Approbation and Implementation of New Technologies for Processing Seismic Data of Complex Folded Zones (CRS, Beam, RTM)
Bibliographic record
Abstract
Abstract In complex folded areas with harsh tectonic conditions, there are problems in the seismic imaging of the subthrust and areas with steep slope angles. Recently, new seismic processing migration algorithms have appeared, which are quite expensive in terms of computational resources, but on the other hand, they make it possible to display complex structures more correctly, for example, with salt-dome tectonics. The purpose of this work is to test new algorithms for improving the signal-to-noise ratio in areas with complex wave fields and migrations. Nine 2D seismic lines were processed in the Omega 2018 software package using CRS, Beam, and RTM technologies. The pre-processing stage included quality analysis of seismic signal and estimation for sources/receivers such parameters as: schematic maps of root-mean-square amplitudes, dominant frequencies, and signal-to-noise ratios. The r robust Surface-consistent deconvolution was applied to improve the signal processing. After selecting the optimal parameters, CRS summation and CRS seismogram operators were obtained. The RTM migration used TEEC ware's RTM method, which leads to migration using relief and generates CRP gathers in either the surface offset region or the reflection angle region. Angular seismograms were calculated to improve the signal-to-noise ratio. Another measure to ensure a high signal-to-noise ratio was the use of CRS gathers as input data, which greatly improved depth imaging. Simulation software for migration processing was used for deep migration of CRS seismograms. The cluster-based imaging system generates seismograms with normal reflection angles without azimuth dependence. Although the velocity models for Beam and RTM are equal, the speed models for PSTM and RTM are very different. When forming a deep velocity model for PSTM, it is necessary to perform smoothing on a large base, otherwise migration artifacts will arise in places of sharp changes in velocities. For RTM, on the contrary, a correct speed model is required (without anti-aliasing), which will generate an image of higher quality. Beam migration calculations are higher than Kirchhoff migration due to more correct consideration of the dynamics and path of rays. However, these calculations are not comparable to the per-account costs for RTM. RTM migration costs are also very sensitive to the maximum frequency for calculations. RTM migration produces less noise than Beam migration and performs better in areas where reflections are lost. However, one must keep in mind that the speeds for Beam and RTM migrations, although the same, were obtained using RTM migration and if only Beam migration was used, the result could be worse. In different parts of the section, the advantages of one or another migration are visible. In general, it is noticeable that RTM migration works better in the upper part of the sections. The frequency content of the RTM migration recording is often higher and there is less noise. The methods outlined in this paper will reduce problems in imaging the environment in subthrust parts of structures and areas with steep slope angles, which is actual problem for Caspian, Russian and some parts of Middle East regions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".