Evaluation of Hydraulic Conductivity Collected by Various Approaches at a Highly Heterogeneous Field Site
Notice bibliographique
Résumé
Significant research efforts have been conducted over the last several decades to better understand the groundwater flow and subsurface contaminant transport. It has been found that building a groundwater model for remediation decision-making requires an accurate delineation of spatial variation in hydraulic conductivity (K) and specific storage (Ss). Currently, numerous methods are available for site characterization. Traditional methods such as grain size analyses, permeameter and slug tests can provide point-scale estimates of K, while large-scale estimates from pumping tests are widely used for water-supply and water-quality investigations. However, when the degree of local heterogeneity increases, the necessary number of K increases dramatically, which presents a challenge to conventional methods. As a consequence, Direct Push (DP) based methods have been developed as efficient alternatives to conventional well-based approaches to provide K variability for shallow, unconsolidated aquifers. Hydraulic Profiling Tool (HPT) is one of the novel DP approaches designed for high-resolution site characterization with a test interval of about 1.5 cm. Various site-dependent formulae can be utilized to convert data collected during the HPT surveys into K estimates over a limited range. More recently, inverse modeling approaches of varying degrees of parametrization have become one of the most promising techniques to map hydrostratigraphic spatial variations between boreholes and identify heterogeneity characteristics with a level of detail never before possible. Many comparisons of diverse approaches have been performed, but there is no consensus on which approach yields parameters that are representative for field sites. The main objective of this study is to evaluate K estimates obtained via various site characterizations methods including: (1) grain size analyses; (2) falling head permeameter tests; (3) slug tests; (4) HPT with three different formulae; (McCall and Christy, 2020; Borden et al., 2021; and Zhao and Illman, 2022b) (5) inverse modeling based on a geological zonation approach, and (6) a highly parametrized transient hydraulic tomography (THT) approach. The performance of each approach is first qualitatively analyzed by comparing it with site geology. A 19-layer geological model and forward groundwater model are employed to further assess various methods by simulating seven independent pumping tests that are not used for model calibration under both steady-state and transient-state conditions. Results reveal that the highly parametrized THT analysis with prior geological information yields the best results in model validation under both steady and transient states, and the generated K field revealed the most salient features of inter- and intra-layer heterogeneity. In contrast, traditional methods yield biased prediction of drawdowns, while HPT methods are primarily constraint by the limited range of estimates, especially for low permeable materials.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».