Inference of lithologic distributions in an alluvial aquifer using airborne transient electromagnetic surveys
Bibliographic record
Abstract
Abstract An airborne transient electromagnetic (TEM) survey was completed in the Upper San Pedro Basin in southeastern Arizona to map resistivity distributions within the alluvial aquifer. This investigation evaluated the utility of 1D vertical resistivity models of the TEM data to infer lithologic distributions in an alluvial aquifer. Comparisons of the resistivity values and layers in the 1D resistivity models of airborne TEM data to 1D resistivity models of ground TEM data, borehole resistivity logs, and lithologic descriptions in drill logs indicated that the airborne TEM identified thick conductive fine-grained sediments that result in semiconfined groundwater conditions. One-dimensional models of ground-based TEM surveys and subsurface lithology at three sites were used to determine starting models and constraints to invert airborne TEM data using a constrained Marquardt-styleunderparameterized method. A maximum structural resolution of six layers underlain by a half-space was determined from the resistivity structure of the 1D models of the ground TEM data. The 1D resistivity models of the airborne TEM data compared well with the control data to depths of approximately 100m in areas of thick conductive silt and clay and to depths of 200m in areas of resistive sand and gravel. Comparison of a 3D interpolation of the 1D resistivity models to drill logs indicated resistive (mean of 65ohm-m) coarse-grained sediments along basin margins and conductive (mean of 8ohm-m) fine-grained sediments at the basin center. Extents of hydrologically significant thick silt and clay were well mapped by the 1D resistivity models of airborne TEM data. Areas of uncertain lithology remain below conductive fine-grained sediments where the 1D resistivity structure is not resolved: in areas where multiple lithologies have similar resistivity values and in areas of high salinity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".