Multiscale Full-Waveform Inversion with Land Seismic Field Data: A Case Study from the Jizhong Depression, Middle Eastern China
Bibliographic record
Abstract
The Jizhong depression contains several geothermal reservoirs that are characterized by localized low-velocity anomalies. In this article, full-waveform inversion (FWI) is used to characterize these anomalies and determine their extent. This is a challenging problem because the reservoirs are quite small and the available data have usable frequencies only down to 5 Hz. An accurate-enough starting model is carefully built by using an iterative travel time tomography method combined with a cycle-skipping assessment method to begin the inversion at 5 Hz. A multiscale Laplace–Fourier-domain FWI with a layer-stripping approach is implemented on the starting model by gradually increasing the maximum offset. The result of overlapping the recovered velocity model on the migrated seismic profile shows a good correlation between the two results. The recovered model is assessed by ray tracing, synthetic seismogram modeling, checkerboard testing and comparisons with nearby borehole data. These tests indicate that low-velocity anomalies down to a size of 0.3 km × 0.3 km at a maximum depth of 2 km can be recovered. Combined with the well log data, the resulting velocity model allows us to delineate two potential geothermal resources, one of which was previously unknown.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".