Seismoelectric and Electroseismic Modeling in Stratified Porous Media With a Shallow or Ground Surface Source
Bibliographic record
Abstract
Abstract For a shallow or ground surface source and receiver at the same level or close depth, it is very difficult or computationally inefficient to simulate seismoelectric or electroseismic wave‐fields in stratified porous media by current reflectivity methods, such as the Luco‐Apsel‐Chen generalized reflection and transmission method (LAC GRTM). In this work, the peak‐trough averaging method which has been proved effective and efficient in dealing with this kind of computational problem is adopted to update the seismoelectric and electroseismic modeling algorithm based on LAC GRTM. After thoroughly verifying the accuracy and computational efficiency of the updated algorithm, we utilize it to numerically investigate both the electroseismic and seismoelectric couplings. Snapshots of electroseismic wave‐fields indicate evanescent electroseismic conversion, a reverse process of evanescent seismoelectric conversion, dominates at relatively larger ratios of seismic wavelength to interface depth, whereas the interfacial radiation electroseismic conversion is more prominent for the opposite situation. Our seismoelectric modeling results demonstrate that electric signals can arrive at the ground surface a few milliseconds earlier than their causative seismic signals due to evanescent seismoelectric conversion. This is the first modeling result considering source‐receiver geometries on the surface capable of explaining similar phenomena reported in geophysical field observations of seismically induced electrokinetic effects over a long history. The updated algorithm offers an accurate and efficient tool for forward modeling and will benefit interpretations of field observations as well as future inversion studies.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".