Capturing seismic velocity changes in receiver functions with optimal transport
Bibliographic record
Abstract
SUMMARY Temporal changes in seismic velocities are an important tool for tracking structural changes within the crust during transient deformation. Although many geophysical processes span the crust, including volcanic unrest and large-magnitude earthquakes, existing methods for seismic monitoring are limited to the shallow subsurface. We present an approach for deep seismic monitoring based on teleseismic receiver functions, which illuminate the crustal velocity structure from the bottom-up. Using synthetic waveform modelling, we show that receiver functions are uniformly sensitive to velocity changes throughout the crust and can locate the depth of the perturbation. We introduce a novel method based on optimal transport for measuring the non-linear time–amplitude signal variations characteristic of receiver function monitoring. We show that optimal transport enables comparison of full waveform distributions rather than relying on representative stacked waveforms. We further study a linearized version of optimal transport that renders time-warping signal variations into simple Euclidean perturbations, and use this capability to perform blind source separation in the space of waveform variations. This disentangles the effects of changes in the source–receiver path from changes in subsurface velocities. Collectively, these methods extend the reach of seismic monitoring to deep geophysical processes, and provide a tool that can be used to study heterogeneous velocity changes with different spatial extents and temporal dynamics.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".