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Bueno, Angel
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- PublicationOpen AccessVINEDA—Volcanic INfrasound Explosions Detector Algorithm(2019-12-13)
; ; ; ; ; ; ; ; ; ; ; ; ; Infrasound is an increasingly popular tool for volcano monitoring, providing insights of the unrest by detecting and characterizing acoustic waves produced by volcanic processes, such as explosions, degassing, rockfalls, and lahars. Efficient event detection from large infrasound databases gathered in volcanic settings relies on the availability of robust and automated workflows. While numerous triggering algorithms for event detection have been proposed in the past, they mostly focus on applications to seismological data. Analyses of acoustic infrasound for signal detection is often performed manually or by application of the traditional short-term average/long-term average (STA/LTA) algorithms, which have shown limitations when applied in volcanic environments, or more generally to signals with poor signal-to-noise ratios. Here, we present a new algorithm specifically designed for automated detection of volcanic explosions from acoustic infrasound data streams. The algorithm is based on the characterization of the shape of the explosion signals, their duration, and frequency content. The algorithm combines noise reduction techniques with automatic feature extraction in order to allow confident detection of signals affected by non-stationary noise. We have benchmarked the performances of the new detector by comparison with both the STA/LTA algorithm and human analysts, with encouraging results. In this manuscript, we present our algorithm and make its software implementation available to other potential users. This algorithm has potential to either be implemented in near real-time monitoring workflows or to catalog pre-existing databases.325 17 - PublicationOpen AccessAdvances on the automatic estimation of the P-wave onset time.(2016)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;García, L. ;Álvarez, I. ;Benitez, C. ;Titos, M. ;Bueno, A. ;Mota, S. ;De La Torre, A. ;Segura, J. C. ;Aguacil, G. ;Diaz-Moreno, A. ;Prudencio, J. ;Garcia-Yeguas, A. ;Ibanez, J. M. ;Zuccarello, L.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Cocina, O.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Patane, D. ; ; ; ; ; ; ; ; ; ; ; ; ;; ; This work describes the automatic picking of the P-phase arrivals of the 3*10^6 seismic registers originated during the TOMO-ETNA experiment. Air-gun shots produced by the vessel “Sarmiento de Gamboa” and contemporary passive seismicity occurring in the island are recorded by a dense network of stations deployed for the experiment. In such scenario, automatic processing is needed given: (i) the enormous amount of data, (ii) the low signal-to-noise ratio of many of the available registers and, (iii) the accuracy needed for the velocity tomography resulting from the experiment. A preliminary processing is performed with the records obtained from all stations. Raw data formats from the different types of stations are unified, eliminating defective records and reducing noise through filtering in the band of interest for the phase picking. The advanced multiband picking algorithm (AMPA) is then used to process the big database obtained and determine the travel times of the seismic phases. The approach of AMPA, based on frequency multiband denoising and enhancement of expected arrivals through optimum detectors, is detailed together with its calibration and quality assessment procedure. Examples of its usage for active and passive seismic events are presented.277 454 - PublicationRestrictedPICOSS: Python Interface for the Classification of Seismic Signals(2020)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; Over the last decade machine learning has become increasingly popular for the analysis and characterization of volcano-seismic data. One of the requirements for the application of machine learning methods to the problem of classifying seismic time series is the availability of a training dataset; that is a suite of reference signals, with known classification used for initial validation of the machine outcome. Here, we present PICOSS (Python Interface for the Classification of Seismic Signals), a modular data-curator platform for volcano-seismic data analysis, including detection, segmentation and classification. PICOSS has exportability and standardization at its core; users can select automatic or manual workflows to select and label seismic data from a comprehensive suite of tools, including deep neural networks. The modular implementation of PICOSS includes a portable and intuitive graphical user interface to facilitate essential data labelling tasks for large-scale volcano seismic studies.279 7