Isosurface extraction and interpretation on very large datasets in geophysics

International audienceIn order to deal with the heavy trend in size increase of volumetric datasets, research in isosurface extraction has focused in the past few years on related aspects such as surface simplification and load balanced parallel algorithms. We present in this paper a parallel, bloc-wise extension of the tandem algorithm [Attali et al. 2005], which simplifies on the fly an isosurface being extracted. Our approach minimizes the overall memory consumption using an adequate bloc splitting and merging strategy and with the introduction of a component dumping mechanism that drastically reduces the amount of memory needed for particular datasets such as those encountered in geophysics. As soon as detected, surface components are migrated to the disk along with a meta-data index (oriented bounding box, volume, etc) that will allow further improved exploration scenarios (small components removal or particularly oriented components selection for instance). For ease of implementation, we carefully describe a master and slave algorithm architecture that clearly separates the four required basic tasks. We show several results of our parallel algorithm applied on a 7000×1600×2000 geophysics dataset

3D geophysical modelling used for structural interpretation in southern Mali and northeastern Guinea, West Africa

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements of the Master degree in Science. 2017.This study presents the combined processing, integration and inverse modelling of magnetic and gravity data for first-order crustal scale structures in southern Mali and northeast of Guinea. Southern Mali and northeast Guinea form part of the Palaeoproterozoic Baoulé-Mossi domain, which is part of the West African craton (WAC). The current understanding of the geology region is limited to exploration camp-scale studies with limited borehole investigations, and regional interpretations of historic geophysical datasets. In this study geophysical modelling is used to attempt to advance the understanding of the geology at depth. The combination of geophysical methods is an optimization that can support geophysical interpretations and contribute to the determination of the geological and structural characteristics that are important in understanding the subsurface geology. Geophysical inversion modelling broadly resolved geology and structures under thick sedimentary cover (850-1100, thick) that is interpreted as comprising basinal sediments of the Taoudenni basin, or Cretaceous ferricrete. Geological constraints reduced the non-uniqueness, but could not control the quality. Nonetheless, the architecture, geometry and form of structures and dykes were predicted when compared with experimental analogue models as being a reasonable predictive tool for the behaviour of structures and dykes at depth. The use of surface physical properties added more information to the inversion modelling, but was very limited. The enhancement of magnetic and gravity data, using filters, defined tip damage zones for firstorder scale Yanfolila and Banifing shear zones that host gold mineralisation for example, at the Morila gold mine, and Kalana, Kodieran mines and Komana prospects. Second-order structures were also defined including in the tip damage zones of the Siguiri, Fatou and Syama shear zones, and the Manakoro fault, Madina-Yanfolila fault and Madina fault.LG201

Electrical Resistivity Tomography for Mapping Subsurface Remediation

Cleaning up sites contaminated with dense non-aqueous phase liquids (DNAPLs) remains a challenging geoenvironmental problem. The performance of site remediation methods is difficult to assess without a practical, non-destructive technique to map where and how quickly DNAPL mass is being reduced. The promise of electrical resistivity tomography (ERT) in this context has not been realized, in part because traditional ERT methods were used to solve the near-impossible problem of mapping the initial DNAPL outline. However, new developments in ERT have emerged that focus on resolving subsurface changes over time. The objective of this work was to evaluate the potential of time-lapse ERT for mapping DNAPL mass reduction during remediation. A new numerical model was developed to explore this potential at the field scale, generating realistic DNAPL scenarios and predicting the response of an ERT survey. Central to the model was the development of a novel linkage between hydrogeological and geoelectrical properties. Sensitivity studies conducted at a variety of scales demonstrated that the linkage routine is robust and the DNAPL-ERT model is a valuable research tool. Moving forward to consider site applications, a new time-lapse method, four-dimensional (4D, three spatial dimensions plus time) ERT, was identified as highly promising. A laboratory experiment was conducted that demonstrated, for the first time, the effectiveness of 4D ERT applied at the surface for mapping an evolving DNAPL distribution. Independent simulation of the experiment demonstrated the reliability of the DNAPL-ERT model for simulating real systems. The numerical model was then used to explore the 4D surface ERT approach at the field scale for monitoring a range of realistic DNAPL remediation scenarios. The approach showed excellent potential for mapping shallow DNAPL changes but deeper changes were not as well resolved. To overcome this limitation, a new surface-to-horizontal borehole (S2HB) ERT configuration was proposed. The potential benefit of this innovation was first demonstrated by using the numerical model to compare surface ERT to S2HB ERT for a realistic, field scale DNAPL scenario with remediation at depth. A second laboratory experiment then demonstrated that this new configuration does better resolve changes in DNAPL distribution relative to surface ERT, particularly at depth. Independent simulation of the experiment showed that S2HB ERT is reliably modelled. Overall, this research has substantially advanced ERT in the context of DNAPL sites, with novel contributions to theory, modelling, demonstrations with physical systems, and simulations of realistic field scenarios. As a whole, this work demonstrates that, with these innovations, ERT exhibits significant potential as a DNAPL remediation site monitoring tool

3D printing porous proxies as a new tool for laboratory and numerical analyses of sedimentary rocks

The study of geological processes at the pore-scale has significant implications to understanding many real-world phenomena related to flow in porous media (e.g., hydrogeology, petroleum geology and engineering, CO2 sequestration). While numerical and experimental analyses of sedimentary-rock pore systems have advanced to the characterization of nanometer-scale features, correlation of data across multiple scales of investigation (e.g., between seismic data, core samples, thin-section images, and SEM images) is still challenging. The differences arise in petrophysical properties (e.g., permeability) calculated on the same pore network under varying experimental conditions (e.g., pressure, temperature). 3D printing is a rapidly evolving technology that enables the manufacture of intricate 3D pore-network models (defined in this research as proxies) that can be investigated experimentally and compared to numerical simulations repeatedly. The main objective of my Ph.D. research has been to improve our understanding of the accuracy of 3D-printed pore networks in comparison to natural rocks. In addition, the researched aimed at: 1) the improvement of building and post-processing workflows for accurate geometric replication of pore networks by each 3D printing technique; 2) the establishment and enhancement of validation workflows to test transport properties of rock proxies (e.g., porosity and permeability); and 3) the characterization of artifacts related to 3D printing, post-processing, and validation methods for several common 3D printing methods. While all 3D printers build models layer-by-layer, the physical and chemical properties of build materials, the build process itself, and post-processing methods vary widely. My research results provide the extent to which major 3D printing techniques (binder jet, polyjet, stereolithography, and fused depositional modelling) and associated materials (powders, polymers, resins, and plastics) can generate useful proxies of common porous sandstones (Idaho gray, Berea, and Fontainebleau) that can be tested in the laboratory as natural porous rocks. The accuracy and resolution of each technique was evaluated by testing the 3D printers with simple pore proxies (built from simple numerical models) and natural rock proxies (built from computed tomography data of natural porous rocks). With future advances in 3D printer resolution and materials, the fidelity with which we can reproduce natural rock pore systems should improve

Detecting graves in GPR data: assessing the viability of machine learning for the interpretation of graves in B-scan data using medieval Irish case studies.

As commercial archaeogeophysical survey progressively shifts towards large landscape-scale surveys, small features like graves become more difficult to identify and interpret. In order to increase the rate and confidence of grave identification before excavation using geophysical methods, the accuracy and speed of survey outputs and reporting must be improved. The approach taken in this research was first to consider the survey parameters that govern the effectiveness of the four conventional techniques used in commercial archaeogeophysical evaluations (magnetometry, earth resistance, electromagnetic induction and ground-penetrating radar). Subsequently, in respect of ground-penetrating radar (GPR), this research developed machine learning applications to improve the speed and confidence of detecting inhumation graves. The survey parameters research combined established survey guidelines for the UK, Ireland, and Europe to account for local geology, soils and land cover to provide survey guidance for individual sites via a decision-based application linked to GIS database. To develop two machine learning tools for localising and probability scoring grave-like responses in GPR data, convolutional neural networks and transfer learning were used to analyse radargrams of medieval graves and timeslices of modern proxy clandestine graves. Models were c. 93% accurate at labelling images as containing a grave or no grave and c. 96% accurate in labelling and locating potential graves in radargram images. For timeslices, machine learning models achieved 94% classification accuracy. The >90% accuracy of the machine learning models demonstrates the viability of machine-assisted detection of inhumation graves within GPR data. While the expansion of the training dataset would further improve the accuracy of the proposed methods, the current machine-led interpretation methods provide valuable assistance for human-led interpretation until more data becomes available. The survey guidance tool and the two machine learning applications have been packaged into the Reilig web application toolset, which is freely available

Ground Penetrating Radar Imaging and Systems

The ASCE confers an overall D+ grade to American infrastructure, while the NAE lists the restoration and improvement of urban infrastructure as one of its grand engineering challenges for the 21st century, indicating that infrastructure renovation and development is a major challenge in the US. Furthermore, according to the UN World Urbanization Prospects, about 55% of the world\u27s population lives in urban areas and this percentage is set to grow, especially in Africa and Asia. The growth of urban population poses challenges to the expansion of underground infrastructure, such as water, sewage, electricity and telecommunications. Localization and mapping of underground infrastructure are fundamental for infrastructure maintenance and development. Ground penetrating radar (GPR) is a remote sensing method capable of detecting subsurface assets that has been used in the localization and mapping of underground utilities. This thesis contributes improvements of GPR systems and imaging algorithms towards smarter infrastructure, specifically: Application of GPR imaging algorithm to improve GPR data readability and generate augmented reality (AR) content; Use of photogrammetric methods to improve GPR positioning for underground infrastructure localization and mapping

ToScA North America (6 – 8 June 2017, The University of Texas, Austin, TX) Program

ToScA North America will address key areas of science, including Multi-modal Imaging, Geosciences, Forensics, Increasing Contrast, Educational Outreach, Data, Materials Science and Medical and Biological Science.University of Texas High-Resolution X-ray CT Facility (UTCT); Jackson School of Geosciences, The University of Texas at Austin; Natural History Museum (London); Royal Microscopical Society (Oxford, UK)Geological Science

Slimming Brick Cache Strategies for Seismic Horizon Propagation Algorithms

International audienceIn this paper, we propose a new bricked cache system suitable for a particular surface propagation algorithm : seismic horizon reconstruction. The application domain of this algorithm is the interpretation of seismic volumes used, for instance, by petroleum companies for oil prospecting. To ensure the optimality of such surface extraction, the algorithm must access randomly into the data volume. This lack of data locality imposes that the volume resides entirely in the main memory to reach decent performances. In case of volumes larger than the memory, we show that using a classical brick cache strategy can also produce good performances until a certain size. As the size of these volumes increases very quickly, and can now reach more than 200GB, we demonstrate that the performances of the classical algorithm are dramatically reduced when processed on standard workstation with a limited size of memory (currently 8GB to 16GB). In order to handle such large volumes, we introduce a new slimming brick cache strategy where bricks size evolves according to processed data : at each step of the algorithm, processed data could be removed from the cache. This new brick format allows to have a larger number of brick loaded in memory. We further improve the releasing mechanism by filling in priority the “holes” that appear in the surface during the propagation process. With this new cache strategy, horizons can be extracted into volumes that are up to 75 times the size of the available cache memory. We discuss the performances and results of this new approach applied on both synthetic and real data

Monitoring the evolution and migration of a methane gas plume in an unconfined sandy aquifer using time-lapse GPR and ERT

The definitive publication is available at Elsevier via http://dx.doi.org/10.1016/j.jconhyd.2017.08.011 © 2017. This version, has not been modified, and is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Fugitive methane (CH4) leakage associated with conventional and unconventional petroleum development (e.g., shale gas) may pose significant risks to shallow groundwater. While the potential threat of stray (CH4) gas in aquifers has been acknowledged, few studies have examined the nature of its migration and fate in a shallow groundwater flow system. This study examines the geophysical responses observed from surface during a 72 day field-scale simulated CH4 leak in an unconfined sandy aquifer at Canadian Forces Base Borden, Canada, to better understand the transient behaviour of fugitive CH4 gas in the subsurface. Time-lapse ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) were used to monitor the distribution and migration of the gas-phase and assess any impacts to groundwater hydrochemistry. Geophysical measurements captured the transient formation of a CH4 gas plume emanating from the injector, which was accompanied by an increase in total dissolved gas pressure (PTDG). Subsequent reductions in PTDG were accompanied by reduced bulk resistivity around the injector along with an increase in the GPR reflectivity along horizontal bedding reflectors farther downgradient. Repeat temporal GPR reflection profiling identified three events with major peaks in reflectivity, interpreted to represent episodic lateral CH4 gas release events into the aquifer. Here, a gradual increase in PTDG near the injector caused a sudden lateral breakthrough of gas in the direction of groundwater flow, causing free-phase CH4 to migrate much farther than anticipated based on groundwater advection. CH4 accumulated along subtle permeability boundaries demarcated by grain-scale bedding within the aquifer characteristic of numerous Borden-aquifer multi-phase flow experiments. Diminishing reflectivity over a period of days to weeks suggests buoyancy-driven migration to the vadose zone and/or CH4 dissolution into groundwater. Lateral and vertical CH4 migration was primarily governed by subtle, yet measurable heterogeneity and anisotropy in the aquifer.NSERC Strategic Partnerships Grant Project (SPG-P)NSERC Banting Fellowshi