Three-dimensional distribution of primary melt inclusions in garnets by X-ray microtomography

open6X-ray computed microtomography (X-mu CT) is applied here to investigate in a non-invasive way the three-dimensional (3D) spatial distribution of primary melt and fluid inclusions in gamets from the metapeitic enclaves of El Hoyazo and from the migmatitcs of Sierra Alpujata, Spain. Attention is focused on a particular case of inhomogeneous distribution of inclusions, characterized by inclusion-rich cores and almost inclusion-free rims (i.e., zonal arrangement), that has been previously investigated in detail only by means of 2D conventional methods. Different experimental X-mu CT configurations, both synchrotron radiation- and X-ray tube-based, are employed to explore the limits of the technique. The internal features of the samples are successfully imaged, with spatial resolution down to a few micrometers. By means of dedicated image processing protocols, the lighter melt and fluid inclusions can be separated from the heavier host garnet and from other non-relevant features (e.g., other mineral phases or large voids). This allows evaluating the volumetric density of inclusions within spherical shells as a function of the radial distance from the center of the host garnets. The 3D spatial distribution of heavy mineral inclusions is investigated as well and compared with that of melt inclusions. Data analysis reveals the occurrence of a clear peak of melt and fluid inclusions density, ranging approximately from 1/3 to 1/2 of the radial distance from the center of the distribution and a gradual decrease from the peak outward. heavy mineral inclusions appear to be almost absent in the central portion of the garnets and more randomly arranged, showing no correlation with the distribution of melt and fluid inclusions. To reduce the effect of geometric artifacts arising from the non-spherical shape of the distribution, the inclusion density was calculated also along narrow prisms with different orientations, obtaining plots of pseudo-linear distributions. The results show that the core-rim transition is characterized by a rapid (but not step-like) decrease in inclusion density, occurring in a continuous mode. X-ray tomographic data, combined with electron microprobe chemical profiles of selected elements, suggest that despite the inhomogeneous distribution of inclusions, the investigated garnets have grown in one single progressive episode in the presence of anatectic melt. The continuous drop of inclusion density suggests a similar decline in (radial) garnet growth, which is a natural consequence in the case of a constant reaction rate. Our results confirm the advantages of high-resolution X-mu CT compared to conventional destructive 2D observations for the analysis of the spatial distribution of micrometer-scale inclusions in minerals, owing to its non-invasive 3D capabilities. The same approach can be extended to the study of different microstructural features in samples from a wide variety of geological settings.openParisatto, Matteo; Turina, Alice; Cruciani, Giuseppe; Mancini, Lucia; Peruzzo, Luca; Cesare, BernardoParisatto, Matteo; Turina, Alice; Cruciani, Giuseppe; Mancini, Lucia; Peruzzo, Luca; Cesare, Bernard

Parallel 3-D marine controlled-source electromagnetic modelling using high-order tetrahedral Nédélec elements

We present a parallel and high-order Nédélec finite element solution for the marine controlled-source electromagnetic (CSEM) forward problem in 3-D media with isotropic conductivity. Our parallel Python code is implemented on unstructured tetrahedral meshes, which support multiple-scale structures and bathymetry for general marine 3-D CSEM modelling applications. Based on a primary/secondary field approach, we solve the diffusive form of Maxwell’s equations in the low-frequency domain. We investigate the accuracy and performance advantages of our new high-order algorithm against a low-order implementation proposed in our previous work. The numerical precision of our high-order method has been successfully verified by comparisons against previously published results that are relevant in terms of scale and geological properties. A convergence study confirms that high-order polynomials offer a better trade-off between accuracy and computation time. However, the optimum choice of the polynomial order depends on both the input model and the required accuracy as revealed by our tests. Also, we extend our adaptive-meshing strategy to high-order tetrahedral elements. Using adapted meshes to both physical parameters and high-order schemes, we are able to achieve a significant reduction in computational cost without sacrificing accuracy in the modelling. Furthermore, we demonstrate the excellent performance and quasi-linear scaling of our implementation in a state-of-the-art high-performance computing architecture.This project has received funding from the European Union's Horizon 2020 programme under the Marie Sklodowska-Curie grant agreement No. 777778. Furthermore, the research leading to these results has received funding from the European Union's Horizon 2020 programme under the ChEESE Project (https://cheese-coe.eu/ ), grant agreement No. 823844. In addition, the authors would also like to thank the support of the Ministerio de Educación y Ciencia (Spain) under Projects TEC2016-80386-P and TIN2016-80957-P. The authors would like to thank the Editors-in-Chief and to both reviewers, Dr. Martin Cuma and Dr. Raphael Rochlitz, for their valuable comments and suggestions which helped to improve the quality of the manuscript. This work benefited from the valuable suggestions, comments, and proofreading of Dr. Otilio Rojas (BSC). Last but not least, Octavio Castillo-Reyes thanks Natalia Gutierrez (BSC) for her support in CSEM modeling with BSIT.Peer ReviewedPostprint (author's final draft

Enhancing Energy Production with Exascale HPC Methods

High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the Intel Corporation, which enabled us to obtain the presented experimental results in uncertainty quantification in seismic imagingPostprint (author's final draft

Predicting the movements of permanently installed electrodes on an active landslide using time-lapse geoelectrical resistivity data only

If electrodes move during geoelectrical resistivity monitoring and their new positions are not incorporated in the inversion, then the resulting tomographic images exhibit artefacts that can obscure genuine time-lapse resistivity changes in the subsurface. The effects of electrode movements on time-lapse resistivity tomography are investigated using a simple analytical model and real data. The correspondence between the model and the data is sufficiently good to be able to predict the effects of electrode movements with reasonable accuracy. For the linear electrode arrays and 2D inversions under consideration, the data are much more sensitive to longitudinal than transverse or vertical movements. Consequently the model can be used to invert the longitudinal offsets of the electrodes from their known baseline positions using only the time-lapse ratios of the apparent resistivity data. The example datasets are taken from a permanently installed electrode array on an active lobe of a landslide. Using two sets with different levels of noise and subsurface resistivity changes, it is found that the electrode positions can be recovered to an accuracy of 4 % of the baseline electrode spacing. This is sufficient to correct the artefacts in the resistivity images, and provides for the possibility of monitoring the movement of the landslide and its internal hydraulic processes simultaneously using electrical resistivity tomography only

Tomographic inversion of time-domain resistivity and chargeability data for the investigation of landfills using a priori information

In this paper, we present a new code for the modelling and inversion of resistivity and chargeability data using a priori information to improve the accuracy of the reconstructed model for landfill. When a priori information is available in the study area, we can insert them by means of inequality constraints on the whole model or on a single layer or assigning weighting factors for enhancing anomalies elongated in the horizontal or vertical directions. However, when we have to face a multilayered scenario with numerous resistive to conductive transitions (the case of controlled landfills), the effective thickness of the layers can be biased. The presented code includes a model-tuning scheme, which is applied after the inversion of field data, where the inversion of the synthetic data is performed based on an initial guess, and the absolute difference between the field and synthetic inverted models is minimized. The reliability of the proposed approach has been supported in two real-world examples; we were able to identify an unauthorized landfill and to reconstruct the geometrical and physical layout of an old waste dump. The combined analysis of the resistivity and chargeability (normalised) models help us to remove ambiguity due to the presence of the waste mass. Nevertheless, the presence of certain layers can remain hidden without using a priori information, as demonstrated by a comparison of the constrained inversion with a standard inversion. The robustness of the above-cited method (using a priori information in combination with model tuning) has been validated with the cross-section from the construction plans, where the reconstructed model is in agreement with the original design