MODELING AND MEASUREMENT OF ATTENUATION IN SYNTHETIC SEISMIC DATASETS
Bibliographic record
Abstract
Anelasticity and heterogeneity in the Earth decreases the energy and modifies the dominant frequency of seismic wavetrains as they travel through the Earth. These phenomena are known as seismic attenuation. The associated physical processes lead to reduced amplitudes, waveform distortions, and phase delays of seismic wave arrivals. Seismic wave attenuation is often viewed as an important indicator of the presence of fluids, variations of saturation, porosity, and fracturing within the subsurface, as well as of variations of temperature, pressure, and the mineral content of rocks. However, along with its usefulness for interpreting subtle physical properties of the Earth, seismic wave attenuation is often difficult to measure accurately, and the resulting measures may be difficult to relate to physical properties. In this Thesis, I investigate three types of attenuation-measurement methods in detail, by using three high-quality synthetic datasets. The first dataset simulates a two-dimensional (2-D) reflection seismic profile and is generated by the popular Seismic Un*x software. The second dataset is performed by the classic and accurate one-dimensional (1-D) modeling method called “reflectivity” and simulates the subsurface structure of the Weyburn oil field in southern Saskatchewan. The third synthetic dataset is also 1-D but is unique in modeling a nuclear explosion as the source and covering depths down to about 600 km. These datasets are used to test and compare three methods of attenuation measurement: 1) the well-known spectral ratio (SR) method, 2) the less known instantaneous-frequency matching (IFM) method, and 3) a new method based on time-variant deconvolution (TVD). The TVD method uses the full-waveform modeling for measuring not only the traditional quality factor (usually denoted Q) but also all other effects of attenuation in seismic records, including the effects of reflections, multiples, thin-layer tuning, surface and other types of waves). This method is also the only one allowing measurement of the Q at every point within a seismic section. Due to these properties, the TVD method can be used for advanced interpretation and for compensating the attenuation effects in seismic records. With each of the above methods, detailed Q measurements were performed at variable source-receiver distances for several arrivals within the seismic records and compared to the models. The Q values obtained by the SR, IFM, and TVD methods were found suitable for clear isolated arrivals such as shallow reflections. However, the resulting Q-factors begin deviating from the expected model levels when these arrivals are complicated by interferences with other reflections, multiples, mode conversions, and noise. Because of its spectral averaging properties the SR method is somewhat more stable with respect to such effects. For all three methods, significant variations in performance were found for different source-receiver distances. Overall, the Q-factors measured within the seismic sections are variable and not simply related to the Q of the subsurface. A somewhat unexpected yet important result of this study consists in finding that the attenuation modeled by the 2-D Seismic Un*x program is of a very peculiar kind described by the Q-factor proportional to frequency. The above results show that seismic attenuation still requires substantial research in both modeling and measurements, in both exploration-scale and earthquake seismology.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".