Water Table and Permeability Estimation From Multi‐Channel Seismoelectric Spectral Ratios
Bibliographic record
Abstract
Abstract Recent developments in predicting and interpreting seismoelectric (SE) signals suggest a great potential for studying near‐surface hydrogeological properties, particularly in the vadose zone. Previous studies have revealed that the SE spectral ratios obtained from earthquake‐triggered SE data contain valuable hydrogeological information concerning porous media (e.g., permeability, porosity, fluid viscosity, and salinity). This study introduces Multi‐Channel SeismoElectric Spectral Ratios (MC‐SESRs) by considering an active seismic source acting on the ground surface. The frequency‐ and saturation‐dependent excess charge density is adopted to calculate the cross‐coupling coefficients. Applying a supervised learning task based on a flat neural network, the so‐called “broad learning (BL)” model, to map and extract the features of MC‐SESRs data, we seek to determine the permeability and the water table depth. Our results indicate that (a) MC‐SESRs are sensitive to the water table depth and permeability; (b) using more traces of SESRs data for inversion can increase accuracy; and (c) the changing water table can be rapidly determined by the MC‐SESRs by resorting to the BL inverse model, and it can attain an excellent accuracy while disturbed by data noise and misspecified model parameters (e.g., porosity and permeability) with errors of up to 20%. The proposed MC‐SESRs inversion has potential applications for non‐invasive monitoring in shallow porous media (e.g., frost thawing and geothermal upwelling).
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".