Identification of Volcanic Breccia Formation Distribution in Relation to Groundwater Aquifer Potential Using 3D Resistivity Data Modelling
Bibliographic record
Abstract
Electrical resistivity tomography (ERT) method has been widely used in environmental surveys including hydrogeology study to provide images of the subsurface resistivity distribution. In this study, ERT survey using Wenner - Schlumberger electrode configuration was conducted to investigate the distribution of groundwater aquifer potential in the area dominated by various weathered volcanic rocks that unconformably overlaid limestone formation. The resistivities were measured using ARES resistivity meter each with total of 48 electrodes. The resistivity data were then processed using the robust inversion method that is more optimal to characterize sharp lithological boundary transitions observed in the study area. The resistivity value of the inverted model is interpreted into three different lithologies, namely soil (1.82-5 Ωm), volcanic breccias (5-20 Ωm) and limestone (>20 Ωm). This lithological interpretation was confirmed by borehole cutting report from the nearby well, regional geological map, and direct geological observation. Further, the inverted ERT section along with geological observation indicated volcanic breccias as an aquifer potential in the study area. The 2D resistivity cross-section is then gridded to obtain a 3D model of the potential aquifer geometry. From the model, the volume of volcanic breccia which is suspected as an potential aquifer layer is estimated at 122,392,828 m3.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".