The 1991 eruption of Hekla,Iceland

The eruption that started in the Hekla volcano in South Iceland on 17 January 1991, and came to an end on 11 March, produced mainly andesitic lava. This lava covers 23 km² and has an estimated volume of 0.15 km³. This is the third eruption in only 20 years, whereas the average repose period since 1104 is 55 years. Earthquakes, as well as a strain pulse recorded by borehole strainmeters, occurred less than half an hour before the start of the eruption. The initial plinian phase was very short-lived, producing a total of only 0.02 km³ of tephra. The eruption cloud attained 11.5 km in height in only 10 min, but it became detached from the volcano a few hours later. Several fissures were active during the first day of the eruption, including a part of the summit fissure. By the second day, however, the activity was already essentially limited to that segment of the principal fissure where the main crater subsequently formed. The average effusion rate during the first two days of the eruption was about 800 m³ s^–1. After this peak, the effusion rate declined rapidly to 10–20 m³ s^–1, then more slowly to 1 m³ s^–1, and remained at 1–12 m³ s^–1 until the end of the eruption. Site observations near the main crater suggest that the intensity of the volcanic tremor varied directly with the force of the eruption. A notable rise in the fluorine concentration of riverwater in the vicinity of the eruptive fissures occurred on the 5th day of the eruption, but it levelled off on the 6th day and then remained essentially constant. The volume and initial silica content of the lava and tephra, the explosivity and effusion rate during the earliest stage of the eruption, as well as the magnitude attained by the associated earthquakes, support earlier suggestions that these parameters are positively related to the length of the preceeding repose period. The chemical difference between the eruptive material of Hekla itself and the lavas erupted in its vicinity can be explained in terms of a density-stratified magma reservoir located at the bottom of the crust. We propose that the shape of this reservoir, its location at the west margin of a propagating rift, and its association with a crustal weakness, all contribute to the high eruption frequency of Hekla.     

Assessment of ground motion variability and its effects on seismic hazard analysis: a case study for iceland

Probabilistic seismic hazard analysis (PSHA) generally relies on the basic assumption that ground motion prediction equations (GMPEs) developed for other similar tectonic regions can be adopted in the considered area. This implies that observed ground motion and its variability at considered sites could be modelled by the selected GMPEs. Until now ground-motion variability has been taken into account in PSHA by integrating over the standard deviation reported in GMPEs, which significantly affects estimated ground motions, especially at very low probabilities of exceedance. To provide insight on this issue, ground-motion variability in the South Iceland Seismic Zone (SISZ), where many ground-motion records are available, is assessed. Three statistical methods are applied to separate the aleatory variability into source (inter-event), site (inter-site) and residual (intra-event and intra-site) components. Furthermore, the current PSHA procedure that makes the ergodic assumption of equality between spatially and temporal variability is examined. In contrast to the ergodic assumption, several recent studies show that the observed ground-motion variability at an individual location is lower than that implied by the standard deviation of a GMPE. This could imply a mishandling of aleatory uncertainty in PSHA by ignoring spatial variability and by mixing aleatory and epistemic uncertainties in the computation of sigma. Station correction coefficients are introduced in order to capture site effects at different stations. The introduction of the non-ergodic assumption in PSHA leads to larger epistemic uncertainty, although this is not the same as traditional epistemic uncertainty modelled using different GMPEs. The epistemic uncertainty due to the site correction coefficients (i.e. mean residuals) could be better constrained for future events if more information regarding the characteristics of these seismic sources and path dependence could be obtained.     

Stochastic models for simulation of strong ground motion in Iceland

Two types of modelling approaches for simulating ground motion in Iceland are studied and compared. The first type of models, named discrete‐time series models (ARMA), are based solely on measured acceleration in earthquakes occurring in Iceland. The second type of models are based on a theoretical seismic source model called the extended Brune model. Based on measured acceleration in Iceland during the period 1986–1996, the parameters for the extended Brune models have been estimated. The seismic source models are presented here as ARMA models, which simplifies the simulation process. A single‐layer soil amplification model is used in conjunction with the extended Brune model to estimate local site amplification. Emphasis is put on the ground motion models representing the variability in the measured earthquakes, with respect to energy, duration and frequency content. Demonstration is made using these models for constructing linear and non‐linear probabilistic response spectra using a discretised version of the Bouc–Wen model for the hysteresis of the second‐order system. Copyright © 2001 John Wiley & Sons, Ltd.     

Well Conditioning in Object Models

This paper presents two object models with corresponding simulation algorithms, which aim to condition well data correctly while still converging in reasonable time. The first model is devoted to fluvial channels and the second one is mainly intended for smaller objects. To verify the conditioning, a method for validating well conditioning algorithms for object models is given. The purpose is to determine the extent to which the well conditioning introduces a bias in the models. To do this, we check that the double expectation of a parameter conditioned to wells is equal to the unconditional expectation. This method is applied to two different object models. Both the conditioning algorithms presented here give good results using this test.