PS-VSP: Deep Learning for qP and qS Arrival Picking in Vertical Seismic Profiles
Bibliographic record
Abstract
This repository includes the raw dataset and manual picks for qP- and qS-waves. Our machine learning approach aims to support the inversion of elastic anisotropy in metamorphic bedrock. Data Generation: Geological Model: Synthetic data is produced using a two-phase model from the Deep Fault Drilling Project (PFPD-2b) on the Alpine Fault, New Zealand, featuring an upper sediment layer and a lower metamorphic bedrock layer. Modeling: Forward modeling was performed with Devito on a rotated staggered grid by varying the elastic stiffness of the metamorphic bedrock. Manual Picking: The corresponding manual picks were completed using SLB VISTA desktop seismic data processing software. Machine Learning Workflow: A Python script demonstrates the training process using PyTorch with a three-layer U-Net. Input: 3-component velocity images (vx, vy, vz) from a continuous borehole. Output: Probability maps for qP- and qS-waves.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.040 |
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".