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Record W3110755409 · doi:10.48336/63f0-xc10

Three-dimensional finite-volume time-domain modeling of graphitic fault zones in the Athabasca Basin using unstructured grids.

2021· dissertation· en· W3110755409 on OpenAlexaff
Xushan Lu

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2021
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFinite volume methodPolygon meshDomain decomposition methodsComputer scienceVoronoi diagramUnstructured gridScalar potentialScalar (mathematics)Applied mathematicsFinite element methodAlgorithmMathematicsGeometryEngineeringGridPhysicsMechanics

Abstract

fetched live from OpenAlex

In this thesis, numerical modeling methods for geophysical time-domain electromag- netic (EM) problems and their applications in modeling graphitic faults in the Atha- basca Basin are investigated. A finite-volume time-domain numerical modeling method is developed. The method uses unstructured Delaunay-Voronoï dual meshes. Such unstructured meshes are more flexible and efficient when models containing geological units with complex geometries and topography need to be considered. A model build- ing procedure is established to construct arbitrarily complex models with topography. The procedure locally refines the mesh quality at certain areas such as loop sources and receivers in order to obtain better numerical results. For modeling time-domain EM problems, two approaches are used: the electric field approach and the potential approach. The electric field method directly solves the electric field Helmholtz equation while the potential method solves the Helmholtz equation expressed using vector and scalar potentials. The electric field method is simpler in theory and results in a smaller linear system of equations compared to potential methods. The potential method, on the other hand, is more complex intheory and a larger linear system of equations needs to be solved. However, using the potentials method enables the decomposition of the electric field into galvanic and inductive parts, which is helpful for understanding the physics behind the behaviour of the EM fields in the ground. In addition, the linear system of equations is better conditioned which potentially allows the use of iterative methods to solve it. Both methods are validated by comparing the modeling results with analytic solu- tions for homogeneous half-space models and numerical results for models presented in the literature. The modeling methods developed in this thesis are then applied to the modeling of real EM data collected in the Athabasca Basin. Thin, steeply dip- ping graphitic fault systems, which are linked to the formation of uranium deposits are present in the basin and have a large conductivity contrast with the background host. Because of the close relationship between the graphitic faults and the uranium deposits, time-domain EM surveys are important tools for uranium exploration in the basin. Geological models of the graphitic fault systems are discretized with unstruc- tured grids using the model building procedure developed in this thesis. Two real data sets that were previously collected from the Athabasca Basin are modeled and the modeling results are compared with the real data. The match between the calculated three-component responses and real data is good for models built based on geological information, drilling information, and trial-and-error. These models can help us to infer the complex geometry and conductivity features of the subsurface conductor be- yond the areas targeted by drilling. Therefore, 3D modeling of realistic, complicated real-life conductive targets such as in the uranium exploration in the Athabasca Basin or any other classic mineral exploration for a conductive target with complex shape is an important tool.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.247
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
Has abstractyes

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