Forearc versus Backarc Attenuation of Earthquake Ground Motion
Bibliographic record
Abstract
Understanding of the attenuation of seismic wave amplitudes with distance is a crucial issue for ground motion prediction equations and seismic hazard analysis. In this study, we empirically evaluate the influence of regional geologic structure, in particular the attenuation effects of traveling through a volcanic arc region (forearc versus backarc attenuation) in northern Japan. We perform regression analysis of Fourier amplitude spectra (FAS) of five well-recorded Japanese events that occurred very close to the line formed (in map view) by the volcanic arc. This provides an approximately symmetrical distribution of stations for each event relative to the volcanic front; forearc stations lie to the east of the front, while backarc stations lie to the west. To compare the characteristics of shallow and deep events, we have included both crustal and in-slab events in our dataset. Based on inspection of the data, we assume a hinged bilinear geometric spreading model with a fixed slope of -1 out to a distance of 50 km, with slope of -0.5 thereafter (for all events). Our results show, with a very high level of confidence, that high-frequency attenuation is greater in the backarc direction. The Q values for forearc regions ( Q ∼240 at 1 Hz, increasing to Q ∼3890 at 10 Hz) are about a factor of 2 larger than those for backarc regions ( Q ∼196 at 1 Hz, increasing to Q ∼1669 at 10 Hz) at high frequencies, implying much stronger attenuation as waves travel through the volcanic crustal structure to the backarc stations. The separation of forearc and backarc travel paths results in a significant reduction in the standard deviation ( σ ) of ground motion predictions (by as much as 0.05 log10 units), which has important implications for hazard analyses in subduction zone regions.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".