Bayesian inference for ultralow velocity zones in the Earth's lowermost mantle: Complex ULVZ beneath the east of the Philippines
Bibliographic record
Abstract
Abstract Ultralow velocity zones (ULVZs) are small‐scale structures with a sharp decrease in S and P wave velocity, and an increase in the density on the top of the Earth's core‐mantle boundary. The ratio of S and P wave velocity reduction and density anomaly are important to understanding whether ULVZs consist of partial melt or chemically distinct material. However, existing methods such as forward waveform modeling that utilize 1‐D and 2‐D Earth‐structure models face challenges when trying to uniquely quantify ULVZ properties because of inherent nonuniqueness and nonlinearity. This paper develops a Bayesian inversion for ULVZ parameters and uncertainties with rigorous noise treatment to address these challenges. The posterior probability density of the ULVZ parameters (the solution to the inverse problem) is sampled by the Metropolis‐Hastings algorithm. To improve sampling efficiency, parallel tempering is applied by simulating a sequence of tempered Markov chains in parallel and allowing information exchange between chains. First, the Bayesian inversion is applied to simulated noisy data for a realistic ULVZ model. Then, measured data sampling the lowermost mantle under the Philippine Sea are considered. Cluster analysis and visual waveform inspection suggest that two distinct classes of ScP (S waves converted to, and reflected as, P waves) waves exist in this region. The distinct waves likely correspond to lateral variability in the lowermost mantle properties in a NE‐SW direction. For the NE area, Bayesian model selection identifies a two‐layer model with a gradual density increase as a function of depth as optimal. This complex ULVZ structure can be due to the percolation of iron‐enriched, molten material in the lowermost mantle. The results for the SW area are more difficult to interpret, which may be due to the limited number of data available (too few waveforms to appropriately reduce noise) and/or complex 2‐D and 3‐D structures that cannot be explained properly by the 1‐D models required by our inversion approach. In particular, the complex waveforms require highly layered 1‐D models to fit the data. These models appear physically unreasonable and suggest that the SW region cannot be explained by 1‐D structure.
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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) | 0.000 | 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".