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VOLGEO-04240; No of Pages 16 ARTICLE IN PRESS Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx Contents lists available at ScienceDirect Journal of Volcanology and Geothermal Research j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / j v o l g e o r e s Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004 Francesca Martini a,⁎, Christopher J. Bean a,d, Gilberto Saccorotti b, Fatima Viveiros c, Nicolau Wallenstein c a Seismology and Computational Rock Physics Lab., School of Geological Sciences, University College Dublin, Belfield, Dublin 4, Ireland Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, Via U. della Faggiola, 32, 56126 Pisa, Italy Centro de Vulcanologia e Avaliação de Riscos Geológicos, Universidade dos Açores, Edifício do Complexo Científico, Rua da Mãe de Deus, Apartado 1422, 9501-801 Ponta Delgada, Açores, Portugal d Complex and Adaptive Systems Lab., (CASL), University College Dublin, Dublin 4, Ireland b c a r t i c l e i n f o Article history: Received 16 June 2008 Accepted 9 January 2009 Available online xxxx Keywords: velocity changes rainfall volcano seismicity triggered seismicity Azores archipelago a b s t r a c t Fogo volcano is an active central volcano, with a lake filled caldera, in the central part of São Miguel Island, Azores, whose current activity is limited to hydrothermal manifestations such as active fumarolic fields, thermal and CO2 cold springs and soil diffuse degassing areas. It is affected by important active tectonic structures, with high seismic activity and practically continuous micro-seismicity. A recurrent feature from the seismicity observed in volcanic regions is the occurrence of clusters of similar earthquakes, whose origin can be attributed to the repeated action of a similar source mechanism at the same focal area. Doublets/multiplets were identified in this study within a catalogue of small magnitude (usually b 3) volcano tectonic events recorded in 2003–2004 by a selection of stations around Fogo volcano. All events have been cross-correlated and pairs whose waveforms exhibited a cross-correlation coefficient equal to or higher than 0.9 were analysed using the coda-wave interferometry technique. Subtle velocity variations found between events highlight a seasonal cycle of the velocity patterns, with lower velocity in winter time and higher velocity during summer months. Those results, together with quantitative differences between the same doublets at different stations, exhibit an excellent correlation with rainfall. A seasonal effect can also be broadly seen in the seismicity occurrence, and some of the swarms recorded over the two year period occur during the wettest season or close to episodes of abundant (above average) rainfall. Moreover, temporal and spatial analysis of several swarms highlighted the lack of any mainshock– aftershock sequence and organized migration of the hypocenters. This is suggestive of a very heterogeneous stress field. Vp/Vs is found to be lower than usually observed in volcanic areas, an occurrence likely related to the presence of steamy fluid associated with the geothermal system. Taken together, these observations suggest that pore pressurisation plays a major role in controlling a considerable part of the recorded seismicity. The geothermal fluids around Fogo massif have been identified as derived from meteoric water, which infiltrates through Fogo Lake and the volcano flanks and flows from south to north on the northern flank. All those elements seem to point to a role played by rainfall in triggering seismicity at São Miguel, possibly through pressure changes at depth in response to surface rain and/or an interaction with the geothermal system. © 2009 Elsevier B.V. All rights reserved. 1. Introduction The Agua de Pau/Fogo Massif, usually called Fogo volcano, occupies the central part of São Miguel Island, Azores, located near the Mid Atlantic Ridge at the triple junction between the North American, Eurasian and African lithospheric plates. Fogo is an active central volcano with a lake filled caldera, composed of a succession of basaltic to trachytic lava flows, trachytic domes, cinder cones, pyroclastic flows, ⁎ Corresponding author. Tel.: +353 1 716 2137; fax: +353 1 2837733. E-mail addresses: [email protected] (F. Martini), [email protected] (C.J. Bean), [email protected] (G. Saccorotti), [email protected] (F. Viveiros), [email protected] (N. Wallenstein). lahars, pumice and ash deposits (Moore, 1991; Wallenstein, 1999: Wallenstein et al., 2007a). The earliest stage of its activity has been dated at 280,000 ± 140,000 years BP (Muecke et al., 1974) from a borehole sample on the northern flank. There have been at least four eruptions in the last 5000 years from this centre (Booth et al., 1978, Wallenstein, 1999), mostly explosive, intercalated with more effusive eruptions on satellite centres on its flanks. The most recent eruptions occurred in 1563 A.D. (Weston, 1964) with a sub-plinian intracaldera eruption followed by a basaltic flank eruption (Walker and Croasdale, 1971; Wallenstein, 1999); current activity is limited to hydrothermal manifestations such as active fumarolic fields, thermal and CO2 cold springs and soil diffuse degassing. The hydrothermal manifestations of Fogo volcano are mainly present in the northern flank of the 0377-0273/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jvolgeores.2009.01.015 Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015 ARTICLE IN PRESS 2 F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx volcano: the main fumarole grounds can be observed in Caldeira Velha, Pico Vermelho and Caldeiras da Ribeira Grande. Their location is associated with the NNW–SSE fault system that defines the socalled Ribeira Grande graben. The volcano lies at the intersection of the main regional tectonic structures with NW–SE, NNW–SSE and WNW–ESE directions, and circular and sub-circular faults associated with the caldera collapses (Wallenstein, 1999). It is therefore affected by important active tectonic structures: it is characterized by high seismic activity and practically continuous micro-seismicity, concentrated in very frequent swarms (Nunes, 1991). In the period analysed within this study (2003– 2004) the swarms are characterised by hundreds of low magnitude (Md b 3) events occurring over a few hours (Silva et al., 2005; Wallenstein et al., 2007b). Understanding the origin of this type of seismicity is of primary importance. In particular, it is crucial to understand whether the periods of increased seismicity are related to episodes of magmatic unrest, as it would provide a substantial contribution to the assessment of volcanic hazards on the island. As a first step in this direction, in the present study we analyse families of repeating seismic events identified within the 2003–2004 seismic catalogue recorded in an areas of approximately 20 × 20 km centred on Fogo volcano. The aim is to monitor velocity changes within the volcano structure that might be associated with volcanic activity and/or external parameters influencing the volcanic system. 2. Monitoring of volcanic activity in São Miguel Island: data available to this study Due to its peculiar geodynamic setting, the Azores archipelago is the location of important seismo-volcanic activity. The islands were affected by several major earthquakes and about 30 volcanic eruptions over the past five hundred years (Weston, 1964; Silveira et al., 2003). Fig. 1. a) location of São Miguel, Azores, b) map of São Miguel Island, with location of the permanent CO2 soil flux stations; fumaroles location and geographical references are also marked, c) seismic stations in the central part of São Miguel, around Fogo volcano, including those from the SIVISA permanent seismic network and those deployed during the field experiment in April–July 2003. Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015 ARTICLE IN PRESS F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx Presently the Centro de Vulcanologia e Avaliação de Riscos Geológicos (CVARG), a multidisciplinary research unit within the University of Azores and a member of the World Organization of Volcano Observatories (WOVO), monitors the seismic and volcanoseismic activity, fluids geochemistry, ground deformation and volcano-magmatic processes in the Azores. On S. Miguel Island, the permanent seismological network consists of 19 stations (16 analogue, with 3-D and 1-D short period sensors, and 3 digital, 2 with 3D short period and one with broadband sensors), located throughout the island (Fig. 1) and telemetred to the CVARG recording centre in Ponta Delgada. Nine 3D-1 Hz short period analogue permanent stations are deployed around Fogo Volcano. Additionally, a temporary network was deployed from April to mid July 2003, in the framework of the EU project e-Ruption (Saccorotti et al., 2004; Fig. 1). The sparse network consisted of 14 stations equipped with both short period and broad-band seismometers, and three dense arrays of short period instruments. The aim of the field experiment was to obtain a continuous record of the seismicity to characterise the present level of activity of Fogo and its neighbouring Furnas volcano (on the eastern part of São Miguel) and to obtain broadband recordings of distant sources for investigating the presence of shallow velocity anomalies beneath Fogo volcano, possibly associated with the existence of a shallow magma chamber (Saccorotti et al., 2004; Silva, 2004; 3 Silva et al., 2005; Zandomeneghi, 2007; Zandomeneghi et al., 2008; Bonagura et al., Characteristic of recent seismicity at Central São Miguel Island, Azores, Manuscript in preparation, hereinafter referred to as Bonagura et al., in preparation). Regular fumarole sampling, soil CO2 concentration/flux and soil radon measurements are performed by the CVARG Gas Geochemistry Unit. Four soil CO2 flux continuous monitoring stations are running at São Miguel Island, based on the accumulation chamber method (Chiodini et al., 1998). One of those, set up in 2002, is on the northern flank of Fogo volcano, within the Pico Vermelho geothermal area (Fig. 1). The station also includes additional sensors to record (on an hourly basis) information on meteorological and environmental parameters such as barometric pressure, air temperature, air humidity, wind speed and direction, rainfall, soil water content and soil temperature (Ferreira et al., 2005). Monthly rainfall data are also provided by a series of udometric stations managed by the DROTRH (Direcção Regional do Ordenamento do Território e Recursos Hídricos; Fig. 12). The influence of external factors on soil CO2 flux at the permanent stations is gauged by applying multivariate regression analysis (Draper and Smith, 1981). The results obtained show that the meteorological variables play different roles in the control of the gas flux depending on the selected monitoring site (Viveiros, 2003; Viveiros et al., 2003; Viveiros et al., 2008). Meteorological variables may explain between Fig. 2. Time evolution of the daily number of events (top) and corresponding duration magnitudes (bottom). Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015 ARTICLE IN PRESS 4 F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx 18.1% and 50.5% of the soil CO2 flux variations at the permanent stations installed in the island of São Miguel. At the Fogo station (GFOG1), only 18.1% of the gas oscillations are due to the external monitored variables including soil temperature, soil water content and rainfall (Viveiros et al., 2008). The remaining oscillation is likely explained by the background hydrothermal variations and the influence of other unmonitored variables. The presence of a geothermal power plant in the vicinities of Fogo permanent flux station is probably responsible for some of the gas flux oscillations, however those activities are not being monitored and consequently are not included in the statistical analysis. Recognizing and filtering environmental influences is an important prerequisite to a reliable application of soil CO2 flux monitoring prior to establishing correlations with seismic and/or volcanic activity. 3. The seismic data This study involves the analyses of the seismic data recorded by the six seismic stations located on and around the Fogo massif (Lagoa de Fogo—LFA, Coroa Mata—PMAT, Monte Escuro—MESC, Vila Franca do Campo—VIF, Chã da Macela—CML and Pico Vermelho—PVER; Fig. 1) in São Miguel by the seismic network SIVISA (Azorean Seismic Surveillance System) during 2003 and 2004. More than five thousand seismic events were recorded by the network in this period (Fig. 2). Within this dataset, 2067 good signal/noise ratio events from the six stations are used in this study. P and S waves were manually picked for these events. In addition, during the three months field survey in 2003 (Saccorotti et al., 2004; Silva, 2004), more than a thousand earthquakes were detected by the temporary network. Most of this seismicity is associated with an intense swarm that occurred on April 26th, 2003, in which more than 300 micro-earthquakes (Md b 3.5) occurred over a period of a few hours. The high number of additional temporary stations deployed around Fogo volcano provides a very well constrained sub-set of data, allowing for precise hypocentral solutions for about 500 events in this 3 months period, and a study of the temporal and spatial evolution of the seismicity. As seen later, such detail can not be achieved for the entire 2003–2004 dataset due to the low energy of the seismicity and the low number of stations at which the signals were recorded. 4. Locations of seismicity 4.1. Method and velocity model The hypocentral location of the seismicity has been obtained using the probabilistic approach of Lomax et al. (2000). In this method, minimum-misfit hypocenters are determined based upon a complete grid search acting over a pre-computed library of travel times calculated via a finite-difference approximation to the Eikonal equation. These were computed using a slightly modified version (explained below) of the minimum 1-D velocity model obtained by tomographic inversion of the subset of the data recorded between 1st April and 15th July 2003 by Zandomeneghi (2007). The true 3-D velocity deviations from the minimum 1-D model are evenly distributed with zero mean. Table 1 Velocity model used for the location procedure. Modified version of 1-D minimum model from Zandomeneghi (2007) Layer Layer Layer Layer Layer Layer Depth (km) Vp (km/s) Vp gradient Vs (km/s) Vs gradient Density Density gradient −1.0 1.0 2.0 3.0 5.0 12.0 2.58 3.83 5.26 5.46 6.78 7.80 0.00 0.00 0.00 0.00 0.00 0.00 1.53 2.28 3.13 3.25 4.03 4.64 0.00 0.00 0.00 0.00 0.00 0.00 2.7 2.7 2.7 2.7 2.7 2.7 0.0 0.0 0.0 0.0 0.0 0.0 Fig. 3. Hypocentral solutions for the 2003–2004 seismicity recorded at stations LFA, PMAT, MESC, VIF, CML and PVER. The locations were obtained using the probabilistic approach of Lomax et al. (2000) with a modified version of the minimum 1-D velocity model obtained by tomographic inversion of the subset of the data recorded between 1st April and 15th July 2003 (Zandomeneghi, 2007). Solutions obtained with a minimum of four P phases only were retained. The average value of the 3-D model across each velocity layer is effectively identical to the minimum 1-D model for the same layer. From the minimum 1-D velocity model obtained by Zandomeneghi (2007) and Zandomeneghi et al. (2008), the top layer has been extended to −1 km a.s.l., so that the station elevation is taken into account. Layers between 5 and 9 km depth were incorporated in one layer only as their velocities only differ by a few m/s. The model is listed in Table 1. 4.2. Relocation of the 2003–2004 data recorded by the permanent network Only solutions obtained with a minimum of four P phases were retained. This yielded 1030 solutions out of the initial 2067 events from the entire 2 year dataset. The resulting hypocenters (Fig. 3) have a sparse distribution, with most of the seismicity clustered on the eastern flank and E of Fogo caldera. The hypocentral depths range from 0 to 10 km, with most of the seismicity concentrated between 2 and 7 km. This cut off in depth might be related to the brittle–ductile transition associated to the high geothermal gradient observed in the area (Dawson et al., 1985). Seven major swarms have been identified in the 2 year period analysed (Table 2). The earthquake magnitudes rarely exceed 2.5 Md. These data suggest that the different swarms originate substantially in the same seismogenic region. For individual swarm activity, no mainshock–aftershock sequence nor migration of the hypocenters over time was observed. Some migration towards shallower locations can be observed in some periods for different swarms (Wallenstein et al., 2007b). Table 2 Seven major swarms identified in the period 2003–2004 Swarm no. Date No. of days from 01-01-03 Number of events 1 2 3 4 5 6 26 April 2003 7 September 2003 7–8–9 November 2003 21–22 January 2004 10 June 2004 5–6–(7–8)–9 September 2004 30–31 October 2004 116 250 311–312–313 386–387 527 614–615– (616–617–)618 669–670 1327 298 121 + 293 + 58 136 + 512 180 142 + 98 + (42 + 19) + 125 141 + 221 7 Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015 ARTICLE IN PRESS F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx Fig. 4. Hypocentral solutions for the events of the 26th April 2003 swarm, obtained retaining a minimum of four P phases picked at a) the six permanent stations LFA, PMAT, MESC, VIF, CML and PVER, and b) with additional stations deployed in the Fogo area during the field experiment between April and mid July 2003. 4.3. Relocation and analyses of the April 2003 data recorded by the permanent and additional temporary networks Due to the additional sensors deployed during the three months field experiment in 2003, the events belonging to the April 26th, 2003 5 swarm were recorded at a higher number of stations around the Fogo massive, allowing a more precise location of a higher number of events. In Fig. 4, the swarm hypocentral solutions obtained using only the six permanent network stations on Fogo and using also the additional stations deployed during the 2003 field experiment are plotted. While the survey data provide a well constrained focused image of the swarm, very few sparse hypocentral solutions were found with the data from only the six permanent stations. From this example, it is clear that a detailed analysis of the swarms other than that of April 26th 2003 is really not feasible. By applying the master event technique of Deichmann and GarciaFernandez (1992) to the April 26th 2003 dataset with a crosscorrelation threshold of 0.75, for a window of 5 s around P and S arrivals, Bonagura et al., (in preparation) identified three clusters of well cross-correlated events within the swarm. Each cluster has inter-hypocentre distances of the order of tens of metres, with a total extent of some hundreds of meters (Bonagura et al., in preparation). They strike from ENE–WSW to NE–SW and describe sub vertical or very steeply N dipping planes (Bonagura et al., in preparation). They range in depth between 4–4.5 km, 5–5.5 km and 7.5–8 km approximately. Observing their temporal occurrence, the cluster at ∼5 km depth activates after both the shallower and the deeper clusters (Bonagura et al., in preparation). These observations, taken together with the uneven temporal distribution of magnitudes, suggest that most of the 26th April seismicity was possibly related to the random activation of distinct patches of the same fault plane subjected to a heterogeneous stress field (Bonagura et al., in preparation). Fault plane solutions have been determined for the better constrained absolute location hypocenters, obtained using at least twelve P and S phase readings, with a RMS error b1 and both vertical and horizontal error b2 km (Bonagura et al., in preparation). Fault plane solutions associated with a compressional regime were found for the April 26th swarm events, while the seismicity preceding and following the swarm gave normal focal solutions, suggesting extension along E–W striking faults (Bonagura et al., in preparation). Bonagura et al. (in preparation) and Saccorotti et al. (2004) conclude that the seismicity preceding and following the April 26th swarm on E–W trending faults was mostly associated with the plate divergence along the Terceira Rift, which is consistent with, e.g., the GPS data analysed by Jónsson et al. (1999) which suggest an accommodation of 75% of plate divergence inside the island of S. Miguel. On the other hand, the reverse movement related to the April 26th swarm is not consistent with the general stress field acting on the island. Taken together with the arguments reported above, therefore, we may imagine that fault weakening due to increasing pore pressure has played an important role in triggering these particular earthquakes. Fig. 5. Example of three doublets identified in the catalogue under investigation and the detection of velocity changes through the application of Coda Wave Interferometry (CWI). Seismograms (top) and plots of lag times of maximum of cross-correlation between the two traces, in non overlapping windows (bottom). a) No velocity variation is detected between the two events, b) lag times show a linear decrease with time: the second event in the doublet is slower, c) lag times show a linear increase with time: the second event is faster. Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015 6 ARTICLE IN PRESS F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015 Fig. 6. Results from CWI for the six permanent network stations on and around Fogo, Z-component. Each line represents the result of CWI applied to a doublet. The line starts at the time of the first event and ends at the time of the second event. Black solid line = the second event is slower, black dashed line = the second event is faster, gray line = no velocity variation was observed. This allows us to define time periods of velocity variation, that have been highlighted in dark grey (slower velocity) and light grey (faster velocities). The asterisks highlight some departures from the general behaviors: those can be explained by the rainfall patterns (see text in Section 7). ARTICLE IN PRESS Fig. 6 (continued). F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015 7 ARTICLE IN PRESS 8 F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx These observations are compatible with the temporal and spatial behaviour of the April 26th seismicity. Moreover, the Vp/Vs ratio is found to be 1.4–1.7 (Dawson et al.,1985; Saccorotti et al., 2004; Bonagura et al., in preparation), lower than usually observed in volcanic areas. According to Ito et al. (1979), such anomalous Vp/Vs ratios could be attributed to the presence of a boiling hydrothermal system beneath the investigated area. The water–steam phase transition should affect compressibility more than shear modulus, with the net effect of lowering the ratio among P- and S-wave velocities. These observations seem to suggest that pressurization may play a role in triggering the seismicity. These conclusions have been deduced from a detailed study of the April, 26th swarm and its background seismicity. It would be interesting to attempt to confirm this behaviour for all swarms that occurred throughout 2003 and 2004. However, such a detailed study of the other swarms is not feasible due to the low number of stations at which they are recorded. As pressurization would lead to velocity changes in the medium, we search for such changes using the repeating seismicity. The method is outlined in the next section. tion is proportional to the slope. The velocity variation and its associated error are computed by least squares method. More than 400 doublets have been identified by correlating in a 3 second window all the events within the catalogue (outlined in Section 2) in the time interval between January 2003 to December 2004 at the stations LFA, PMAT, MESC, VIF, CML and PVER. The data were band pass filtered between 2 and 15 Hz to reduce environmental noise. We apply the CWI method to monitor velocity changes through time, using pairs of events (doublets) with cross-correlation coefficients of 0.9 or greater. Only in two cases (station CML and PMAT) the threshold had to be lowered to 0.85, due to low signal to noise ratio. The high correlation between events is not sufficient to guarantee the same source characteristics, which requires detailed observation of the first arrivals. All pairs of events were visually checked before being analysed with the CWI technique. The waveforms are aligned at the Pwave arrival and we visually check the similarity of the P-onset, as an additional constraint on the similarity of the source-time function. The choice of the window length T for the time-shifted correlation coefficient is determined by a trade off between the number of independent data and a fast drop in coherency for windows that are too small. Our 5. The methodology: coda wave interferometry Families of repeating, almost identical earthquakes or multiplets are observed among the seismicity recorded on Fogo volcano on the fixed network as well as the 2003 survey data. Repeating earthquakes exhibiting very similar waveforms are representative of the subsequent activation of a source whose mechanism and location do not change over time (Geller and Mueller, 1980; Poupinet et al., 1984; Roberts et al., 1992). In a doublet, while ballistic arrivals match, the coda can lose coherency as multiply scattered waves are sensitive to small changes in the medium. In a strongly scattering environment such as a volcano, the seismic coda, composed of multiply scattered waves, ‘samples’ the medium more effectively than the direct (ballistic) arrivals. For identical co-located sources, acting at different times, any observed difference in waveforms is related to a change in the elastic properties of the medium. The coda wave interferometry technique (CWI; Snieder et al., 2002) uses multiply scattered waves to detect temporal changes in a medium by using the medium as an interferometer, i.e. comparing waves sampling the medium at different times. The perturbation of the medium can be retrieved from the time shifted cross-correlation of the coda waves before and after the perturbation. Exploiting the information contained in the coda, CWI can discriminate between source movement, scatterer movement and velocity variations in the medium (Snieder et al., 2002; Snieder and Hagerty, 2004; Gret et al., 2005; Snieder and Vrijlandt, 2005; Pandolfi et al., 2006, Carmona et al., 2007; Martini et al., 2007). CWI theory is based on path summation; the total wave field is regarded as a superposition of all the possible scattering paths, including all mode conversions (Snieder, 1999, 2002). Starting with two waveforms, u(t) and ū(t), the time-shifted correlation coefficient is computed in a time window of length 2T centred on t, following the formula: R t+T uðt VÞu ðt V+ ts Þdt V t−T ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Rðts Þ = v ut R+ T tR +T u t u2 ðt VÞdt V u 2 ðt VÞdt V t−T ð1Þ t−T For each time window, the percentage velocity variation dv/v can be computed from the lag-time ts as ts = −dv / vt. Under the hypothesis of a uniform velocity variation, the plot of the lag-time corresponding to the maximum of the cross-correlation per time window, versus time, shows a linear relationship, where the mean velocity perturba- Fig. 7. Top: Monthly rainfall (in mm) measured at station GFOG1, between January 1st 2003 and December 31st 2004. Middle: Black line/symbol: rain per day (in mm) for the same time period and, in white, the 6th order polynomial fit. The choice of order is simply to highlight the long term trend. The seasonal variations are evident, with more rainfall between October and April and minimum rainfall in June–July and August. Bottom: Time periods of velocity variation, observed through coda wave interferometry analysis of all the doublets (delineated by the vertical black dotted lines in the limit of the doublet time resolution): in dark grey defined period of slower velocity and in light grey faster velocities. The vertical dashed grey lines highlighted the months (as in Fig. 6). A correspondence between periods of low rainfall and higher velocities, and high rainfall and lower velocity is clear. Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015 ARTICLE IN PRESS F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx results are stable using window lengths between 5 and 10 cycles, without an overlap. We show doublet pairs from the 2003–2004 dataset in Fig. 5; the application of CWI technique to these pairs, in case of a) null, b) negative and c) positive velocity variations. 6. Results We found a high number of doublets and, amongst those, several multiplets. Due to the low energy of the majority of the events, not all the stations in the region recorded all the pairs. We use each pair to both determine the percentage change in velocity between the events in the pair and to determine the sign of the change (faster or slower). As not all stations are equipped with three component sensors, 9 we applied the CWI technique to the vertical component only, at all stations. Results are visually represented in Fig. 6a to f. In the figures, each line represents the result from CWI applied to a doublet. The line starts at the time corresponding to the first event of the doublet (time 1) and it ends at the time corresponding to the second event (time 2). This means that the results each line represents are not relative to the entire line length, but they are relative to the status of the medium at time 2 compared to time 1. A black solid line means that the second event (at time 2) is slower than the first (see example in Fig. 5b), a black dashed line that the second event is faster (Fig. 5c), a grey line that no variation was observed between the two events (Fig. 5a). A consistency in the sign of the velocity variation at all the stations was observed. The magnitudes of the variations are also consistent, Fig. 8. a) Example of doublets measured at more than one station, and the relative percent velocity change calculated with coda wave interferometry for the same doublets at the different stations, b) Rainfall measured at udometric stations Monte Escuro and Fogo II in May, June, July 2003 (first doublet in panel a), c) Rainfall measured at udometric stations Monte Escuro, Fogo II and Ribeira da Praia in May, June, July 2003 (second doublet in panel a), d) Rainfall measured at udometric stations Monte Escuro and Fogo II between May and December 2003 (third doublet in panel a), e) Rainfall measured at udometric stations Monte Escuro and Fogo II November 2003 to February 2004 (fourth and fifth doublet in panel a). Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015 ARTICLE IN PRESS 10 F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx when the signal to noise ratio is good. At all stations, the results show a decrease of velocity between November 2003 and March 2004, and again starting in October 2004. An increase of velocity between May 2003 and September 2003 and again between May and September 2004 was also observed. These variations are indicated as dark and light grey background shading in Fig. 6, respectively. The subtle velocity variations clearly highlight a seasonal cycle of the velocity patterns, which is unlikely to be related to volcanic activity. No multiplets were found with both events before April 2003 and both events after November 2004; therefore the velocity patterns before April 2003 and after November 2004 cannot be defined through analysis of this catalogue. 7. Correlation with rainfall The daily and the monthly rainfall (Fig. 7; hourly rain data available from the main meteorological station on Fogo) for the same two year period show clearly that São Miguel Island experiences maximum rainfall from October to April, and minimum rainfall between May and July. This indicates a possible relationship between dry months and higher velocities, and wet months and lower velocities. The magnitude of velocity variations can also be correlated with the episodes of rainfall: when the same doublet is recorded at more than one station, a broad correlation could be found between the magnitude of the velocity variation obtained by that doublet at each station with the locally measured amount of rainfall (udometric stations rain monthly data from the DROTRH, Direcção Regional do Ordenamento do Território e Recursos Hídricos). Some examples are listed and compared with the rain data in Fig. 8. Coda wave interferometry analysis on the first doublet (25 May 2003–24 July 2003) gives a velocity variation of +0.2% at station MESC and +0.96% at station LFA. Analysing the rain data at the two closest udometric stations (Fogo II for LFA and Monte Escuro for MESC), we find that in the area close to Monte Escuro it rained more than in the area in proximity of LFA in the months of May, June and July 2003 (Fig. 8b), therefore explaining a smaller positive variation. Analogously, the doublet 22 May 2003–24 July 2003 gives again the smallest positive variation at MESC (0.13%) when compared with the values obtained at LFA (0.4%) and VIF (0.6%): the udometric station at Ribeira da Praia (the closest to VIF) recorded locally the lowest amount of rain of the three, while the highest amount of rain for the period fell in the Monte Escuro area (Fig. 8c). Coda wave interferometry analysis on the doublet 30 July 2003–03 November 2003 shows a velocity variation of −0.56% at LFA and −0.52% at MESC, mirroring a similar pattern of the rainfall at the udometric stations Fogo II and Monte Escuro (Fig. 8d). The event on 9th December 2003 compared with a similar event on 14th February 2004 shows a negative velocity variation of −0.26% at LFA while no variation velocity was observed at MESC: in February 2004 the Fogo II udometric station recorded a very high peak of rainfall (Fig. 8d). Interestingly, the same December 2003 event gave a negative velocity variation of −0.50% and −0.37% at LFA and MESC respectively, when analysed towards an event on 16th February 2004 (only two days after the previous example): the different values obtained at the two stations only two days apart can be explained by a particularly heavy rain episode on February 15th recorded also at the Fogo main meteorological station. In a similar matter, abundant episode of rainfall can explain the few exceptions in the velocity variations from the general seasonal cycles (indicated by asterisks in Fig. 6). For example, the positive velocity variation identified between April 2003 and January 2004 at station PMAT can possibly be explained by above the average rainfall in March–April 2003 (which can also explain a positive variation recorded between May 2003 and January 2004 at station PVER). Analogously, again at the station PMAT, the positive velocity variation found between doublets with events in November 2003 and November 2004 can be explained as November 2004 was drier than Fig. 9. a) Cumulative sum of the seismicity (solid line, left) and of the rainfall (dashed lines, right), b) normalised correlation coefficients calculated between the seismicity and rainfall time series: at all lags (M ⁎ M − 1, where M is the length of the time series) in the inset, for seismicity following rainfall (from lag M + 1 to end) in the main image: the two highest peaks occur at time lag 733 (3 days) and 777 (47 days), c) the correlation is performed scaling the raw values by [1 / (M − abs(lags))] (“unbiased”), at all lags in the inset, for M + 1 to end in the main image. The values are normalised by the maximum value amongst all lags for the insets, for positive lags only in the main images b and c. the same month in the previous year. A drier 2004 winter explains similar anomalies at station CML. At station VIF, events from 14 January 2004 and 22 January 2004 gave a different sign velocity variation when compared to events in October 2004, which can be explained by a peak in the rainfall on 18–19 January 2004. A seasonal effect can also be broadly seen in the seismicity occurrence. September to April is the wettest period in São Miguel, and all the swarms under investigation occur in those months, with the only exception of swarm no. 5 in June 2004 (Table 2). In June 2004 rain fell more abundantly than the summer average, an amount also considerably higher than that of June 2003. The majority of the swarms recorded over the two year period seems to occur in close temporal association with higher than average rainfall (the converse is Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015 ARTICLE IN PRESS F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx 11 Fig. 10. Correlation coefficients calculated between the seismicity and a randomize version of the rainfall series. This was done in order to test the statistical significance of maxima of correlation coefficient obtained from the two real time series, observed in Fig. 9b. The normalized correlation values for 4 populations of 100 tests each are plotted in panels a, b, c, d (circle symbol), for the peak at 3 days (left column) and 47 days lag (right column), compared with the real time series (cross symbol). Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015 ARTICLE IN PRESS 12 F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx not always true). The observed delay between the seismicity and the episode of rainfall ranges between 0 to 5 days (Fig. 9a). In order to better assess this empirical observation, we correlated the rainfall and seismicity time series at all lags (2 ⁎ M − 1, where M is the length of the time series). The hourly rainfall data recorded at the main meteorological station were summed up to obtain a daily record, which was correlated with the daily number of seismic events. When looking at the results for all lags (Fig. 9b and c, insets), an annual periodicity can be seen in the correlation. This tells us that, statistically, there is an underlying annual cycle in both data sets. The correlation also exists, and is quasi-periodic, at negative lag times (e.g. seismicity occurring after the rain), therefore meaning that the two datasets have the same underlying periodicity. From these long term trends we can conclude that the annual periodicity of the rainfall and the seismicity appear to be the same, which indicates that, on average, the rain is helping to drive the seismicity. In analysing the results from lag M + 1 to end (e.g. seismicity occurring after the rain), we found that the two highest values of the correlation coefficient (1 and 0.99, or 0.69 and 0.68 when values are normalised to the same value for all lags) are obtained with a 3 and 47 days lag delay between the rain and the seismicity (Fig. 9b). When the correlation is performed scaling the raw values by [1 / (M − abs(lags))] (“unbiased”) to compensate for the fact that the coefficient decreases moving away from zero lag as less data are correlated, the highest values results at large lags, but the two peaks at lag 3 and 47 still stands out in the shorter lag region (Fig. 9c). The fact that the correlations are better at larger lag times is an artefact of more noise as less data are overlapped, and, in any case, it refers to lags (N500) too large to have a physical significance for a possible influence of the rain on the seismicity. Due to high amount of rainfall and high level of seismicity in the island, we have to exclude the possibility that the observed correlations cannot be explained by random chance. In order to test the statistical significance of this result, we performed the correlation several hundreds times, randomizing at each run the time of the rainfall series. We then compared the correlation coefficient at lag 3 and 47 for each run with the results obtained with the real rainfall time series. The normalized correlation values for 4 populations of 100 tests each are plotted in Fig. 10 (circle symbol), for the peak at 3 days (left column) and 47 days lag (right column), compared with the real time series (cross symbol). For the 3 days lag case, we can observe that none of the randomize test gave a value equal or higher than the real one, and only few runs (approximately 2%) gave values equal or greater than 0.6. For the 47 days delay case (the second highest peak in the correlation), several randomize test gave values higher or similar to the real one. Hence, while we feel confident of the existence of a correlation between the rainfall and seismicity time series with a delay of 3 days, the same cannot be confirmed for the 47 days peak, which therefore has no significance for our purposes. Moreover, the spatial distribution of the epicentres within each swarm was analysed in relation to the rainfall distribution recorded at the udometric stations (monthly rainfall data; Fig. 11). These data show two high well-above-the average values at the beginning and end of 2004 at Fogo II and Lombo stations. The epicentre distribution of swarm 4 and 7 (Fig.12c and e), recorded in the corresponding periods, seem to be concentrated in an area around or not far from station Fogo II and Lombo, respectively. With the exception of those two obvious peaks, all the remaining rainfall data, at all stations (Fig. 11), have very comparable values, making this spatial correlation impossible for the other swarms. It should be added that the udometric stations record monthly rainfall data while the swarms always occur in 2 day periods, therefore the two cases of observed spatial correspondence between the seismicity and the pronounced peaks of rainfall should be considered cautiously. Deformation data (GPS) at Fogo showed in recent times several periods of inflation and deflation in the area. In 1993–1997 displacements toward the caldera were registered, indicating slight deflation of the volcano, which were interpreted as due to pressure decrease in a shallow magma chamber beneath Fogo, or extraction of hot water and steam by a geothermal plant to the north of the volcano edifice (Jónsson et al., 1999). Unfortunately, no recent data are currently available to the authors to establish a correlation with the observed velocity variations and/or rainfall episodes. 8. Discussion One of the most striking features of São Miguel seismicity is the occurrence of swarm episodes comprised of hundreds of low Fig. 11. Monthly rainfall data measured at the udometric stations for 2003 and 2004. Station locations are shown in Fig. 12. Data courtesy of DROTRH (Direcção Regional do Ordenamento do Território e Recursos Hídricos). Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015 ARTICLE IN PRESS F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx magnitude events occurring over a few hours. Understanding the origin of this type of seismicity is our long term primary goal, especially if it is understood to be connected to renewed volcanic activity. As a first step toward this objective, we analysed 430 doublets identified in the 2003–2004 catalogue at six stations on and around the volcano through a cross-correlation based technique. The results highlight a seasonal cycle of the velocity patterns, with lower velocity in winter time and higher velocity during summer months. We observed that seismicity seems to occur preferably in periods of more abundant rainfall. While there is a clear relationship between seismicity and rainfall, an incontrovertible evidence of rain episodes triggering the seismic swarms at Fogo cannot be established mainly due to lack of detailed temporal rainfall information (the hourly data are only available at two permanent stations). Fluids are known to be important in earthquake generation as pore pressure variations alter the effective strength of faults, thus tiny varia- 13 tions of pressure associated with precipitation could conceivably initiate earthquakes at a few kilometres depth when the crust is close to a critical stress state. Evidence for meteorological triggering of seismicity has already been found in other part of the world, e.g. in Switzerland (Deichmann et al., 2006), USA (Saar and Manga, 2003; Christiansen et al., 2005), Germany (Hainzl et al., 2006; Kraft et al., 2006a,b) and in the Balkan area (Muço, 1999). Although seasonal variability related to ground water recharge and precipitation has been observed in all those cases, a statistically significant causal relationship between rainfall and earthquake swarms occurrence in an isolated region has been shown only for Mt. Hochstaufen in SE Germany: Hainzl et al. (2006) found that the recorded seismicity at Mt. Hochstaufen is highly correlated with the calculated spatiotemporal pore pressure changes due to diffusing rain water and in good agreement with the response of faults described by the rate-state friction law (Kraft et al., 2006a). For those data, the precipitation and Fig. 12. Epicentre distribution for swarms a) 1, b) 3, c) 4, d) 6 and e) 7 (2 and 5 did not have enough solutions), together with location of seismic stations (stars) and udometric stations (squares). Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015 ARTICLE IN PRESS 14 F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx groundwater increase show maximum cross-correlation with the seismicity when delayed by 9–11 days. In active volcanoes, precipitation clearly influences, amongst other parameters, fumaroles and ground temperature (for example, at Merapi, Richter et al., 2004, Friedel et al., 2004) as well as thermal springs temperature and gas concentrations (i.e. Arenal volcano, Lopez et al., 2006) or self potential (Friedel et al., 2004). There is, to our knowledge, no unambiguous evidence of the effect of precipitation on seismicity. An increase in superficial seismic events due to quenching and destabilisation of parts of the lava dome has been observed at Unzen volcano (Yamasato et al., 1998), more significantly when fresh lava was involved. At Mt. Merapi volcano there is some evidence that rainfall influences seismicity rates, indicating interaction of meteoric water with the volcanic systems (Richter et al., 2004), but in this case the effect seems to be different: even if fresh lava was observed in the dome region, there are examples of a decrease in the total seismicity rate after heavy rainfall (Richter et al., 2004). All those examples are clearly not comparable to the Fogo case. At Fogo, there is no lava dome. While our case seems to be more similar to the Hochstaufen one, it should be noted that at Mt. Hochstaufen the seismicity ranges from very shallow depth to a maximum of 2.5 km and the effect of precipitation at surface on the fault system at this depth takes approximately 10 days. In contrast at Fogo the seismic events range in average from 2 to 7 km depth, and up to 10 km, with a delay between the rainfall and the swarms occurrence from 0 to a maximum of 5 days: the process seems to be different. Furthermore, the velocity variations highlighted with the CWI technique can be observed as an immediate effect following the episodes of rainfall. Due to the lack of resolution in time of the CWI technique (its resolution is strictly dependent on the occurrence of doublets and their timing) we cannot quantify the delay in term of hours; however, in many cases the delay is smaller than 1 day. One possible interpretation of the velocity variations is that they are the effect of water saturation of the soil layer following rainfall. But it should be kept in mind that those velocity variations are observed with the CWI method for doublets some of which are located at several kilometres depth and multiply scattered coda waves are likely generated throughout a large volume encompassing the source and the receiver, although there may be some near surface bias. Therefore, it is unlikely that the CWI-derived velocity changes measure “directly” the rain at surface. The temporal and spatial analysis of the swarms highlights the lack of any main-shock after-shock sequence and no organized migration of the hypocenters. The almost immediate response to the rain and lack of hypocenter migration would exclude a process of fault pressurisation due directly to water percolation at Fogo. It would rather suggest that a general effect of some other form of pressurization might instead play a contribution in triggering the seismicity. Similar observations have been made for induced seismicity at the Castanhão water reservoir in NE Brasil where a very good correlation and virtually no time delay between the water level rise in the reservoir (due to above average heavy rainfall) and the increase in seismic activity has been observed in 2004 (Ferreira et al., 2008). The spatio temporal analysis of the data revealed that the seismicity occurred in clusters at different depths and that these were activated at different periods. The mechanism controlling this seismicity has been explained as an instantaneous effect of loading and the delayed effect of pore pressure (Ferreira et al., 2008). While this investigation goes beyond the scope of this paper, it will be object of future work; the mechanisms we intend to explore for our dataset at Fogo are (i) pressure changes at depth in response to surface rain that could contribute to critically stress the system and trigger seismicity, and (ii) interaction with the geothermal system. The geothermal fluids in the Fogo massif have been identified as derived from meteoric water, which infiltrates through the Fogo caldera and the volcano flanks and flows from south to north on the northern flank (Carvalho et al., 2006). Analysis of seismic array data through multi-channel processing techniques from a deployment in April 2003 (Bonagura et al., in preparation), shows the presence of correlated noise days before the occurrence of the April 26th, 2003 swarm. These authors interpreted such occurrence in terms of tremor-like signals originating from unsteady fluid flow in the geothermal system (see their paper for a detailed discussion of these results). Such unsteadiness could be due to rapid pressure changes at depth caused by increased load due to the rain at surface and/or increase of the ground water level, and the consequent static stress increase in the volume to depth. Another possibility is that after rainfall, the impermeable moisture layer near the surface could have a capping effect and thus temporarily prevent CO2 flux locally, whilst increasing soil gas levels in the subsurface. This capping effect has been observed in other areas for both CO2 (Solomon and Cerling 1987; Hinkle 1991, 1994) and Radon (Asher-Bolinder et al., 1991). Both a pressure increase and/or an increase of soil gas levels at depth might explain the seismic velocity variations observed with coda wave interferometry. Both the extra load due to the rain and/or soil capping effects could cause an increase of pressure at depth, contributing, together with tectonic and/or magmatic causes, to seismicity triggering when the system reaches a critical stress state. If the system is critically stressed, the small changes associated with extra load due to the rain and/or soil capping effects could possibly lead to triggered seismicity. Both processes would likely have only a local effect (e.g. the degassing is not totally prevented during rainy periods if we look at the system at large scale) and their efficiency for increasing pressure at depth needs to be tested and quantified. In the case where the seismicity has a tectonic or deep magmatic origin, the rain/stress transmission/pore pressure increase effect would facilitate the seismic energy release at shallower levels by decreasing the capability of the system to sustain the stress transfer from the triggering sources. This could explain why deep and shallower events are triggered simultaneously. This would also justify the “style” of the seismicity in the region, with low Vp/Vs ratio and many low energy events as a measure of the low capacity of the system to hold stress to be released in bigger events. Samplings at the main fumarolic fields show in some cases minor increases in some gas ratios (e.g. H2/CH4) that could possibly be explained by an increased pressure in the feeding aquifers of the fumaroles; these changes are observed at times of increased seismicity (F. Viveiros, pers. comm.). If this can be observed in more detail for all the seismicity episodes, it could help explain the mechanism that triggers the seismicity at Fogo volcano, and determine if the seismicity variations are driven by magmatic processes, rainfall or an interaction (and/or alternating) of the two. 9. Conclusions A recurrent feature of the seismicity observed in volcanic regions is the occurrence of families of similar seismic events, whose origin can be attributed to the same source mechanism, acting in the same small rock volume. Doublets/multiplets were identified in this study within a catalogue of small magnitude (usually b3) volcano tectonic events recorded in 2003–2004 by a selection of seismic stations around Fogo volcano, São Miguel, Azores. Subtle velocity variations, found through analysis of all the doublets using Coda Wave Interferometry, highlight a seasonal cycle of the velocity patterns and an excellent correlation with the rainfall, with lower velocity in the wetter winter time and higher velocity during the drier summer months. A seasonal effect can also be broadly seen in the seismicity occurrence, suggesting that rainfall might play a contribution in local seismicity. Please cite this article as: Martini, F., et al., Seasonal cycles of seismic velocity variations detected using coda wave interferometry at Fogo volcano, São Miguel, Azores, during 2003–2004, J. Volcanol. Geotherm. Res. (2009), doi:10.1016/j.jvolgeores.2009.01.015 ARTICLE IN PRESS F. Martini et al. / Journal of Volcanology and Geothermal Research xxx (2009) xxx–xxx Moreover, temporal and spatial analysis of several swarms highlights the lack of any mainshock–aftershock sequence and organized migration of the hypocenters, suggesting the involvement of a heterogeneous stress field, which in turn might be explained in terms of vigorous fluid migration/pressurization at and around the hypocentral areas. These results point to a possible role played by rainfall in triggering seismicity at Fogo volcano, the mechanisms of which are currently under investigation. The immediate response of the seismic velocity changes to the rain, an average 3 days delay between the seismicity occurrence to the rain and lack of hypocenters migration would exclude a process of fault lubrication. The mechanisms is more likely related to local pressure changes at depth in response to surface rain that could contribute to triggered seismicity when the system is critically stressed and/or the interaction of rain water with the geothermal system. Acknowledgements This work is sponsored by European Commission, 6th Framework Project VOLUME (Contract 08471). DROTRH (Direcção Regional do Ordenamento do Território e Recursos Hídricos) is kindly acknowledged for providing rain data from the udometric stations. The authors wish to thank C. Riedel and R. Silva for early discussion and very valuable information on the dataset, and Jesus Ibañez and an anonymous reviewer for their suggestions on the manuscript. 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