Structural complexity inferred from anisotropic resistivity: Example from airborne EM and compilation of historical resistivity/induced polarization data from the gold-rich Canadian Malartic district, Québec, Canada

This paper is © 2019 Society of Exploration Geophysicists. The posting is available free of charge and its use is subject to the SEG terms and conditions: https://seg.org/Terms-of-UseStructurally complex zones within orogenic terranes typically correspond to areas where there is interference between multiple fold generations and are known to be favorable pathways for fluid flow because of their higher permeability. In the Canadian Malartic district, gold anomalies have been linked with zones of structural complexity that have been quantified by outcrop bedding orientation measurements and calculation of bedding variance maps. In this work, historical apparent resistivity and induced polarization data in the Canadian Malartic district were reprocessed and combined with new surveys to create a compilation of inverted chargeability and resistivity, which were then interpreted together with airborne electromagnetics and outcrop structural data. The results indicate chargeability anomalies, up to five times the background value, associated with the sulfide mineral content in monzodioritic dikes that are thickened in folds and hydrothermally altered. Although the airborne apparent half-space resistivity is mostly sensitive to conductive surficial cover, the inverted ground resistivity method is sensitive to deeper structure and likely represents bedrock signal at depths greater than 25 m. Inverted ground resistivity exhibits strong anisotropy in areas of subvertical bedding, where measured resistivities can vary by up to a factor of two, over the same location, depending on whether the survey lines are perpendicular or parallel to the strike of bedding. This result is observed at scales of 50 cm up to 100 m. Analysis of inverted ground resistivity together with bedding variance indicates a strong correlation between structurally complex zones with high bedding variance and a decrease in resistivity at depths greater than 25 m. This suggests that in places where the presence of disseminated gold cannot be directly detected, or where the outcrop exposure is limited due to overburden cover, geophysical data may still succeed in identifying structural complexity zones that could potentially host mineralization.Natural Sciences and Engineering Research Council of Canada and the Canada Mining Innovation Council (NSERC-CMIC Mineral Exploration Footprints Project Contribution 178

Integrated Multi-Parameter Exploration Footprints of the Canadian Malartic Disseminated Au, McArthur River-Millennium Unconformity U, and Highland Valley Porphyry Cu Deposits: Preliminary Results from the NSERC-CMIC Mineral Exploration Footprints Research Network

Mineral exploration in Canada is increasingly focused on concealed and deeply buried targets, requiring more effective tools to detect large-scale ore-forming systems and to vector from their most distal margins to their high grade cores. A new generation of ore system models is required to achieve this. The Mineral Exploration Footprints Research Network is a consortium of 70 faculty, research associates, and students from 20 Canadian universities working with 30 mining, mineral exploration, and mining service providers to develop new approaches to ore system modelling based on more effective integration and visualization of multi-parameter geological-structural-mineralogical-lithogeochemical-petrophysical-geophysical exploration data. The Network is developing the next generation ore system models and exploration strategies at three sites based on integrated data visualization using self-consistent 3D Common Earth Models and geostatistical/machine learning technologies. Thus far over 60 footprint components and vectors have been identified at the Canadian Malartic stockwork-disseminated Au deposit, 20–30 at the McArthur-Millennium unconformity U deposits, and over 20 in the Highland Valley porphyry Cu system. For the first time, these are being assembled into comprehensive models that will serve as landmark case studies for data integration and analysis in the today’s challenging exploration environment

Mélange à quatre ondes en bord de bande d'un cristal photonique 1D

International audienc

Mélange à quatre ondes en bord de bande d'un cristal photonique 1D

Nous présentons ici une expérience de mélange à quatre ondes pour la caractérisation de non-linéarités optiques du troisième ordre dans des cristaux photoniques 1D. Nous avons montré que la structuration du matériau permettait d'augmenter l'efficacité des processus non-linéaires grâce aux propriétés de localisation de la lumière en bord de bande interdite du cristal photonique

opesci/devito: Devito-3.5

Release notes MPI support: Python-level: MPI-distributed NumPy arrays. C-level: code generation for sub-domains, staggered grids, operators with coupled PDEs. C-level: performance optimizations (e.g., computation-communication overlap). Lazy evaluation of derivatives. Revisited staggered grids API (now Dimension-based, previously mask-based). Re-engineered clustering (which means smarter loop fusion/fission). DSE: Improved aliases detection. DLE: OpenMP nested parallelism; hierarchical loop blocking. Auto-padding for Functions/TimeFunctions. Improved data dependency analysis. Smarter Operator auto-tuning. New tutorials: Operator application, MPI, new propagators, custom stencils, and more. Revisited benchmarking scripts. Revisited examples, new models and propagators (e.g., visco-elastic). Smarter continuous integration: now Travis sided by Azure Pipelines; dropped Jenkins. Misc bug fixes. Hundreds of tests added. More sophisticated platform auto-detection.Release notes MPI support: Python-level: MPI-distributed NumPy arrays. C-level: code generation for sub-domains, staggered grids, operators with coupled PDEs. C-level: performance optimizations (e.g., computation-communication overlap). Lazy evaluation of derivatives. Revisited staggered grids API (now Dimension-based, previously mask-based). Re-engineered clustering (which means smarter loop fusion/fission). DSE: Improved aliases detection. DLE: OpenMP nested parallelism; hierarchical loop blocking. Auto-padding for Functions/TimeFunctions. Improved data dependency analysis. Smarter Operator auto-tuning. New tutorials: Operator application, MPI, new propagators, custom stencils, and more. Revisited benchmarking scripts. Revisited examples, new models and propagators (e.g., visco-elastic). Smarter continuous integration: now Travis sided by Azure Pipelines; dropped Jenkins. Misc bug fixes. Hundreds of tests added. More sophisticated platform auto-detection.3.

opesci/devito: Devito-4.0

Tensor algebra support (#873): VectorFunction and VectorTimeFunction (2nd order) TensorFunction and TensorTimeFunction Full support for FD and related operations (derivatives, shortcuts, solve, ...) Differential operators such as div, grad and curl FD extensions: Custom FD with user-supplied coefficients as Function (#964) Extended and more rigorous support for staggered grids (#873): True half-grid staggering (u(x + h_x/2)) Automatic evaluation at half-nodes (averaging only) Automatic staggered FD of any orderTensor algebra support (#873): VectorFunction and VectorTimeFunction (2nd order) TensorFunction and TensorTimeFunction Full support for FD and related operations (derivatives, shortcuts, solve, ...) Differential operators such as div, grad and curl FD extensions: Custom FD with user-supplied coefficients as Function (#964) Extended and more rigorous support for staggered grids (#873): True half-grid staggering (u(x + h_x/2)) Automatic evaluation at half-nodes (averaging only) Automatic staggered FD of any order4.