Time-lapse seismic imaging using shot gathers with nonrepeatable source wavelets
Bibliographic record
Abstract
ABSTRACT In time-lapse seismic applications, the signal produced by changes in the properties of subsurface rocks is generally obscured by noise associated with imperfect repeatability between surveys. A particularly important obstacle in the formation of time-lapse difference images is variation in the effective source wavelet between baseline and monitoring data sets. However, the partially separable influence of the wavelet within Green’s function model of seismic data permits two frequency-domain matching filters to be designed, which act to reduce source wavelet nonrepeatability. One is based on the spectral ratio of the baseline and monitoring wavelets and can be applied when prior estimates of the wavelets are available; the other is the average spectral ratio of the baseline and monitoring traces and can be applied when prior estimates are unavailable. After balancing the data sets with either of these filters, we further prepare for the imaging step with time-shift corrections, using a published fast local crosscorrelations algorithm, preparing the difference data for use in an imaging algorithm. Reverse time migration is engaged for the imaging task, but we observe that residual repeatability errors tend to be magnified at this stage when source-normalized crosscorrelation imaging conditions are used. Testing indicates that replacing this with a Poynting vector imaging condition strongly suppresses remaining errors in a robust manner. This includes stability within simulated data environments to noise-free data and data with random noise, up to signal-to-noise ratios of roughly one. Furthermore, our method illustrates better performance when compared with the conventional least-squares matching filter and common-depth-point-domain warping. At present, there is no common workflow for seismic imaging directly using time-lapse shot gathers. Our research contribution lies not only in the two matching filters but also in a novel workflow for time-lapse seismic imaging.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".