Predicting Vp/Vs in North America with a Machine Learning Approach
Bibliographic record
Abstract
Vp/Vs (or the Poisson's ratio) provides critical information to constrain the bulk crustal composition, stress state, and tectonic evolution. The crustal Vp/Vs beneath the seismic station can be effectively determined by the receiver function H-κ stacking technique. However, complex crustal structures often cause large uncertainty in Vp/Vs measurements. Additionally, Vp/Vs observations are sparse in many regions of the world due to the uneven distribution of seismic stations. While interpolation methods have been widely applied to obtain a continuous distribution of Vp/Vs, these results are not accurate enough and may be strongly biased by the interpolation artifacts. Therefore, reliable mapping of the Vp/Vs variation in the crust remains a major challenge in seismological study.We present a machine learning approach to estimate the Vp/Vs using multiple geophysical datasets. This approach assumes that the Vp/Vs is related to the physical and chemical properties of the crust. Specifically, we implement a Gradient Boosted Regression Tree algorithm (XGBoost) to develop an optimal prediction model for North America. To train the model, we use Vp/Vs as the target values and a compilation of geophysical observations as the predictor variables. These measurements are composed of two types of data: (1) continuous data, such as crustal velocities and gravity anomalies; (2) categorical data (tectonic type). We use 80% of the measurements to train the model, and the remaining 20% to validate the model. We first examine the reliability of this method by predicting Vp/Vs for the United States, where extensive geophysical data are available. Overall, the model achieves a high R2 value of 0.88 in all measured results versus prediction results, indicating robust prediction results at most locations. In the second, a more challenging test, we predict Vp/Vs for Canada where measurements are sparse and uneven, and the amount of data is only 14% of that in the United States. The results show an R2 value of 0.87 between the measured and predicted values. Feature importance analysis indicates that crustal shear wave velocity and tectonic type contribute most significantly to reducing the loss function. The prediction results show that bulk Vp/Vs varies between 1.70 and 1.90 across Canada, with a mean value of 1.82. The cratonic regions generally exhibit high (>1.80) Vp/Vs with a relatively small (
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".