Array analyses of seismic noise at Mt. Vesuvius Volcano, Italy

Saccorotti, Gilberto; Maresca, Rosalba; Pezzo, E. Del

doi:10.1016/s0377-0273(01)00204-9

Cited by 29 publications

(24 citation statements)

References 20 publications

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“…The events are located with a 3D location program [ Lomax et al , 2001] using the 3D velocity model described by Scarpa et al [2002]. All the doublets are band‐pass filtered between 4–15 Hz to reduce the environmental noise due to the highly urbanized surroundings [ Saccorotti et al , 2001]. The waveforms are aligned at the P‐wave arrival and we visually check the similarity of the P‐onset, as an additional constraint on the similarity of the source function.…”

Section: Methodsmentioning

confidence: 99%

Coda wave interferometric detection of seismic velocity changes associated with the 1999 M = 3.6 event at Mt. Vesuvius

Pandolfi

Bean

Saccorotti

2006

Geophysical Research Letters

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[1] We detect seismic wave velocity changes at Mt. Vesuvius, using doublets and the Coda Wave Interferometry method. The high sensitivity of multiply scattered coda waves to temporal changes in the medium allows us to detect velocity variation smaller than 0.4%. We use 17 doublets, some of them grouped in families of multiplets, spanning January 1996 to December 1999. Data show a systematic increase in velocity from 1996 to endSeptember 1999, followed by a rapid drop in velocity. This drop immediately precedes a sustained swarm of VT-type earthquakes, including the 9th October 1999 M = 3.6 event, the largest in the region since at least 1972. We propose a long term fluid pressurization followed by influx as a possible causative mechanism. Citation: Pandolfi, D., C. J. Bean, and G. Saccorotti (2006), Coda wave interferometric detection of seismic velocity changes associated with the 1999 M = 3.6 event at Mt. Vesuvius, Geophys. Res. Lett., 33, L06306,

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Section: Methodsmentioning

confidence: 99%

Coda wave interferometric detection of seismic velocity changes associated with the 1999 M = 3.6 event at Mt. Vesuvius

Pandolfi

Bean

Saccorotti

2006

Geophysical Research Letters

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“…First, we sought to compare the seismic activity in both areas; and second we wanted to determine the source position by applying a joint location technique [ La Rocca et al , 2000; Almendros et al , 2000]. In the end, this second objective proved unfeasible, due to the great difference in the shallow velocity structure under both arrays, which seriously affected the estimates of the slowness vector [ Saccorotti et al , 2001b]. Taking into account the extremely complex lateral structure beneath the Obsidianas array [ Saccorotti et al , 2001a; F. Luzón et al, Shallow structure of Deception Island volcano, Antarctica, using the two station spatial autocorrelation method on a dense set of seismic arrays, submitted to Geophysical Journal International , 2009], and following Ibáñez et al [2003b], we decided to consider only data recorded in the Fumarole array.…”

Section: Instrumentsmentioning

confidence: 99%

“…Array techniques have been used to locate seismic events, investigate the temporal evolution of the source, analyze the characteristics of seismic noise, and study the details of the local structure. They have been applied at volcanoes such as Stromboli [ Chouet et al , 1997; La Rocca et al , 2004], Etna [ Saccorotti et al , 2004], Vesuvius [ Saccorotti et al , 2001b], Teide [ Del Pezzo et al , 1997; Almendros et al , 2000, 2007], Masaya [ Métaxian et al , 1997], Arenal [ Métaxian et al , 2002; Mora et al , 2006], Kilauea [ Almendros et al , 2001a, 2001b], Copahue [ Ibáñez et al , 2008], and Colima [ Palo et al , 2009]. The use of array techniques to obtain spatial locations is based on three different approaches: circular wavefronts; ray tracing; and joint location with various arrays.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the information on the apparent slowness vector provided by the array, we need to know the arrival times of P and S phases to determine the distance along the seismic ray. This requirement means that the technique can only be applied to earthquakes with clear phases [e.g., Del Pezzo et al , 1997; Almendros et al , 2000, 2007; Saccorotti et al , 2001b]. Finally, the third method is based on the use of several arrays.…”

Section: Introductionmentioning

confidence: 99%

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Characterization of fracture systems using precise array locations of earthquake multiplets: An example at Deception Island volcano, Antarctica

Carmona¹,

Almendros²,

Peña³

et al. 2010

J. Geophys. Res.

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[1] Volcano-tectonic earthquakes are common seismic events in active volcanic areas. The stress produced by volcanic processes is released through fracturing of the shallow crust. Very often, these earthquakes occur in multiplets with similar waveforms, a fact which indicates common source characteristics. In this work, we introduce a method that uses array techniques to calculate precise relative locations of earthquake multiplets. We use the relative slowness estimate method to determine accurately the apparent slownesses and propagation azimuths of the earthquakes relative to a selected master event.We also obtain precise estimates of the S-P delays. This information is used to calculate precise relative locations by ray tracing in an Earth model. We applied this method to determine the characteristics of the fractures activated during the 1999 seismic series at the Deception Island volcano, Antarctica. We selected a set of 17 earthquake multiplets, initially located in a small (4 × 4 km) region a few km NE of the array site. We estimated precise locations for 14 of the clusters. In most cases, hypocenters were distributed in well-defined planar geometries. We found the best fitting planes, which we interpreted as fractures in the medium. For two clusters, the method spatially separated the earthquakes into two subgroups. Thus, we obtained two planes for each of these clusters, resulting in a total of 16 fracture planes. This is the first time that the orientations of fracture planes related to a seismic series have been obtained using a seismic array. We performed several tests to check various aspects in relation to the stability of the method and concluded that the results were robust. The dip angles indicate that the planes are mostly subvertical, while the strike angles clearly show a NW-SE trend for most of the planes and a few planes with NE-SW trends. The geometry and position of these planes suggest that the 1999 seismic series was influenced by regional tectonics, although the origin of the destabilization of the system may be related to the reactivation of a shallow magma chamber.

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“…This approach is attractive and economic as it relies on the background seismic noise and does not require any particular source. Earlier analyses of the dispersive properties of surface waves in active volcanoes were performed by Ferrazzini et al (1991) at the Puu Oo crater, Hawaii; Métaxian et al (1997) at the Masaya Volcano, Nicaragua; Chouet et al (1998) at the Stromboli Volcano, Italy; Saccorotti et al (2001a) at the Deception Island Volcano, Antarctica; and Saccorotti et al (2001c) at the Vesuvius Volcano, Italy.…”

Section: Introductionmentioning

confidence: 99%

Shallow-velocity models at the Kilauea Volcano, Hawaii, determined from array analyses of tremor wavefields

Saccorotti¹,

Chouet

Dawson

2003

Geophysical Journal International

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SUMMARY The properties of the surface wavefield at Kilauea Volcano are analysed using data from small‐aperture arrays of short‐period seismometers deployed in and around the Kilauea caldera. Tremor recordings were obtained during two Japan–US cooperative experiments conducted in 1996 and 1997. The seismometers were deployed in three semi‐circular arrays with apertures of 300, 300 and 400 m, and a linear array with length of 1680 m. Data are analysed using a spatio‐temporal correlation technique well suited for the study of the stationary stochastic wavefields of Rayleigh and Love waves associated with volcanic activity and scattering sources distributed in and around the summit caldera. Spatial autocorrelation coefficients are obtained as a function of frequency and are inverted for the dispersion characteristics of Rayleigh and Love waves using a grid search that seeks phase velocities for which the L‐2 norm between data and forward modelling operators is minimized. Within the caldera, the phase velocities of Rayleigh waves range from 1400 to 1800 m s−1 at 1 Hz down to 300–400 m s−1 at 10 Hz, and the phase velocities of Love waves range from 2600 to 400 m s−1 within the same frequency band. Outside the caldera, Rayleigh wave velocities range from 1800 to 1600 m s−1 at 1 Hz down to 260–360 m s−1 at 10 Hz, and Love wave velocities range from 600 to 150 m s−1 within the same frequency band. The dispersion curves are inverted for velocity structure beneath each array, assuming these dispersions represent the fundamental modes of Rayleigh and Love waves. The velocity structures observed at different array sites are consistent with results from a recent 3‐D traveltime tomography of the caldera region, and point to a marked velocity discontinuity associated with the southern caldera boundary.

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Array analyses of seismic noise at Mt. Vesuvius Volcano, Italy

Cited by 29 publications

References 20 publications

Coda wave interferometric detection of seismic velocity changes associated with the 1999 M = 3.6 event at Mt. Vesuvius

Coda wave interferometric detection of seismic velocity changes associated with the 1999 M = 3.6 event at Mt. Vesuvius

Characterization of fracture systems using precise array locations of earthquake multiplets: An example at Deception Island volcano, Antarctica

Shallow-velocity models at the Kilauea Volcano, Hawaii, determined from array analyses of tremor wavefields

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