Álvarez, Isaac

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.

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.

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 and both the Mt. Etna and Aeolian volcanic environments. The tomographic images provide a detailed and complete regional seismotectonic framework and highlight a spatially heterogeneous tectonic regime, which is consistent with and extends the findings of previous models. One of our most significant results was a tomographic map extending to 14 km depth showing a discontinuity striking roughly NW–SE, extending from the Gulf of Patti to the Ionian Sea, south-east of Capo Taormina, corresponding to the Aeolian–Tindari– Letojanni fault system, a regional deformation belt. Moreover, for the first time, we observed a high-velocity anomaly located in the south-eastern sector of the Mt. Etna region, offshore of the Timpe area, which is compatible with the plumbing system of an ancient shield volcano located offshore of Mt. Etna.

In this manuscript we present the new friendly seismic tomography software based on joint inversion of active and passive seismic sources called PARTOS (Passive Active Ray TOmography Software). This code has been developed on the base of two well-known widely used tomographic algorithms (LOTOS and ATOM-3D), providing a robust set of algorithms. The dataset used to set and test the program has been provided by TOMO-ETNA experiment. TOMO-ETNA database is a large, highquality dataset that includes active and passive seismic sources recorded during a period of 4 months in 2014. We performed a series of synthetic tests in order to estimate the resolution and robustness of the solutions. Real data inversion has been carried out using 3 different subsets: (i) active data; (ii) passive data; and (iii) joint dataset. Active database is composed by a total of 16,950 air-gun shots during 1 month and passive database includes 452 local and regional earthquakes recorded during 4 months. This large dataset provides a high ray density within the study region. The combination of active and passive seismic data, together with the high quality of the database, permits to obtain a new tomographic approach of the region under study never done before. An additional user-guide of PARTOS software is provided in order to facilitate the implementation for new users.

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.

In the present paper we describe the on-land field operations integrated in the TOMO-ETNA experiment carried out in June-November 2014 at Mt. Etna volcano and surrounding areas. This terrestrial campaign consists in the deployment of 90 short-period portable three-component seismic stations, 17 broadband seismometers and the coordination with 133 permanent seismic station belonging to Italy’s Istituto Nazionale di Geofisica e Vulcanologia (INGV). This temporary seismic network recorded active and passive seismic sources. Active seismic sources were generated by an array of air-guns mounted in the Spanish oceanographic vessel “Sarmiento de Gamboa” with a power capacity of up to 5200 cubic inches. In total more than 26,000 shots were fired and more than 450 local and regional earthquakes were recorded. We describe the whole technical procedure followed to guarantee the success of this complex seismic experiment. We started with the description of the location of the potential safety places to deploy the portable network and the products derived from this search (a large document including full characterization of the sites, owners and indication of how to arrive to them). A full technical description of the seismometers and seismic sources is presented. We show how the portable seismic network was deployed, maintained and recovered in different stages. The large international collaboration of this experiment is reflected in the participation of more than 75 researchers, technicians and students from different institutions and countries in the on-land activities. The main objectives of the experiment were achieved with great success.