K.H. Spaans
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4 records found
1
The Krafla volcanic system has geothermal areas within the Krafla caldera and at Bjarnarflag in the Krafla fissure swarm, 9-km south of the Krafla caldera. Arrays of boreholes extract geothermal fluids for power plants in both areas. We collected and analyzed InSAR, GPS, and leveling data spanning 1993-2015 in order to investigate crustal deformation in these areas. The volcanic zone hosting the geothermal areas is also subject to large scale regional deformation processes, including plate spreading and deflation of the Krafla volcanic system. These deformation processes have to be taken into account in order to isolate the geothermal deformation signal. Plate spreading produces the largest horizontal displacements, but the regional deformation pattern also suggests readjustment of the Krafla system at depth after the 1975-1984 Krafla rifting episode. Observed deformation can be fit by an inflation source at about 20km depth north of Krafla and a deflation source at similar depth directly below the Krafla caldera. Deflation signal along the fissure swarm can be reproduced by a 1-km wide sill at 4km depth closing by 2-4cm per year. These sources are considered to approximate the combined effects of vertical deformation associated with plate spreading and post-rifting response. Local deformation at the geothermal areas is well resolved in addition to these signals. InSAR shows that deformation at Bjarnarflag is elongated along the direction of the Krafla fissure swarm (∼ 4km by ∼ 2km) while it is circular at Krafla (∼ 5km diameter). Rates of deflation at Krafla and Bjarnarflag geothermal areas have been relatively steady. Average volume decrease of about 6.6×105 m3/yr for Krafla and 3.9×105 m3/yr for Bjanarflag are found at sources located at ∼ 1.5km depth, when interpreted by a spherical point source of pressure. This volume change represents about 8×10 -3 m3/ton of the mass of geothermal fluid extracted per year, indicating important renewal of the geothermal reservoir by water flow.
Previously we modelled individual interferograms that showed that there was a large uplift signal. We modelled this as a series of sills and a dike with a total volume of ~0.05 km3. During the flank eruption, beginning on 20 March, no significant deformation is detected, but coinciding with the start of the explosive eruption on April 14, we detected subsidence centred on the caldera. What we modelled showed us that Eyjafjallajökull was an unusual which we modelled …. This deformation does not relate to pressure changes within a single magma chamber.
Here we extend our analysis InSAR time series covering full eruptive period. After correcting for DEM errors and reduction of atmospheric signal, we have found a number of signals that we interpreted in terms of magma movement. These magma movements are separately analysed in 3 phases: pre-eruptive (inflation), co-eruptive (no deformation) and post-eruptive (deflation).
The displacement time series from June 2009 to 4 February 2010 (pre-eruptive-phase) shows line-of-sight shortening on the southwest flank of about 2 cm. The displacement signal is present in a set of interferograms and it has a consistent behaviour in time, implying that it is not due to atmospheric contamination. We performed atmospheric stratification over the entire Interferogram (4 Feb 2010) to check how much of the signal correlated with the topography would disappear when removed. The correlation coefficient over the southwest flank is very small compared with the signal from the entire interferogram. We can say that in this area not much of the atmospheric effects related with the topography are present, suggesting that the signal could be deformation.
For the co/post-eruptive phases we calculated phase difference between nearby points to check their evolution in time. In the southeast flanks we observe deflation through all analyzed period, while in western flanks of the volcano we observe Inflation during effusive eruption, followed by deflation during explosive eruption, and a new inflation pattern between 05 June and 19 July that we cannot explain. In preliminary modelling we fit this post-eruptive phase with a pressure decrease of an ellipsoidal source, equivalent to a volume reduction of ~0.03 km3.
The limitations when analysing this dataset are mainly concerning the phase unwrapping performance through ice- and ash-covered areas. This is caused by decorrelation owing to ash cover where there is almost complete loss of coherence. We applied new methods to overcome these limitations. To improve point density over the scene, we combined PS (Persistent Scatterers) and SB (Small Baselines) methods. By combining highly coherent interferograms, the increase of distributed scatterers is clear and the phase unwrapping performance improved. To detect and correct non-systematic unwrapping errors, we calculated azimuth and range offsets. Additionally, because of the fact that L band has higher penetration, we processed ALOS images trough single interferogram analysis. By these means we were able to extract more of the deformation signal around decorrelated areas.
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Previously we modelled individual interferograms that showed that there was a large uplift signal. We modelled this as a series of sills and a dike with a total volume of ~0.05 km3. During the flank eruption, beginning on 20 March, no significant deformation is detected, but coinciding with the start of the explosive eruption on April 14, we detected subsidence centred on the caldera. What we modelled showed us that Eyjafjallajökull was an unusual which we modelled …. This deformation does not relate to pressure changes within a single magma chamber.
Here we extend our analysis InSAR time series covering full eruptive period. After correcting for DEM errors and reduction of atmospheric signal, we have found a number of signals that we interpreted in terms of magma movement. These magma movements are separately analysed in 3 phases: pre-eruptive (inflation), co-eruptive (no deformation) and post-eruptive (deflation).
The displacement time series from June 2009 to 4 February 2010 (pre-eruptive-phase) shows line-of-sight shortening on the southwest flank of about 2 cm. The displacement signal is present in a set of interferograms and it has a consistent behaviour in time, implying that it is not due to atmospheric contamination. We performed atmospheric stratification over the entire Interferogram (4 Feb 2010) to check how much of the signal correlated with the topography would disappear when removed. The correlation coefficient over the southwest flank is very small compared with the signal from the entire interferogram. We can say that in this area not much of the atmospheric effects related with the topography are present, suggesting that the signal could be deformation.
For the co/post-eruptive phases we calculated phase difference between nearby points to check their evolution in time. In the southeast flanks we observe deflation through all analyzed period, while in western flanks of the volcano we observe Inflation during effusive eruption, followed by deflation during explosive eruption, and a new inflation pattern between 05 June and 19 July that we cannot explain. In preliminary modelling we fit this post-eruptive phase with a pressure decrease of an ellipsoidal source, equivalent to a volume reduction of ~0.03 km3.
The limitations when analysing this dataset are mainly concerning the phase unwrapping performance through ice- and ash-covered areas. This is caused by decorrelation owing to ash cover where there is almost complete loss of coherence. We applied new methods to overcome these limitations. To improve point density over the scene, we combined PS (Persistent Scatterers) and SB (Small Baselines) methods. By combining highly coherent interferograms, the increase of distributed scatterers is clear and the phase unwrapping performance improved. To detect and correct non-systematic unwrapping errors, we calculated azimuth and range offsets. Additionally, because of the fact that L band has higher penetration, we processed ALOS images trough single interferogram analysis. By these means we were able to extract more of the deformation signal around decorrelated areas.