A preliminary assessment of uncertainties attributed by analysts, array types and processing algorithms for microtremor observations, via the COSMOS Blind Trials
Bibliographic record
Abstract
SummaryThe blind trial studies conducted for the 2006 3rd International Symposium on the Effects of Surface Geology (Grenoble, France) and the 2015 Inter-comparison of Methods for Site Parameter and Velocity Profile Characterization (InterPACIFIC) Workshop (Turin, Italy) evaluated the utility of microtremor array methods for characterizing seismic site conditions. These studies used a multiplicity of arrays but left an open question as to whether (and under what) conditions might sparse (low-cost) arrays be technically sufficient for the task. Similar questions arise when designing arrays for use in mapping cover thickness or buried high-velocity layers in mineral exploration.In this study, the Consortium of Organizations for Strong Motion Observation Systems (COSMOS) blind trials used microtremor array data from four sites with geology ranging from deep alluvial valleys to an alpine valley. Data were incrementally released to approximately a dozen analysts in four phases: (1) 2-station linear arrays; (2) sparse triangular arrays; (3) complex nested triangular or circular arrays; (4) all available geological control including drillhole data. While data from one site consisted of recordings from 3-component sensors, the other three sites consisted of data from vertical-component sensors only. The sites covered a range of noise source distributions, ranging from one site with a highly directional microtremor wave field, to others with distributed or omni-directional wave fields.Here, we review the results based on the different processing algorithms (e.g. beam-forming, spatial autocorrelation, seismic interferometry) as applied by the analysts to the incrementally released data, and then compare the effectiveness between the differing wave-field distributions. The results of the study will aid in building an evidence-based consensus on preferred cost-effective arrays and processing methodology for future studies of earthquake hazard site-effects and cover thickness studies in mineral exploration.
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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 | 0.261 | 0.500 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".