Seismic Velocities Contain Information about Depth, Lithology, Fluid Content, and Microstructure
Bibliographic record
Abstract
Recent advances in field and laboratory methods for measuring elastic wave velocities provide incentive and opportunity for improving interpretation of geophysical data for engineering and environmental applications. Advancing the state-of-the-art of seismic imaging requires developing petrophysical relationships between measured velocities and the hydrogeology parameters and lithology. Our approach uses laboratory data and rock physics methods. Compressional (Vp) and shear (Vs) wave velocities, Vp/Vs ratios, and relative wave amplitudes show systematic changes related to composition, saturation, applied stress (analogous to depth), and distribution of clay for laboratory ultrasonic measurements on soils. The artificial soils were mixtures of Ottawa sand and a second phase, either Wyoming bentonite or peat moss used to represent clay or organic components found in natural soils. Compressional and shear wave velocities were measured for dry, saturated, and partially-saturated conditions, for applied stresses between about 7 and 100 kPa, representing approximately the top 5 m of the subsurface. Analysis of the results using rock physics methods shows the link between microstructure and wave propagation, and implications for future advances in seismic data interpretation. For example, we found that Vp in dry sand-clay mixtures initially increases as clay cements the sand grains and fills porosity, but then Vp decreases when the clay content is high enough that the clay matrix controls the elastic response of the material. Vs decreases monotonically with increasing clay content. This provides a method for using Vp/Vs ratios to estimate clay content in a dry soil.
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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 | 0.000 |
| 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 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".