An Automatic P-Phase Picking Algorithm Based on Adaptive Multiband Processing

Álvarez, Isaac; García, Luz; Mota, Sonia; Cortés, Guillermo Ruperto Martín; Benítez, Carmen; Torre, Ángel de la

doi:10.1109/lgrs.2013.2260720

Cited by 45 publications

(21 citation statements)

References 10 publications

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“…In this study, we applied automatic procedures for P-phase determination based on the Adaptive Multiband Picking Algorithm (AMPA) developed by Alvarez et al (2013). This software is characterized by its adaptability.…”

Section: P-wave First Arrival Picksmentioning

confidence: 99%

New Insights on Mt. Etna’s Crust and Relationship with the Regional Tectonic Framework from Joint Active and Passive P-Wave Seismic Tomography

et al. 2017

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In the Central Mediterranean region, the production of chemically diverse volcanic products (e.g., those from Mt. Etna and the Aeolian Islands archipelago) testifies to the complexity of the tectonic and geodynamic setting. Despite the large number of studies that have focused on this area, the relationships among volcanism, tectonics, magma ascent, and geodynamic processes remain poorly understood. We present a tomographic inversion of P-wave velocity using active and passive sources. Seismic signals were recorded using both temporary on-land and ocean bottom seismometers and data from a permanent local seismic network consisting of 267 seismic stations. Active seismic signals were generated using air gun shots mounted on the Spanish Oceanographic Vessel 'Sarmiento de Gamboa'. Passive seismic sources were obtained from 452 local earthquakes recorded over a 4-month period. In total, 184,797 active P-phase and 11,802 passive P-phase first arrivals were inverted to provide three different velocity models. Our results include the first crustal seismic active tomography for the northern Sicily area, including the Peloritan-southern Calabria region

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Section: P-wave First Arrival Picksmentioning

confidence: 99%

New Insights on Mt. Etna’s Crust and Relationship with the Regional Tectonic Framework from Joint Active and Passive P-Wave Seismic Tomography

et al. 2017

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“…Although considerable information has already been extracted from the dataset, there remains the potential for additional research given the high quality of the data, the volume of unanalysed (magnetic, gravity) or partially-analysed data (bathymetry), and the recent advances in the field of signal processing, from automatic signal recognition techniques 24–27 to new methods for the determination of first arrivals 28–30 . We expect that our data is of broad interest and may yield new and high-impact results that will add to the existing scientific impact of the experiment.…”

Section: Background and Summarymentioning

confidence: 99%

Database of multi-parametric geophysical data from the TOMO-DEC experiment on Deception Island, Antarctica

et al. 2017

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Deception Island volcano (Antarctica) is one of the most closely monitored and studied volcanoes on the region. In January 2005, a multi-parametric international experiment was conducted that encompassed both Deception Island and its surrounding waters. We performed this experiment from aboard the Spanish oceanographic vessel ‘Hespérides’, and from five land-based locations on Deception Island (the Spanish scientific Antarctic base ‘Gabriel de Castilla’ and four temporary camps). This experiment allowed us to record active seismic signals using a large network of seismic stations that were deployed both on land and on the seafloor. In addition, other geophysical data were acquired, including bathymetric high precision multi-beam data, and gravimetric and magnetic profiles. To date, the seismic and bathymetric data have been analysed but the magnetic and gravimetric data have not. We provide P-wave arrival-time picks and seismic tomography results in velocity and attenuation. In this manuscript, we describe the main characteristics of the experiment, the instruments, the data, and the repositories from which data and information can be obtained.

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“…In more advanced processing work-flows, this is followed by automatic classification of the signals by different methods, including Neural Networks (Scarpetta et al, 2005), pattern recognition (e.g., Curilem et al, 2014), Hidden Markov Models (Ibáñez et al, 2009), Support Vector Machines (Giacco et al, 2009), or statistical properties (Bueno et al, 2019). A wealth of new algorithms are constantly published in the literature in order to improve the efficiency of automatic detection and classification procedures for different types of signals, including those associated with tectonic earthquakes (Di Stefano et al, 2006;Álvarez et al, 2013;Bhatti et al, 2016), low-frequency volcano-seismic events (Frank and Shapiro, 2014), avalanches (Marchetti et al, 2015), and debris flows (Schimmel and Hübl, 2016). Collectively, these algorithms represent an important toolbox for the creation of high-quality research databases.…”

Section: Introductionmentioning

confidence: 99%

VINEDA—Volcanic INfrasound Explosions Detector Algorithm

et al. 2019

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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.

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An Automatic P-Phase Picking Algorithm Based on Adaptive Multiband Processing

Cited by 45 publications

References 10 publications

New Insights on Mt. Etna’s Crust and Relationship with the Regional Tectonic Framework from Joint Active and Passive P-Wave Seismic Tomography

New Insights on Mt. Etna’s Crust and Relationship with the Regional Tectonic Framework from Joint Active and Passive P-Wave Seismic Tomography

Database of multi-parametric geophysical data from the TOMO-DEC experiment on Deception Island, Antarctica

VINEDA—Volcanic INfrasound Explosions Detector Algorithm

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