Jean-Philippe Métaxian

Permanent tremor of Masaya Volcano, Nicaragua : Wave field analysis and source location

The Masaya Volcano, Nicaragua, is a basaltic caldera in a subduction zone. The permanent source of the volcanic tremor was located inside Santiago crater, at the lava lakes position and 400 m below the NE rim, and therefore corresponds to superficial magma activity. We used two tripartite arrays (90 m side), one semicircular array (r=120 m) in 1992, and two semicircular arrays (r=60 m) and a 2500 m long linear array radiating out from the source and on the flank of the crater in 1993. We used both a cross-spectrum method and a correlation method to determine the wave delay time between the reference station and the other stations of an array and to quantify the wave field. Using the delays therefore by intersecting the back azimuth wave directions from the arrays, we could pinpoint the source. Additionally, the correlation coefficients obtained as functions of frequency for the three components of motion confirm the inferred position of the source of tremor. The tremors wave field is composed of comparable quantities of dispersed Rayleigh and Love surface waves, whose phase velocities lie in the ranges 730–1240 m/s at 2 Hz and 330–550 m/s at 6 Hz. The dispersive phase velocities were inverted to obtain crustal structures with a minimal number of layers. The resulting velocity models are similar for the northern and southern parts of the volcano. After geometrical spreading corrections, Q2Hz=14 and Q3Hz=31 were determined along the northern linear array. The typical low velocities and low Q corresponding to the cone structure and are similar to those of other basaltic volcanoes like Puu Oo, Hawaii, and Klyuchevskoy, Kamchatka.

Seismicity related to the glacier of Cotopaxi Volcano, Ecuador

[1] Significant seismic activity is generally recorded on volcanoes covered by an icecap. This work was carried out in order to quantify the role of the glaciers in the generation of seismicity for Cotopaxi volcano. We compared the seismic activity registered on the glacier and on the rock near the snout of the north flank glacier. We focused on the analysis of low frequency events (<5 Hz) similar to volcanic LP events when recorded on rock base. The particle motion analysis helps to estimate source locations, which are distributed in crevasses areas. High incident angles suggest a superficial origin. These events are interpreted as icequakes for which we propose as source mechanism a fluid-driven crack model triggered by ice cracking or hydraulic transients. The low quality factor values estimated are consistent with the resonance of an ice crack filled with water. This work shows that low frequency icequakes can be confusingly taken as volcanic LP events.

Study of seismic site effects using H/V spectral ratios at Arenal Volcano, Costa Rica

By using data obtained with a linear array at Arenal volcano, we show that the H/V spectral ratio method can be profitably applied to detect site effects on volcanoes. Similar results are obtained when calculating spectral ratios with different types of seismo-volcanic signals (tremor, ambient noise, explosion quakes, LP events). We compare the H/V ratios with theoretical S-wave transfer functions calculated using velocity models obtained from seismic refraction studies. There is a good agreement when the H/V ratios display sharp peaks, indicating a close relationship between the ratios and the transfer function of the shallow structure. Furthermore, the main peaks of the spectral ratios are consistent with local amplification of seismic waves observed at the corresponding frequencies.

Source geometry from exceptionally high resolution Long Period event observations at Mt Etna during the 2008 eruption.

During the second half of June, 2008, 50 broadband seismic stations were deployed on Mt Etna volcano in close proximity to the summit, allowing us to observe seismic activity with exceptionally high resolution. 129 long period events (LP) with dominant frequencies ranging between 0.3 and 1.2 Hz, were extracted from this dataset. These events form two families of similar waveforms with different temporal distributions. Event locations are performed by cross-correlating signals for all pairs of stations in a two-step scheme. In the first step, the absolute location of the centre of the clusters was found. In the second step, all events are located using this position. The hypocentres are found at shallow depths (20 to 700 m deep) below the summit craters. The very high location resolution allows us to detect the temporal migration of the events along a dike-like structure and 2 pipe shaped bodies, yielding an unprecedented view of some elements of the shallow plumbing system at Mount Etna. These events do not seem to be a direct indicator of the ongoing lava flow or magma upwelling.

Sea-ice thickness measurement based on the dispersion of ice swell.

The dispersion of flexural waves propagating in the Arctic sea ice cover is exploited in order to locally measure the ice thickness. The observed dispersion, for waves filtered in the 4-20 s period interval, at up to 4 broad-band seismometers deployed in Spring 2007 near the North Pole, is compared to a parameterized model that accounts for a complex wavefield made of a superposition of independent plane waves with different amplitudes and back-azimuth angles. The parameterization, that includes finding the best modeled ice thickness, is performed by using the cross-correlation functions between the seismometers. The ice thickness is estimated to 2.5 ± 0.2 m for the ~1 km-large floe the seismic stations were deployed on, which is coherent with other, independent measurements at this site. This study thus demonstrates the feasibility of using broad-band seismometers deployed on the sea-ice in order to passively measure the ice thickness, without requiring active sources nor human intervention.

Low-frequency bursts of horizontally polarized waves in the Arctic sea-ice cover

We report the detection of bursts of low-frequency waves, typically f = 0.025 Hz, on horizontal channels of broadband seismometers deployed on the Arctic sea-ice cover during the DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies) experiment in spring 2007. These bursts have amplitudes well above the ambient ice swell and a lower frequency content. Their typical duration is of the order of minutes. They occur at irregular times, with periods of relative quietness alternating with periods of strong activity. A significant correlation between the rate of burst occurrences and the ice-cover deformation at the ∼400 km scale centered on the seismic network suggests that these bursts are caused by remote, episodic deformation involving shearing across regional-scale leads. This observation opens the possibility of complementing satellite measurements of ice-cover deformation, by providing a much more precise temporal sampling, hence a better characterization of the processes involved during these deformation events.

Short term precursors of Strombolian explosions at Yasur volcano (Vanuatu)

The seismic wavefield associated with Strombolian activity is usually dominated by explosion quakes (EQs), tremor, and various signals generated by surface phenomena. Looking at the seismicity recorded at Yasur volcano in 2008, we found that beside the transient events which occur simultaneously with surface explosions, the seismicity includes events related to a deeper process. These long period (LP) events form a family of similar events located below the southeastern part of the crater rim at a depth of about 700–1200 m below the summit. They are commonly followed by EQs with a variable delay. The examination of about 20,000 LP-EQ sequences at several stations near the summit shows that interevent delays follow distributions peaked around 11–12 s. This short delay compared to the relatively great source depth of the LPs favors a causal relationship linked to pressure transfer rather than gas slug propagation after nucleation at the LP source.

Earthquake location determination using data from DOMERAPI and BMKG seismic networks: A preliminary result of DOMERAPI project

DOMERAPI project has been conducted to comprehensively study the internal structure of Merapi volcano, especially about deep structural features beneath the volcano. DOMERAPI earthquake monitoring network consists of 46 broad-band seismometers installed around the Merapi volcano. Earthquake hypocenter determination is a very important step for further studies, such as hypocenter relocation and seismic tomographic imaging. Ray paths from earthquake events occurring outside the Merapi region can be utilized to delineate the deep magma structure. Earthquakes occurring outside the DOMERAPI seismic network will produce an azimuthal gap greater than 1800. Owing to this situation the stations from BMKG seismic network can be used jointly to minimize the azimuthal gap. We identified earthquake events manually and carefully, and then picked arrival times of P and S waves. The data from the DOMERAPI seismic network were combined with the BMKG data catalogue to determine earthquake events outside the Merapi region. ...

Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives

Environmental monitoring is a topic of increasing interest, especially concerning the matter of natural hazards prediction. Regarding volcanic unrest, effective methodologies along with innovative and operational tools are needed to monitor, mitigate, and prevent risks related to volcanic hazards. In general, the current approaches for volcanoes monitoring are mainly based on the manual analysis of various parameters, including gas leaps, deformations measurements, and seismic signals analysis. However, due to the large amount of data acquired by in situ sensors for long-term monitoring, manual inspection is no longer a viable option. As in many big data situations, classic machinelearning approaches are now considered to automatize the analysis of years of recorded signals, thereby enabling monitoring on a larger scale.

Machine learning for automatic classification of volcano-seismic signatures

The evaluation and prediction of volcanoes activities and associated risks is still a timely and open issue. The amount of volcano-seismic data acquired by recent monitoring stations is huge (e.g., several years of continuous recordings), thereby making machine learning absolutely necessary for their automatic analysis. The transient nature of the volcano-seismic signatures of interest further enforces the need of automatic detection and classification of such events. In this paper, we present a novel architecture for automatic classification of volcano-seismic events based on a comprehensive signal representation with a large feature set. To the best of our knowledge this is one of the first attempts to automatize the classification task of these signals. The proposed approach relies on supervised machine learning techniques to build a prediction model.