Hydrogeologic structure underlying a recharge pond delineated with shear‐wave seismic reflection and cone penetrometer data
Bibliographic record
Abstract
ABSTRACT With the goal of improving the understanding of the subsurface structure beneath the Harkins Slough recharge pond in Pajaro Valley, California, USA, we have undertaken a multimodal approach to develop a robust velocity model to yield an accurate seismic reflection section. Our shear‐wave reflection section helps us identify and map an important and previously unknown flow barrier at depth; it also helps us map other relevant structure within the surficial aquifer. Development of an accurate velocity model is essential for depth conversion and interpretation of the reflection section. We incorporate information provided by shear‐wave seismic methods along with cone penetrometer testing and seismic cone penetrometer testing measurements. One velocity model is based on reflected and refracted arrivals and provides reliable velocity estimates for the full depth range of interest when anchored on interface depths determined from cone data and borehole drillers’ logs. A second velocity model is based on seismic cone penetrometer testing data that provide higher‐resolution 1D velocity columns with error estimates within the depth range of the cone penetrometer testing. Comparison of the reflection/refraction model with the seismic cone penetrometer testing model also suggests that the mass of the cone truck can influence velocity with the equivalent effect of approximately one metre of extra overburden stress. Together, these velocity models and the depth‐converted reflection section result in a better constrained hydrologic model of the subsurface and illustrate the pivotal role that cone data can provide in the reflection processing workflow.
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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 | 0.001 | 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) | 0.002 | 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".