PyRaysum: Software for Modeling Ray-theoretical Plane Body-wave Propagation in Dipping Anisotropic Media
Bibliographic record
Abstract
This article introduces PyRaysum, a Python software for modeling ray-theoretical body-wave propagation in dipping and/or anisotropic layered media based on the popular Fortran code Raysum. We improve and expand upon Raysum in several ways: 1) we significantly reduce the overhead by avoiding I/O operations; 2) we implement automatic phase labeling to facilitate the interpretation of complex seismograms; 3) we provide the means to correct inaccuracies in the calculated amplitude of free surface reverberations. We take advantage of the modern, object-oriented Python environment to offer various classes and methods to perform receiver function calculation, filtering and plotting. PyRaysum also integrates well with NumPy and ObsPy, two standard libraries for numerical computing and seismology. PyRaysum is built in Python version 3 and requires a Fortran compiler, but otherwise runs on all platforms. The software offers a high-level, ease-of-use user interface and is equipped with complete documentation and testing as well as tutorials to reproduce published examples from the literature. Time-optimized post-processing functions allow for the straightforward and efficient incorporation of PyRaysum synthetic data into optimization or probabilistic parametric search approaches.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".