Frequency‐Dependent Coda Amplitude Decays in the Region of Himalaya, India
Bibliographic record
Abstract
Abstract An earthquake dataset consisting of 327 records from 67 earthquakes is used to study the frequency‐dependent amplitude decay of seismic coda waves in the Kumaon Himalayas, India. Coda amplitude decays with time t are measured by empirical t − α dependences, with exponent α being variable regionally with frequencies and lapse times. The results show that α varies from 1 to 3, with an average of about 1.93. The lapse‐time dependence of α is noticeable but weak, and no significant azimuthal dependence of α is obtained. Significant regional variations of α are found, ranging from α ≈2.1 for the Lesser Himalaya to α ≈1.76 for the Greater Himalaya. These variations correlate with the geology of the region and crustal structure. The values of α are mostly frequency independent, indicating low intrinsic attenuation within the crust. When interpreted according to the conventional coda‐ Q ( Q c ) model, these values of α lead to Q c ≈78 f 1.06 in the Kumaon Himalaya, Q c ≈90 f 1.09 in the Kumaon Lesser Himalaya, and Q c ≈90 f 0.92 in the Kumaon Greater Himalaya. A frequency‐dependent α is found within a localized area, giving an estimate of near‐surface Q ≈200. Three general conclusions of this study may be significant for coda studies in other areas. First, the values of α are variable regionally, whereas its average level is remarkably constant and correlates with rock types and upper‐crustal structure. Second, the traditionally assumed value of α =1 is much lower than the actual spreading rates, which shows that the coda cannot be viewed as body waves scattered within a uniform crust. Using the value of α =1 causes a systematic underestimation of coda amplitude decays in the data. Third, the dependence of α on frequency is relatively weak and occurs in localized areas. Combined with an underestimated α , this weak frequency dependence may cause biases in the estimation of secondary parameters, such as coda Q .
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".