Volcano Seismology: Detecting Unrest in Wiggly Lines

Performance of a rotational sensor at Etna, Italy focusing on back-azimuth estimations of volcano-seismic events

10.5194/egusphere-egu21-10927 ◽

2021 ◽

Author(s):

Martina Rosskopf ◽

Eva P. S. Eibl ◽

Gilda Currenti ◽

Philippe Jousset ◽

Joachim Wassermann ◽

...

Keyword(s):

Rotation Rate ◽

Mount Etna ◽

Seismic Events ◽

Volcano Seismology ◽

Rotational Seismology ◽

Long Period ◽

Tectonic Events ◽

Back Azimuth ◽

Seismic Wavefield

The field of rotational seismology has only recently emerged. Portable 3 component rotational sensors are commercially available since a few years which opens the pathway for a first use in volcano-seismology. The combination of rotational and translational components of the wavefield allows identifying and filtering for specific seismic wave types, estimating the back azimuth of an earthquake, and calculating local seismic phase velocities.Our work focuses on back-azimuth calculations of volcano-tectonic and long-period events detected at Etna volcano in Italy. Therefore, a continuous full seismic wavefield of 30 days was recorded by a BlueSeis-3A, the first portable rotational sensor, and a broadband Trillium Compact seismometer located next to each other at Mount Etna in August and September of 2019. In this study, we applied two methods for back-azimuth calculations. The first one is based on the similarity of the vertical rotation rate to the horizontal acceleration and the second one uses a polarization analysis from the two horizontal components of the rotation rate. The estimated back-azimuths for volcano-tectonic events were compared to theoretical back-azimuths based on the INGV event catalog and the long-period event back-azimuths were analyzed for their dominant directions. We discuss the quality of our back azimuths with respect to event locations and evaluate the sensitivity and benefits of the rotational sensor focusing on volcano-seismic events on Etna regarding the signal to noise ratios, locations, distances, and magnitudes.

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Volcano seismology

Central America ◽

10.1201/9780203947043.ch39 ◽

2007 ◽

Author(s):

Emilio Talavera ◽

Wilfried Strauch ◽

Philippe Lesage ◽

Guillermo Alvarado ◽

√ÅNgel Rodr√çGuez ◽

...

Keyword(s):

Volcano Seismology

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Volcano Ambient Noise Interferometry in the Caribbean (VANIC)

10.5194/egusphere-egu2020-6058 ◽

2020 ◽

Author(s):

Reinoud Sleeman

Keyword(s):

Ambient Noise ◽

Seismic Velocity ◽

Volcano Seismology ◽

Base Level ◽

Noise Interferometry ◽

Single Station ◽

Cross Correlations ◽

The Caribbean ◽

Velocity Variations ◽

Seismic Networks

The hazardous stratovolcanoes in the Lesser Antilles island arc are monitored with sparse seismic networks. The application of ambient noise interferometry to monitor seismic velocity variations (dv/v) on data from such a sparse instrumented volcanic environment often is a challenge. For the purpose of monitoring it is important a) to analyse the applicability of, and differences between, cross- and single-station cross-correlations, b) to estimate the base level of seismic velocity variations during quiet times and c) to understand the characteristics. Within the EUROVOLC instrument &#8220;Transnational Access (TA)&#8221; a proposal called VANIC was supported to a) use and evaluate different types of ambient noise cross correlations (single stations vs. multiple stations; auto, cross and cross-component correlations) to be applied on seismic recordings from the Guadeloupe seismic network on La Soufriere, b) compare the results with dv/v base level estimates from the sparse Netherlands Caribbean network on The Quill and Mt. Scenery and c) start collaboration between OVSG and KNMI on both monitoring and research levels with a focus on volcano seismology. This presentation will focus is on the results obtained during the TA visit to OVGS.

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Volcano Seismology: An Introduction

Encyclopedia of Complexity and Systems Science ◽

10.1007/978-3-642-27737-5_607-2 ◽

2019 ◽

pp. 1-32

Author(s):

Vyacheslav M. Zobin

Keyword(s):

Volcano Seismology

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Multistage adaptive spectral subtraction of seismic signals

10.5194/egusphere-egu2020-4853 ◽

2020 ◽

Author(s):

Yousef Rajaeitabrizi ◽

Robabeh Salehiozoumchelouei ◽

Luca D'Auria ◽

José Luis Sánchez de la Rosa

Keyword(s):

Automatic Detection ◽

Noise Spectrum ◽

Spectral Subtraction ◽

Transient Signal ◽

Volcano Seismology ◽

Time Data ◽

Zero Crossing ◽

Signal Noise ◽

Band Pass ◽

Noise Ratio

The detection of microearthquakes is an important task in various seismological applications as volcano seismology, induced seismicity, and mining safety. In this work we have developed a novel technique in order to improve the quality and efficiency of STA/LTA based detection of microearthquakes. This technique consists of different stages of filtering employing an adaptive spectral subtraction method, which allows greatly improving the signal/noise ratio.The implemented technique consists in a preliminary band-pass filtering of the signal followed by different stages of an adaptive spectral subtraction. The spectral subtraction technique is a non-linear filtering which allows taking into account the actual noise spectrum shape. It allows achieving a good filtering even in cases where the signal and noise spectrum overlaps. In order to take into account of the temporal variation in the background noise spectrum, we designed an adaptive technique. We first divide the incoming signals into short temporal windows. Each window is classified as &#8220;noise only&#8221; or &#8220;meaningful signal&#8221; (which can be either a microearthquake or any other relevant transient signal) using different features as the signal energy and the zero-crossing rate. Windows classified as &#8220;noise only&#8221; are continuously accumulated in a dynamic buffer which allows the average noise spectrum to be estimated and updated in an adaptive manner. This technique can be applied on subsequent stages to further improve the signal/noise ratio. This technique has been implemented in Python for the automatic detection of the microearthquakes on both off-line and near-real time data.In order to check the efficiency of the results, we compared the results of an STA/LTA based automatic detection on the initial band-pass filtered signal and on the spectral subtracted signals after different stages of filtering. A notable improvement of the quality of the detection process is observed when repeated spectral subtraction stages are applied.&#160;We applied this procedure to seismic data recorded by Red S&#237;smica Canaria, managed by Instituto Volcanol&#243;gico de Canarias (INVOLCAN), on Tenerife (Canary Islands), comparing results from the proposed detection algorithm with standard approaches as well as with manual detections. We present an extensive statistical analysis of the results, determining the percentage of correct detections, novel detections, false positives and false negatives after each stage of filtering. First results have shown that this technique is also able to detect automatically microearthquakes which went undetected after a manual analysis.

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