Postseismic Deformation Following the 1994 Northridge Earthquake Identified Using the Localized Hartley Transform Filter
Bibliographic record
Abstract
Here we present a new mathematical tool, the localized ( HL ); Bracewell, 1990 ), that allows for the filtering of 1-D time series through the identification of the power at various spatial and temporal wavelengths. Its application to and the associated results are presented from its application to continuous Global Positioning System (GPS) data from southern California for the time period 1994 through 2006. The HL transform filter removes the high-frequency components of the data and effectively isolates the longer period signal. This long-period signal is modeled as time-dependent postseismic deformation using the viscoelastic-gravitational model of ( 2004 ) for six stations selected for their proximity to the Northridge earthquake. The x-, y-, and z-components of the postseismic deformation are compared to the filtered data. Results suggest that this long-period deformation is a result of postseismic relaxation and that the HL transform filter provides an important new technique for the filtering of geophysical data consisting of the superposition of the effects of numerous complex sources at a variety of spatial and temporal scales.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".