On the estimation of stochastic parameters from deep seismic reflection data and its use in delineating lower crustal structure
Bibliographic record
Abstract
In this study, a new experimental method of extracting geological data from seismic reflection data was developed and tested on synthetic and real seismic data. The method uses statistics to describe the distribution of geological heterogeneity of the subsurface as well as the distribution of reflections in a resulting reflected seismic wavefield. These 2 types of statistics are then related and through this relation, the statistics of the geological heterogeneity in the subsurface can be estimated from the distribution of reflections in the resulting reflected seismic wavefield. This relation between the 2 types of statistics is found and verified in elaborate controlled synthetic experiments, involving amongst others visco-elastic Finite Difference forward modelings of wavefields in random seismic velocity fields . The estimates of geological heterogeneity are computed from seismic reflection data in the form of stochastic (von Karman) parameters correlation length and Hurst number. These parameters are properties of lateral autocorrelations, that are computed in windows of seismic data. By doing sliding-window estimations of these parameters in seismic sections, maps can be compiled of the variability of the parameters throughout the seismic section. This variability in stochastic parameters represents a variability in the distribution of underlying geological heterogeneity. A map of the latter can give valuable information about the scale lengths of heterogeneity in the subsurface and how they came to be as a product of regional rock-mechanical deformation history. A pleasant by-product of the statistical nature of the method is the assessment of uncertainties in the results. By looking at the uncertainties in the estimated parameters, their validity can be discussed. A synthetic test was done to ascertain the ability of the method to differentiate between two tectonic regions with clearly different distributions of geological heterogeneity, through estimations in the (highly complex) reflected wavefield. This test was succesful, the difference in heterogeneity was picked up by the method in the right proportion, also in the case of noisy seismic data and migrated seismic data. Given the good perspective from the synthetic test-case, application to 2 real deep seismic datasets was done. 1 dataset was the AG48 seismic line in the Abitibi-Grenville transect across Quebec, Canada, part of the LITHOPROBE project. The other dataset was the DOBRE 2000/2001 seismic line, a transect of the Donbas Basin in southeastern Ukraine. Both datasets contain reflections from as deep as upper mantle depth. With some enhancements to the method to deal with real-data effects, maps were made of the estimated stochastic parameters. The maps reveal patterns in the distribution of geological heterogeneity that point to clusters of equal scale lengths. These clusters coincide grosso modo with previous tectonic line-drawing interpretations, but also deliver additional patterns that were previously undetected by the line-drawing interpretations. A modified view on regional rock-mechanical deformation history is the most positive outcome of this real-data application.
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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 | 0.001 | 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 | 0.001 | 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 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".