Single parameter full waveform inversion in fluid-saturated porous media
Bibliographic record
Abstract
It is difficult to estimate the properties of reservoir rocks from seismograms. One way to overcome this problem is to use full waveform inversion (FWI), which is applied to image highresolution velocities and density models. However, despite the many successful acoustic and elastic FWI applications, this technique cannot recover the fluid properties of reservoir rocks. We present poroelastic FWI in the frequency domain to reconstruct all of the poroelastic parameters that describe fluid saturated reservoir rocks. We use a type of quasi-Newton algorithm, l-BFGS, to implement poroelastic FWI using frequency continuation. We test our algorithm on simple synthetic models as a first step towards realistic poroelastic FWI. In this abstract we also investigate the sensitivity kernels for every parameter of poroelastic media. We analyze and compare all of the inversion results for single parameters and discuss the relationship between the inversion results and the corresponding sensitivity kernels. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Time: 3:55 PM Location: 225B Presentation Type: Oral
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".