Seismic Data Reconstruction Using Multidimensional Prediction Filters
Bibliographic record
Abstract
Auto-Regressive (AR) modeling has a broad range of applications in signal processing. An auto-regressive operator utilizes the time history of a signal to extract important information hidden in the signal. It has been widely utilized in the area of spectrum estimation, as well as signal filtering. One of the main applications of AR modeling is signal prediction. Spitz (1991) and Porsani (1999) used AR modeling to interpolate the regularly sampled seismic records in the spatial direction. Also, Naghizadeh and Sacchi (2007a) introduced the Multi-Step Auto-Regressive (MSAR) algorithm in order to reconstruct nonuniformly sampled data in the spatial direction. The latter is a novel way of applying AR operation with jumping steps in the low frequency portion of the data in an attempt to extract AR operators for the high frequency portion. The extracted Prediction Filter (PF) for each frequency is then used as a regularization term to reconstruct the missing spatial samples. In this paper we investigate the performance of MSAR algorithm for more than one spatial direction. First, in the theory section, an optimality proof of multidimensional (MD) MSAR algorithm is discussed. Then a practical implementation of MSAR using simple flowcharts is discussed. Finally, synthetic and real data examples of application of MSAR are shown.
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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) | 0.000 | 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".