Passive seismic techniques are a valuable tool to map active faults, follow changes in the local stress field caused by drilling and exploitation processes in geothermal areas and estimate seismic ...velocity ratios. They have, however, not been used extensively in basaltic crust as we find it in Iceland due to the data processing challenges that high impedance contrasts between layers of igneous rocks pose. To improve conventional passive seismic techniques and to test new ones, a dense network of seismometers recorded seismic activity on- and off-shore the Reykjanes peninsula in southwest Iceland from spring 2014 until August 2015. In addition to the existing long-term and permanent networks, a temporary network consisting of 30 on-land stations and 24 ocean bottom seismometers (OBS) was deployed. The network was laid out as concentric circles around the Reykjanes geothermal area at the tip of the peninsula and was about 100 km in diameter. The data of the on-land stations were collected every 70 days and data recorded by the OBSs were read out after one year when they were recovered. We used the SeisComp3 software to automatically detect earthquakes and then manually revised the P and S arrival times. A total of 2066 earthquakes could be located along the mid-ocean ridge and within and around the Reykjanes geothermal system. Earthquake hypocentres associated with the geothermal area occur in the uppermost 2 km while events along the spreading axis occur at 4–6 km depth on-land and appear to deepen with increased distance from the shore. We see a cluster of about 200 events, induced by the drilling and onset of operation of a local injection borehole in the Reykjanes geothermal area. We find a vp/vs ratio from all picks of 1.78 consistent with earlier studies in the area. Focal mechanisms calculated for selected events reveal a regime dominated by strike-slip and normal faulting.
SUMMARY
High spatial and temporal resolution of gravity observations allows quantifying and understanding mass changes in volcanoes, geothermal or other complex geosystems. For this purpose, accurate ...gravity meters are required. However, transport of the gravity meters to remote study areas may affect the instrument's performance. In this work, we analyse the continuous measurements of three iGrav superconducting gravity meters (iGrav006, iGrav015 and iGrav032), before and after transport between different monitoring sites. For 4 months, we performed comparison measurements in a gravimetric observatory (J9, Strasbourg) where the three iGravs were subjected to the same environmental conditions. Subsequently, we transported them to Þeistareykir, a remote geothermal field in North Iceland. We examine the stability of three instrumental parameters: the calibration factors, noise levels and drift behaviour. For determining the calibration factor of each instrument, we used three methods: First, we performed relative calibration using side-by-side measurements with an observatory gravity meter (iOSG023) at J9. Secondly, we performed absolute calibration by comparing iGrav data and absolute gravity measurements (FG5#206) at J9 and Þeistareykir. Thirdly, we also developed an alternative method, based on intercomparison between pairs of iGravs to check the stability of relative calibration before and after transport to Iceland. The results show that observed changes of the relative calibration factors by transport were less than or equal to 0.01 per cent. Instrumental noise levels were similar before and after transport, whereas periods of high environmental noise at the Icelandic site limited the stability of the absolute calibration measurements, with uncertainties above 0.64 per cent (6 nm s–2 V–1). The initial transient drift of the iGravs was monotonically decreasing and seemed to be unaffected by transport when the 4K operating temperatures were maintained. However, it turned out that this cold transport (at 4 K) or sensor preparation procedures before transport may cause a change in the long-term quasi-linear drift rates (e.g. iGrav015 and iGrav032) and they had to be determined again after transport by absolute gravity measurements.
The 2010 eruption of Merapi is the first large explosive eruption of the volcano that has been instrumentally observed. The main characteristics of the seismic activity during the pre-eruptive period ...and the crisis are presented and interpreted in this paper. The first seismic precursors were a series of four shallow swarms during the period between 12 and 4months before the eruption. These swarms are interpreted as the result of perturbations of the hydrothermal system by increasing heat flow. Shorter-term and more continuous precursory seismic activity started about 6weeks before the initial explosion on 26 October 2010. During this period, the rate of seismicity increased almost constantly yielding a cumulative seismic energy release for volcano-tectonic (VT) and multiphase events (MP) of 7.5×1010J. This value is 3 times the maximum energy release preceding previous effusive eruptions of Merapi. The high level reached and the accelerated behavior of both the deformation of the summit and the seismic activity are distinct features of the 2010 eruption.
The hypocenters of VT events in 2010 occur in two clusters at of 2.5 to 5km and less than 1.5km depths below the summit. An aseismic zone was detected at 1.5–2.5km depth, consistent with studies of previous eruptions, and indicating that this is a robust feature of Merapi's subsurface structure. Our analysis suggests that the aseismic zone is a poorly consolidated layer of altered material within the volcano. Deep VT events occurred mainly before 17 October 2010; subsequent to that time shallow activity strongly increased. The deep seismic activity is interpreted as associated with the enlargement of a narrow conduit by an unusually large volume of rapidly ascending magma. The shallow seismicity is interpreted as recording the final magma ascent and the rupture of a summit-dome plug, which triggered the eruption on 26 October 2010.
Hindsight forecasting of the occurrence time of the eruption is performed by applying the Material Failure Forecast Method (FFM) using cumulative Real-time Seismic Amplitude (RSAM) calculated both from raw records and on signals classified according to their dominant frequency. Stable estimates of eruption time with errors as small as ±4h are obtained within a 6day lapse time before the eruption. This approach could therefore be useful to support decision making in the case of future large explosive episodes at Merapi.
•Precursory seismic activity started about 6weeks before eruption onset.•Seismic energy release was 3 times that of previous effusive eruptions.•Sources of VT events are split into two clusters with different depths.•Upward shifting of sources 10days before eruption•Hindsight forecasting of eruption time is obtained with good precision.
Geysers are hot springs whose frequency of water eruptions remain poorly understood. We set up a local broadband seismic network for 1 year at Strokkur geyser, Iceland, and developed an unprecedented ...catalog of 73,466 eruptions. We detected 50,135 single eruptions but find that the geyser is also characterized by sets of up to six eruptions in quick succession. The number of single to sextuple eruptions exponentially decreased, while the mean waiting time after an eruption linearly increased (3.7 to 16.4 min). While secondary eruptions within double to sextuple eruptions have a smaller mean seismic amplitude, the amplitude of the first eruption is comparable for all eruption types. We statistically model the eruption frequency assuming discharges proportional to the eruption multiplicity and a constant probability for subsequent events within a multituple eruption. The waiting time after an eruption is predictable but not the type or amplitude of the next one.
Plain Language Summary
Geysers are springs that often erupt in hot water fountains. They erupt more often than volcanoes but are quite similar. Nevertheless, it is poorly understood how often volcanoes and also geysers erupt. We created a list of 73,466 eruption times of Strokkur geyser, Iceland, from 1 year of seismic data. The geyser erupted one to six times in quick succession. We found 50,135 single eruptions but only 1 sextuple eruption, while the mean waiting time increased from 3.7 min after single eruptions to 16.4 min after sextuple eruptions. Mean amplitudes of each eruption type were higher for single eruptions, but all first eruptions in a succession were similar in height. Assuming a constant heat inflow at depth, we can predict the waiting time after an eruption but not the type or amplitude of the next one.
Key Points
We create a catalog of 73,466 eruptions of Strokkur geyser, Iceland, from a 1 year seismic data set
Single to sextuple eruptions are followed by a mean waiting time of 3.7 to 16.4 min, respectively
Waiting time after an eruption can be predicted, while future eruption type or amplitude cannot
In this study, we measure seismic velocity variations during two cycles of crustal inflation and deflation in 2020 on the Reykjanes peninsula (SW Iceland) by applying coda wave interferometry to ...ambient noise recorded by distributed dynamic strain sensing (also called DAS). We present a new workflow based on spatial stacking of raw data prior to cross‐correlation which substantially improves the spatial coherency and the time resolution of measurements. Using this approach, a strong correlation between velocity changes and ground deformation (in the vertical and horizontal direction) is revealed. Our findings may be related to the infiltration of volcanic fluids at shallow depths, even though the concurrent presence of various processes complicates the reliable attribution of observations to specific geological phenomena. Our work demonstrates how the spatial resolution of DAS can be exploited to enhance existing methodologies and overcome limitations inherent in conventional seismological data sets.
Plain Language Summary
In 2020, an intense unrest period took place on the Reykjanes peninsula, in southwest Iceland, preceding the Fagradalsfjall volcano eruption in 2021. The unrest was characterized by ground movements of several centimeters (measured by GNSS stations) and accompanied by an increased number of local earthquakes. We investigate whether the unrest affects velocities of seismic waves that propagate through the crust in Reykjanes. Instead of conventional seismometers, we use seismic data recorded by a fiber optic cable. This technology has the advantage that measurements can be made every few meters along the cable. We exploit this high spatial sampling to improve methods traditionally applied to seismometer records. These improvements enable us to infer seismic velocity changes as a function of time and space along the fiber optic cable. We detect velocity changes that strongly correlate with the observed ground deformation in Reykjanes and are, therefore, likely linked to the unrest and/or its associated processes.
Key Points
Using distributed dynamic strain sensing (also called DAS) and coda wave interferometry, we resolve velocity changes in time and space
Wavelength‐dependent spatial stacking of raw data prior to cross‐correlation improves the spatio‐temporal coherency of results
Inferred velocity changes correlate with vertical and horizontal ground deformation observed during the 2020 unrest period in Reykjanes
Understanding precursory signals is essential to forecast eruptions hazards and mitigate risks. Satellite observations have been shown to increasingly contribute to this goal. We use ...Differential-Inteferometric Synthetic Aperture Radar (D-InSAR) and SAR backscattering intensity data from ALOS/PALSAR to define a time-series of ground surface displacements for the 2006–2010 inter-eruptive period at Merapi volcano. We correlate trends in the displacement data to trends for summit temperature (determined using data from the ASTER sensor) and to gas emission data. We show that processing of the satellite data must be performed carefully before meaningful interpretations can be drawn. For example, after careful removal of the topographic effect on phase delays, we detect only subtle inter-eruptive episodes of cyclic deformation in the D-InSAR and SAR backscattering data. These small (mostly sub-centimeter) displacements contrast with meter-scale pre-eruptive displacements along an electronic distance measurement (EDM) line on the south flank of the volcano. We suggest that this difference is a consequence of localized movement within the summit area — a result that has important implications for understanding Merapi's structure, for monitoring network design and for eruption forecasting. Although small, Merapi's inter-eruptive deformation cycles are also seen in thermal data from ASTER and gas emissions. Accordingly, we interpret these cycles as the result of successive movement of individual magma batches migrating upward from deeper to shallower storage systems located along a NE dipping conduit. In view of magma migration prior to eruptions, these cycles may also serve as a precursory signal for large eruptions at Merapi, such as the one that took place in early November 2010.
•A combined analysis of D-InSAR displacement, SAR backscattering intensity, and ground surface temperature•Inter-eruptive magmatic period at Mt. Merapi•Cyclic episode of Mt. Merapi deformation•Successive movement of individual magma batches
Ocean-bottom seismometers (OBSs) are equipped with seismic sensors that record acoustic and seismic events at the seafloor, which makes them suitable for investigating tectonic structures capable of ...generating earthquakes offshore. One critical parameter to obtain accurate earthquake locations is the absolute time of the incoming seismic signals recorded by the OBSs. It is, however, not possible to synchronize the internal clocks of the OBSs with a known reference time, given that GNSS signals are unable to reach the instrument at the sea bottom. To address this issue, here we introduce a new method to synchronize the clocks of large-scale OBS deployments. Our approach relies on the theoretical time-symmetry of time-lapse (averaged) crosscorrelations of ambient seismic noise. Deviations from symmetry are attributed to clock errors. This implies that the recovered clock errors will be obscured by lapse crosscorrelations' deviations from symmetry that are not due to clock errors. Non-uniform surface wave illumination patterns are arguably the most notable source which breaks the time symmetry. Using field data, we demonstrate that the adverse effects of non-uniform illumination patterns on the recovered clock errors can be mitigated by means of a weighted least-squares inversion that is based on station-station distances. In addition, our methodology permits the recovery of timing errors at the time of deployment of the OBSs. This error can be attributed to either: i) a wrong initial time synchronization of the OBS or ii) a timing error induced by changing temperature and pressure conditions while the OBS is sunk to the ocean floor. The methodology is implemented in an open-source Python package named OCloC, and we applied it to the OBS recordings acquired in the context of the IMAGE project in and around Reykjanes, Iceland. As expected, most OBSs suffered from clock drift. Surprisingly, we found incurred timing errors at the time of deployment for most of the OBSs.
Ocean-bottom seismometers (OBSs) are equipped with seismic sensors that record acoustic and seismic events at the seafloor, which makes them suitable for investigating tectonic structures capable of ...generating earthquakes offshore. One critical parameter to obtain accurate earthquake locations is the absolute time of the incoming seismic signals recorded by the OBSs. It is, however, not possible to synchronize the internal clocks of the OBSs with a known reference time, given that GNSS signals are unable to reach the instrument at the sea bottom. To address this issue, here we introduce a new method to synchronize the clocks of large-scale OBS deployments. Our approach relies on the theoretical time-symmetry of time-lapse (averaged) crosscorrelations of ambient seismic noise. Deviations from symmetry are attributed to clock errors. This implies that the recovered clock errors will be obscured by lapse crosscorrelations' deviations from symmetry that are not due to clock errors. Non-uniform surface wave illumination patterns are arguably the most notable source which breaks the time symmetry. Using field data, we demonstrate that the adverse effects of non-uniform illumination patterns on the recovered clock errors can be mitigated by means of a weighted least-squares inversion that is based on station-station distances. In addition, our methodology permits the recovery of timing errors at the time of deployment of the OBSs. This error can be attributed to either: i) a wrong initial time synchronization of the OBS or ii) a timing error induced by changing temperature and pressure conditions while the OBS is sunk to the ocean floor. The methodology is implemented in an open-source Python package named OCloC, and we applied it to the OBS recordings acquired in the context of the IMAGE project in and around Reykjanes, Iceland. As expected, most OBSs suffered from clock drift. Surprisingly, we found incurred timing errors at the time of deployment for most of the OBSs.
This study is a metrological investigation of eight superconducting gravimeters that have operated in the Strasbourg gravimetric Observatory. These superconducting gravimeters include an older ...compact C026 model, a new observatory type iOSG23 and six iGravs (6, 15, 29, 30, 31, 32). We first compare the amplitude calibration of the meters using measurements from FG5 #206 absolute gravimeter (AG). In a next step we compute the amplitude calibration of all the meters by time regression with respect to iOSG23 itself carefully calibrated by numerous AG experiments. The relative calibration values are much more precise than absolute calibration for each instrument and strongly reduce any tidal residual signal. We also compare the time lags of the various instruments with respect to iOSG23, either by time cross-correlation or tidal analysis for the longest records (about 1 year). The instrumental drift behavior of the iGravs and iOSG23 is then investigated and we examine the relationships observed between gravity and body temperature measurements. Finally, we compare the noise levels of all the instruments. A three-channel correlation analysis is used to separate the incoherent (instrumental) noise from the coherent (ambient) noise. The self-noise is then compared to a model of thermal noise (Brownian motion) using the known instrumental parameters of the damped harmonic oscillator. The self-noise of iGrav instruments is well-explained by the thermal noise model at seismic frequencies (between 10
–3
and 10
–2
Hz). As expected, the self-noise of iOSG23 with a heavier sphere is also lower than that of iGravs at such frequencies.
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