Layer stripping of shear-wave splitting in marine<i>PS</i>waves
Bibliographic record
Abstract
The properties of split S waves can be used to infer: (1) the state of stress and strain in the Earth; (2) the directional dependence of hydraulic conductivity and (3) small changes in pore-fluid pressure in the rock mass that occur in response to dynamic processes, such as the earthquake cycle.Measurements of split S waves are particularly useful in shallow (<1000 m subseabed) marine sediments, where S-wave splitting from an azimuthal elastic anisotropy is typically produced by the presence of near-vertical aligned cracks.Here we present a method of measuring small amounts of S-wave splitting in marine P-to-S mode-converted waves, and illustrate the technique with data from an ocean-bottom seismometer (OBS) deployed on the west Svalbard continental slope.The analysis applies a modified version of the Alford rotation and layer-stripping technique developed for zero-offset S-wave sources and treats PS waves that undergo mode conversion at reflectors that are close to the seabed in comparison with the overlying water depth.When the seismic record contains coherent signal on both the in-plane and out-of-plane components, the layer-stripping technique is capable of decoupling the Swave splitting from the effects of P-wave velocity anisotropy and reflector dip that influence the downgoing, P wave, part of the ray path.The amount of S-wave splitting in the data is small, however, and we find that this causes a greater practical problem for the analysis than the known theoretical limitations of the layer-stripping theory (such as use of a finite-offset source).For the analysis of the example data we develop a number of procedures that are necessary to mitigate the low signal-to-noise levels.These include using a wide range of shot-receiver azimuths to generate data redundancy, methods of identifying and rejecting poor measurements, and a predictive layer-stripping approach that minimizes the propagation of errors through the analysis that arise from scatter in the layer-by-layer results.With the PS waves of the example data, which have a dominant period about 30 ms, we find the technique is capable of measuring S-wave splitting to a precision of about 0.5 ms for 4 or 5 layers.The number of layers successfully treated would increase if the amount of S-wave splitting were larger than in these data, for which the total cumulative S-wave splitting was about 10 ms over a 450 m depth interval.The orientation of the fast split S wave was measured with a precision of about 15 • .Our results give an S-wave velocity anisotropy of 1-2 per cent in the shallowest 25-30 m subseabed that implies the presence of a differential horizontal stress at, or very close to, the seabed.The S-wave splitting accumulated throughout the investigated section at a rate that was consistent with predictions made for a single set of parallel, fluid-filled cracks with crack-density about 0.015.The fast S wave was found to be oriented at 75 ± 15 • for the uppermost 150 m, before drifting clockwise to an azimuth of 190-210 • .The clockwise drift in the fast S-wave polarization direction implies a transfer of dominance between one set of cracks in the near surface (probably related to the slope of the seabed) and a different set in deeper sediments of tectonic origin.A zone of azimuthal isotropy, where the mechanisms that produce the elastic anisotropy may cancel each other out, occupies the interval between the two almost orthogonal sets of cracks.From interpretation
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".