Comparative Study of Refraction Microtremor (ReMi) and Active Source Methods for Developing Low-Frequency Surface Wave Dispersion Curves
Bibliographic record
Abstract
Abstract Obtaining high-quality dispersion curves is a critical step in the development of reliable shear wave velocity profiles from surface wave measurements. Because of its limited equipment and space requirements, the refraction microtremor (ReMi) method has become a popular approach for determining surface wave dispersion curves, and is increasingly being used for estimating low-frequency (long wavelength) surface wave velocities that are beyond the range of most active sources. The recent development of a low-frequency field vibrator as part of the Network for Earthquake Engineering Simulation (NEES) program has made it possible to actively generate surface wave energy down to frequencies of less than 1 Hz. This paper presents a comparative study of the ReMi method and the active-source frequency-wavenumber (f-k) method (using the NEES vibrator) for developing low-frequency dispersion curves. Linear arrays of 1-Hz seismometers were deployed at eight deep soil sites in the Mississippi Embayment. Using both ambient and active energy, surface wave dispersion curves were determined to wavelengths of 600 m. The dispersion data from the two methods were in good agreement (within about ±5%) to wavelengths of 100 to 150 m (3 to 4 Hz) at most sites. However, at longer wavelengths the dispersion estimates from the ReMi approach deviated significantly from the active source measurements. The validity of the f-k dispersion curve was supported by dispersion data obtained using the SASW method, as well as ambient vibration measurements performed using a circular array at four of the sites. Analyses of ambient vibrations recorded using circular arrays show that the poor performance of the ReMi method at long wavelengths is not because of a lack of ambient surface wave energy at low frequencies, but can be attributed to invalid assumptions about the nature of the ambient wavefield.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".