AUTOMATIC ANALYSIS OF SEISMIC DATA BY USING NEURAL NETWORKS: APPLICATIONS TO ITALIAN VOLCANOES.

The availability of the new computing techniques allows to perform advanced analysis in near real time, improving the seismological monitoring systems, which can extract more significant information from the raw data in a really short time. However, the correct identification of the events remains a critical aspect for the reliability of near real time automatic analysis. We approach this problem by using Neural Networks (NN) for discriminating among the seismic signals recorded in the Neapolitan volcanic area (Vesuvius, Phlegraean Fields). The proposed neural techniques have been also applied to other sets of seismic data recorded in Stromboli volcano. The obtained results are very encouraging, giving 100% of correct classification for some transient signals recorded at Vesuvius and allowing the clustering of the large dataset of VLP events recorded at Stromboli volcano

Automatic Detectors for Underwater Soundscape Measurements

Environmental impact regulations require that marine industrial operators quantify their contribution to underwater noise scenes. Automation of such assessments becomes feasible with the successful categorisation of sounds into broader classes based on source types – biological, anthropogenic and physical. Previous approaches to passive acoustic monitoring have mostly been limited to a few specific sources of interest. In this study, source-independent signal detectors are developed and a framework is presented for the automatic categorisation of underwater sounds into the aforementioned classes

Earthquake source characterization by machine learning algorithms applied to acoustic signals

Underwater seismic events generate acoustic radiation (such as acoustic-gravity waves), that carries information about the source and can travel long distances before dissipating. Effective early warning, emergency response, and information dissemination for earthquakes and tsunamis require a rapid characterisation of the fault properties: geometry and dynamics. In this work, we analysed hydrophone recordings of 201 earthquakes, located in the Pacific and the Indian Ocean, by employing acoustic signal processing and classification methods. The analysis allows identifying the type of earthquake (i.e. slip type, magnitude) and provides near real-time estimation of the effective properties of the fault dynamics and geometry. The results were compared against values reported by the Harvard Global Centroid Moment Tensor catalog (gCMT), revealing statistical significance between the extracted acoustic properties used to feed machine learning algorithms and the predicted slip and magnitude values

Integrated optimization of underwater acoustic ship-radiated noise recognition based on two-dimensional feature fusion

Abstract(#br)Feature fusion methods are introduced to ship-radiated noise recognition in this paper. Wavelet packet (WP) decomposition is used to decompose the ship-radiated noise into multiple different subbands. By considering the features extracted from the different subbands reflecting different characteristics of the ship-radiated noise, a two-dimensional feature fusion (2DFF) scheme is proposed to fuse the features extracted from the different subbands. Principal component analysis (PCA) and canonical correlation analysis (CCA) are used in the 2DFF scheme. Then, a so-called discriminative ability improving (DAI) strategy is proposed to improve the discriminative ability of the extracted features. Starting at the 2DFF, a processing chain of feature fusion and ship-radiated noise recognition is designed and jointly optimized to the task. The 2DFF scheme and DAI strategy are tested on real ship-radiated noise data recorded. Experimental results indicate that compared with the baseline, the 2DFF scheme can improve 7.25% of recognition accuracy. Experimental results also show that the DAI strategy can further improve the recognition accuracy of 13.10%

Machine Learning-powered Artificial Intelligence in Arms Control

Artificial intelligence (AI), especially AI driven by machine learning, is on everyone’s lips. Even in armaments such systems are playing an increasingly important role: Some weapons systems are already able to identify targets independently and engage in combat with them. This poses problems for traditional forms of arms control originally designed to monitor physical objects such as mines and small arms and their internal function. In addition, important additional effects of reliable control such as confidence- building and stabilization of diplomatic relations are not addressed. It is important for arms control to address such risks as well. At the same time, the deployment of Machine Learning-powered Artificial Intelligence (MLpAI) as a tool offers tremendous potential for improving arms control processes. Here, more precise and comprehensive data processing can engender more trust between states in particular. This tension between the risks and the opportunities connected with the use of MLpAI in arms control is highlighted in this report