 iquique and the 2010 Mw8.8 maule tsunamis in the near-field

Tsunamis occur quite frequently following large magnitude earthquakes along the Chilean coast. Most of these earthquakes occur along the Peru-Chile Trench, one of the most seismically active subduction zones of the world. This study aims to understand better the characteristics of the tsunamis triggered along the Peru-Chile Trench. We investigate the tsunamis induced by the Mw8.3 Illapel, the Mw8.2 Iquique and the Mw8.8 Maule Chilean earthquakes that happened on September 16th, 2015, April 1st, 2014 and February 27th, 2010, respectively. The study involves the relation between the co-seismic deformation and the tsunami generation, the near-field tsunami propagation, and the spectral analysis of the recorded tsunami signals in the near-field. We compare the tsunami characteristics to highlight the possible similarities between the three events and, therefore, attempt to distinguish the specific characteristics of the tsunamis occurring along the Peru-Chile Trench. We find that these three earthquakes present faults with important extensions beneath the continent which result in the generation of tsunamis with short wavelengths, relative to the fault widths involved, and with reduced initial potential energy. In addition, the presence of the Chilean continental margin, that includes the shelf of shallow bathymetry and the continental slope, constrains the tsunami propagation and the coastal impact. All these factors contribute to a concentrated local impact but can, on the other hand, reduce the far-field tsunami effects from earthquakes along Peru-Chile Trench.

Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.