Strategies for elastic full waveform inversion
Bibliographic record
Abstract
The advent of modern supercomputers, in conjunction with larger, more comprehensive datasets, has led to a paradigm shift in seismic imaging. Full waveform inversion is routinely employed as a tool to estimate subsurface properties of the Earth with high resolution. The method fits simulated waveforms to observed data by iteratively updating estimates of subsurface properties. While recent advances have fostered seismic imaging success in areas with complex subsurface geology, a variety of challenges persist. Underdeveloped topics include the estimation of multiple physical parameters, uncertainty quantification, robust convergence, and the incorporation of more complex physics. This thesis focuses on multi-parameter inversion in isotropic, elastic full waveform inversion. The transition from acoustic to elastic waveform inversion increases the computational cost, data complexity, and the ill-posed nature of the inverse problem. Estimating multiple independent subsurface parameters is challenging due to the limited, or overlapping, sensitivity of data to different parameters. In this thesis, I explore approaches to accelerate elastic full waveform inversion through simultaneous sources (Chapter 3) and second-order stochastic optimization (Chapter 4). Performance is assessed through controlled numerical experiments. Using the acoustic formulation, I present two forms of resolution/uncertainty analysis predicated on an approximation of the Hessian as a superposition of Kronecker products (Chapter 5). The final chapter compares applications of 2D acoustic and elastic full waveform inversion to a land dataset from the western Canadian basin (Chapter 6). I devise a workflow that includes data-preprocessing, initial model building and inversion.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".