Notice bibliographique
Résumé
<strong class="journal-contentHeaderColor">Abstract.</strong> Increased availability and use of 3D-rendered geological models have provided society with predictive capabilities, supporting natural resource assessments, hazard awareness, and infrastructure development. The Geological Survey of Canada, along with other such institutions, has been trying to standardize and operationalize this modelling practice. Knowing what is in the subsurface, however, is not an easy exercise, especially when it is difficult or impossible to sample at greater depths. Existing approaches for creating 3D geological models involve developing surface components that represent spatial geological features, horizons, faults, and folds, and then assembling them into a framework model as context for downstream property modelling applications (e.g. geophysical inversions, thermo-mechanical simulations, and fracture density models). The current challenge is to develop geologically reasonable starting framework models from regions with sparser data when we have more complicated geology. This study explores the problem of geological data sparsity and presents a new approach that may be useful to open up the logjam in modelling the more challenging terrains using an agent-based approach. Semi-autonomous software entities called spatial agents can be programmed to perform spatial and property interrogation functions, estimations and construction operations for simple graphical objects, that may be usable in building 3D geological surfaces. These surfaces form the building blocks from which full geological and topological models are built and may be useful in sparse-data environments, where ancillary or a priori information is available. Critical in developing natural domain models is the use of gradient information. Increasing the density of spatial gradient information (fabric dips, fold plunges, and local or regional trends) from geologic feature orientations (planar and linear) is the key to more accurate geologic modelling and is core to the functions of spatial agents presented herein. This study, for the first time, examines the potential use of spatial agents to increase gradient constraints in the context of the Loop project (<span class="uri">https://loop3d.github.io/</span>, last access: 1 October 2021âââââââ) in which new complementary methods are being developed for modelling complex geology for regional applications. The spatial agent codes presented may act to densify and supplement gradient as well as on-contact control points used in <i>LoopStructural</i> (<span class="uri">https://www.github.com/Loop3d/LoopStructural</span>, last access: 1 October 2021) and <i>Map2Loop</i> (<a href="https://doi.org/10.5281/zenodo.4288476">https://doi.org/10.5281/zenodo.4288476</a>, de Rose et al., 2020). Spatial agents are used to represent common geological data constraints, such as interface locations and gradient geometry, and simple but topologically consistent triangulated meshes. Spatial agents can potentially be used to develop surfaces that conform to reasonable geological patterns of interest, provided that they are embedded with behaviours that are reflective of the knowledge of their geological environment. Initially, this would involve detecting simple geological constraints: locations, trajectories, and trends of geological interfaces. Local and global eigenvectors enable spatial continuity estimates, which can reflect geological trends, with rotational bias, using a quaternion implementation. Spatial interpolation of structural geology orientation data with spatial agents employs a range of simple nearest-neighbour to inverse-distance-weighted (IDW) and quaternion-based spherical linear rotation interpolation (SLERP) schemes. This simulation environment implemented in NetLogo 3D is potentially useful for complex-geologyâsparse-data environments where extension, projection, and propagation functions are needed to create more realistic geological forms.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,130 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».