A new open-source Python toolbox for processing seismic surface wave data
Bibliographic record
Abstract
The use of open-source processing tools represents a strategic resource for the scientific community. The Open Science philosophy (https://www.unesco.org/en/open-science) promotes transparency, reproducibility and accessibility to data and source codes. This not only ensures continuous and collaborative development, but also increases the quality of proposed solutions.Characterizing the near surface based on geophysical methods is of considerable interest for many disciplines, and the reliability and quality of the provided results is tied to the available processing resources. The surface wave analysis (SWA) of active seismic data is widely used to determine the shear wave velocities of a site. Several efforts have been made to create open-source tools for SWA, starting with the precursor Geopsy (Wathelet, 2005), continuing with the more recent SWIP (Pasquet and Bodet, 2017), MASWaves (Olafsdottir et al., 2018), and SWprocess (Vantassel and Cox, 2022). The classical procedure they propose is limited to a local 1D analysis on (moving) spatial windows, where homogeneous conditions are assumed. Although this is a robust approach, it does not highlight small-scale lateral variations.In this talk, we introduce a new open-source tool under continuous development for processing surface wave data. The Python-based library incorporates, in addition to the classical 1D analysis on moving windows, more advanced techniques such as the Multi-Offset Phase Analysis (MOPA; Strobbia and Foti, 2006) and the Tomography-like approach (Barone et al., 2021), which perform high-resolution 2D SWA for a more accurate identification of lateral velocity variations. The ultimate intent of our Python library is to contribute to further developing standards for processing and inversion of surface wave data in a proper 2D sense. ReferencesBarone I., Boaga J., Carrera A., Flores Orozco A. and Cassiani G., 2021. Tackling Lateral Variability Using Surface Waves: A Tomography-Like Approach. Surveys in Geophysics 42, no. 2, 317–38. https://doi.org/10.1007/s10712-021-09631-xOlafsdottir E. A., Erlingsson S., and Bessason B, 2018. Tool for Analysis of Multichannel Analysis of Surface Waves (MASW) Field Data and Evaluation of Shear Wave Velocity Profiles of Soils. Canadian Geotechnical Journal 55, no. 2, 217–233. https://doi.org/10.1139/cgj-2016-0302Pasquet S., and Bodet L., 2017. SWIP: An Integrated Workflow for Surface-Wave Dispersion Inversion and Profiling. GEOPHYSICS 82, no. 6, WB47–61. https://doi.org/10.1190/geo2016-0625.1Strobbia C., and Foti S., 2006. Multi-Offset Phase Analysis of Surface Wave Data (MOPA). Journal of Applied Geophysics 59, no. 4, 300–313. https://doi.org/10.1016/j.jappgeo.2005.10.009Vantassel J. P., and Cox B.R., 2022. SWprocess: A Workflow for Developing Robust Estimates of Surface Wave Dispersion Uncertainty. Journal of Seismology 26, no. 4, 731–56. https://doi.org/10.1007/s10950-021-10035-yWathelet M., 2005. Array recordings of ambient vibrations: surface-wave inversion. Ph.D. Thesis, University of Liège (Belgium)
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.046 |
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".