ALBERT

Beschreibung: Summary On 24th August 2016 at 01:36 UTC a ML6.0 earthquake struck several villages in central Italy, among which Accumoli, Amatrice and Arquata del Tronto. The earthquake was recorded by about 350 seismic stations, causing 299 fatalities and damage with macroseismic intensities up to 11. The maximum acceleration was observed at Amatrice station (AMT) reaching 916 cm/s2 on E-W component, with epicentral distance of 15 km and Joyner and Boore distance to the fault surface (RJB) of less than a kilometre. Motivated by the high levels of observed ground motion and damage, we generate broadband seismograms for engineering purposes by adopting a hybrid method. To infer the low frequency seismograms, we considered the kinematic slip model by Tinti et al. (2016). The high frequency seismograms were produced using a stochastic finite-fault model approach based on dynamic corner-frequency. Broadband synthetic time series were therefore obtained by merging the low and high frequency seismograms. Simulated hybrid ground motions were compared both with the observed ground motions and the ground-motion prediction equations (GMPEs), to explore their performance and to retrieve the region-specific parameters endorsed for the simulations. In the near-fault area we observed that hybrid simulations have a higher capability to detect near source effects and to reproduce the source complexity than the use of GMPEs. Indeed, the general good consistency found between synthetic and observed ground motion (both in the time and frequency domain), suggests that the use of regional-specific source scaling and attenuation parameters together with the source complexity in hybrid simulations improves ground motion estimations. To include the site effect in stochastic simulations at selected stations, we tested the use of amplification curves derived from HVRSs (horizontal-to-vertical response spectra) and from HVSRs (horizontal-to-vertical spectral ratios) rather than the use of generic curves according to NTC-18 Italian seismic design code. We generally found a further reduction of residuals between observed and simulated both in terms of time histories and spectra.

Beschreibung: Summary We explore here the benefits of using constraints from seismic tomography in gravity data inversion and how inverted density distributions can be improved by doing so. The methodology is applied to a real field case in which we reconstruct the density structure of the Pyrenees along a southwest-northeast transect going from the Ebro basin in Spain to the Arzacq basin in France. We recover the distribution of densities by inverting gravity anomalies under constraints coming from seismic tomography. We initiate the inversion from a prior density model obtained by scaling a pre-existing compressional seismic velocity Vp model using a Nafe-Drake relationship : the Vp model resulting from a full-waveform inversion of teleseismic data. Gravity data inversions enforce structural similarities between Vp and density by minimizing the norm of the cross-gradient between the density and Vp models. We also compare models obtained from 2.5D and 3D inversions. Our results demonstrate that structural constraints allow us to better recover the density contrasts close to the surface and at depth, without degrading the gravity data misfit. The final density model provides valuable information on the geological structures and on the thermal state and composition of the western region of the Pyrenean lithosphere.

Beschreibung: Summary The relatively short duration of the early stages of subduction results in a poor geological record, limiting our understanding of this critical stage. Here, we utilize a 2D numerical model of incipient subduction, that is the stage after a plate margin has formed with a slab tip that extends to a shallow depth and address the conditions under which subduction continues or fails. We assess energy budgets during the evolution from incipient subduction to either a failed or successful state, showing how the growth of potential energy, and slab pull, is resisted by the viscous dissipation within the lithosphere and the mantle. The role of rheology is also investigated, as deformation mechanisms operating in the crust and mantle facilitate subduction. In all models, the onset of subduction is characterized by high lithospheric viscous dissipation and low convergence velocities, whilst successful subduction sees the mantle become the main area of viscous dissipation. In contrast, failed subduction is defined by the lithospheric viscous dissipation exceeding the lithospheric potential energy release rate and velocities tend towards zero. We show that development of a subduction zone depends on the convergence rate, required to overcome thermal diffusion and to localise deformation along the margin. The results propose a minimum convergence rate of ∼ 0.5 cm yr−1 is required to reach a successful state, with 100 km of convergence over 20 Myr, emphasizing the critical role of the incipient stage.

Beschreibung: Summary Although many studies have revealed that the atmospheric effects of electromagnetic wave propagation (including ionospheric and tropospheric water vapor) have serious impacts on Interferometric Synthetic Aperture Radar (InSAR) measurement results, atmospheric corrections have not been thoroughly and comprehensively investigated in many well-known cases of InSAR focal mechanism solutions, which means there is no consensus on whether atmospheric effects will affect the InSAR focal mechanism solution. Moreover, there is a lack of quantitative assessment on how much the atmospheric effect affects the InSAR focal mechanism solution. In this paper, we emphasized that it was particularly important to assess the impact of InSAR ionospheric and tropospheric corrections on the underground nuclear explosion modeling quantitatively. Therefore, we investigated the 4th North Korea (NKT-4) underground nuclear test using ALOS-2 liters-band SAR images. Because the process of the underground nuclear explosion was similar to the volcanic magma source activity, we modeled the ground displacement using the Mogi model. Both the ionospheric and tropospheric phase delays in the interferograms were investigated. Furthermore, we studied how the ionosphere and troposphere phase delays could bias the estimation of Mogi source parameters. The following conclusions were drawn from our case study: The ionospheric delay correction effectively mitigated the long-scale phase ramp in the full-frame interferogram, the standard deviation decreased from 1.83 cm to 0.85 cm compared to the uncorrected interferogram. The uncorrected estimations of yield and depth were 8.44 kt and 370.33 m, respectively. Compared to the uncorrected estimations, the ionospheric correction increased the estimation of yield and depth to 9.43 kt and 385.48 m while the tropospheric correction slightly raised them to 8.78 kt and 377.24 m. There were no obvious differences in the location estimations among the four interferograms. When both corrections were applied, the overall standard deviation was 1.16 cm, which was even larger than the ionospheric corrected interferogram. We reported the source characteristics of NKT-4 based on the modeling results derived from the ionospheric corrected interferogram. The preferred estimation of NKT-4 was a Mogi source located at 129°04′22.35‘E, 41°17′54.57″N buried at 385.48 m depth. The cavity radius caused by the underground explosion was 22.02 m. We reported the yield estimation to be 9.43 kt. This study showed that for large-scale natural deformation sources such as volcanoes and earthquakes, atmospheric corrections would be more significant, but even if the atmospheric signal did not have much complexity, the corrections should not be ignored.

Beschreibung: SUMMARY Self-consistent modelling of magmatic systems is challenging as the melt continuously changes its chemical composition upon crystallization, which may affect the mechanical behaviour of the system. Melt extraction and subsequent crystallization create new rocks while depleting the source region. As the chemistry of the source rocks changes locally due to melt extraction, new calculations of the stable phase assemblages are required to track the rock evolution and the accompanied change in density. As a consequence, a large number of isochemical sections of stable phase assemblages are required to study the evolution of magmatic systems in detail. As the state-of-the-art melting diagrams may depend on nine oxides as well as pressure and temperature, this is a 10-D computational problem. Since computing a single isochemical section (as a function of pressure and temperature) may take several hours, computing new sections of stable phase assemblages during an ongoing geodynamic simulation is currently computationally intractable. One strategy to avoid this problem is to pre-compute these stable phase assemblages and to create a comprehensive database as a hyperdimensional phase diagram, which contains all bulk compositions that may emerge during petro-thermomechanical simulations. Establishing such a database would require repeating geodynamic simulations many times while collecting all requested compositions that may occur during a typical simulation and continuously updating the database until no additional compositions are required. Here, we describe an alternative method that is better suited for implementation on large-scale parallel computers. Our method uses the entries of an existing preliminary database to estimate future required chemical compositions. Bulk compositions are determined within boundaries that are defined manually or through principal component analysis in a parameter space consisting of clustered database entries. We have implemented both methods within a massively parallel computational framework while utilizing the Gibbs free energy minimization program Perple_X. Results show that our autonomous approach increases the resolution of the thermodynamic database in compositional regions that are most likely required for geodynamic models of magmatic systems.

Beschreibung: SUMMARY We present a statistical rock physics inversion of the elastic and electrical properties to estimate the petrophysical properties and quantify the associated uncertainty. The inversion method combines statistical rock physics modeling with Bayesian inverse theory. The model variables of interest are porosity and fluid saturations. The rock physics model includes the elastic and electrical components and can be applied to the results of seismic and electromagnetic inversion. To describe the non-Gaussian behaviour of the model properties, we adopt non-parametric probability density functions to sample multimodal and skewed distributions of the model variables. Different from machine learning approach, the proposed method is not completely data-driven but is based on a statistical rock physics model to link the model parameters to the data. The proposed method provides pointwise posterior distributions of the porosity and CO2 saturation along with the most-likely models and the associated uncertainty. The method is validated using synthetic and real data acquired for CO2 sequestration studies in different formations: the Rock Springs Uplift in Southwestern Wyoming and the Johansen formation in the North Sea, offshore Norway. The proposed approach is validated under different noise conditions and compared to traditional parametric approaches based on Gaussian assumptions. The results show that the proposed method provides an accurate inversion framework where instead of fitting the relationship between the model and the data, we account for the uncertainty in the rock physics model.