Detection of subtle differences in seismic amplitude using convergence rate of the logistic map
Bibliographic record
Abstract
ABSTRACT Subtle reservoir is a key target of oil and gas exploration in the future. The high similarity of seismic amplitude between the reservoir and surrounding rock presents a challenge to detecting the reservoir boundary and its distribution. We have developed a global measurement of the distance between each sample value of seismic data and the stable fixed point of the data, i.e., the seismic convergence rate defined by the logistic map, and use it to highlight subtle differences in seismic amplitude. The logistic map is a fixed-point iteration system with one control parameter. To establish the relationship between seismic data and the logistic map, we define the stable fixed point of seismic data based on the Banach contraction principle and Cauchy convergence theorem, and we derive an equation for adaptively searching for the optimal control parameter of the logistic map based on the data’s stable fixed point. According to such an equation, we design a workflow to automatically generate seismic convergence rate for an input data. The seismic convergence rate is essentially the stable fixed-point image of seismic data, which is characterized by a fine structure and interpreted as the data’s invariant set. We use numerical experiments to illustrate the characteristics of seismic convergence rate, and we use the Marmousi model experiment to demonstrate the effectiveness of the seismic convergence rate on detecting subtle edges of seismic data. Then, we use real data from two typical carbonate exploration areas in China, the Central Tarim Basin and the Ordos Basin, respectively, to show the abilities of the seismic convergence rate in detecting hidden seismic facies and in detecting subtle edges in high-coherence zones, as well as in extracting the seismic invariant set.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".