Improved analysis of horizontal-to-vertical spectral ratio measurements for groundwater investigations
Bibliographic record
Abstract
Ambient seismic noise, and specifically the Horizontal-to-Vertical Spectral Ratio (HVSR), is routinely used for seismic microzonation, assessment of earthquake site characteristics and bedrock depth information for hydrogeological studies. These measurements not only provide peak frequency or period of a seismic resonator, but the shape of the spectral ratio can also give insight into the architecture of subsurface structure. For example a dipping resonator decreases the peak amplitude and increases the peak frequency of the spectrum. Effects of two-dimensional (2-D) subsurface structure (non-horizontal layering) have been successfully modelled and can be observed on the orthogonal Horizontal-to-Vertical Spectral Ratios, where N-S/V and E-W/V have different peak frequencies and peak amplitudes. By analysis of both horizontal spectral ratios the practitioner is able to determine whether one-dimensional (1-D) subsurface layering is present and hence 1-D assumptions are appropriate, e.g. inverting shear-wave velocities (Vs), calculating average Vs and depth to the resonator. HVSR measurements collected along seismic reflection profiles with known resonator topography, e.g. over steeply dipping bedrock resonators, are used to investigate both orthogonal horizontal components separately. Differences between the orthogonal H/V components are able to identify 2-D subsurface structure. Results demonstrate that extending analysis beyond the peak frequency to include orthogonal Horizontal-to- Vertical Spectral Ratios adds important information related to dipping surfaces and their orientations. Due to the ease and rapidity of HVSR data collection, the technique is ideally suited for reconnaissance scale survey work but also for infill where other data is sparse.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".