Educating for earthquake science and risk in a tectonically slowly deforming region

Over the past decade, scientists have been called to participate more actively in public education and outreach (E&O). This is particularly true in fields of significant societal impact, such as earthquake science. Local earthquake risk culture plays a role in the way that the public engages in educational efforts. In this article, we describe an adapted E&O program for earthquake science and risk. The program is tailored for a region of slow tectonic deformation, where large earthquakes are extreme events that occur with long return periods. The adapted program has two main goals: (1) to increase the awareness and preparedness of the population to earthquake and related risks (tsunami, liquefaction, fires, etc.), and (2) to increase the quality of earthquake science education, so as to attract talented students to geosciences. Our integrated program relies on activities tuned for different population groups who have different interests and abilities, namely young children, teenagers, young adults, and professionals.

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.