Time‐lapse borehole radar for monitoring rainfall infiltration through podosol horizons in a sandy vadose zone
Bibliographic record
Abstract
Abstract The shallow aquifer on the Gnangara Mound, north of Perth, Western Australia, is recharged by winter rainfall. Water infiltrates through a sandy Podosol where cemented accumulation (B‐) horizons are common. They are water retentive and may impede recharge. To observe wetting fronts and the influence of soil horizons on unsaturated flow, we deployed time‐lapse borehole radar techniques sensitive to soil moisture variations during an annual recharge cycle. Zero‐offset crosswell profiling (ZOP) and vertical radar profiling (VRP) measurements were performed at six sites on a monthly basis before, during, and after annual rainfall in 2011. Water content profiles are derived from ZOP logs acquired in closely spaced wells. Sites with small separation between wells present potential repeatability and accuracy difficulties. Such problems could be lessened by (i) ZOP saturated zone velocity matching of time‐lapse curves, and (ii) matching of ZOP and VRP results. The moisture contents for the baseline condition and subsequent observations are computed using the Topp relationship. Time‐lapse moisture curves reveal characteristic vadose zone infiltration regimes. Examples are (I) full recharge potential after 200 mm rainfall, (II) delayed wetting and impeded recharge, and (III) no recharge below 7 m depth. Seasonal infiltration trends derived from long‐term time‐lapse neutron logging at several sites are shown to be comparable with infiltration trends recovered from time‐lapse crosswell radar measurements. However, radar measurements sample a larger volume of earth while being safer to deploy than the neutron method which employs a radioactive source. For the regime (III) site, where time‐lapse radar indicates no net recharge or zero flux to the water table, a simple water balance provides an evapotranspiration value of 620 mm for the study period. This value compares favorably to previous studies at similar test sites in the region. Our six field examples demonstrate application of time‐lapse borehole radar for characterizing rainfall infiltration.
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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 | 0.000 | 0.000 |
| 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 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".