Beyond Receiver Functions: Green's Function Estimation by Transdimensional Inversion and Its Application to OBS Data
Bibliographic record
Abstract
Abstract Receiver functions, calculated by deconvolving P (or vertical) component records of teleseismic waveforms from the corresponding SV (or radial) components, have been widely used to obtain receiver‐side Green's functions in an approximate form. Conventional receiver function methods, however, often fail due to numerical instability of the deconvolution and strong multiples on the P components. These problems become severe when analyzing in high frequency and using data from ocean bottom seismometers (OBSs). We present a novel technique to estimate Green's functions of receiver‐side structure from teleseismic P waveforms. In this method, two components of Green's functions, which are assumed to form a series of pulses, are directly related in a single equation without explicit deconvolution. Based on the equation, we construct posterior probability distributions regarding the number of pulses, their timing, and amplitudes within a transdimensional Bayesian framework. A reversible‐jump Markov chain Monte Carlo method is used for this purpose, and we further utilize a parallel tempering method to achieve rapid convergence. Synthetic tests and application to an OBS installed at the Yamato Basin, the Sea of Japan, show that the proposed method can estimate radial‐component Green's functions more accurately than conventional receiver function methods. We suggest that the high‐frequency Green's functions estimated by the new method can be used to reveal fine‐scale (in order ~100 m) structure of the seafloor sediment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".