Including Earth-structure uncertainties in nonlinear moment-tensor estimations
Bibliographic record
Abstract
Earthquake-source parameters can be estimated from seismic waveforms. Since these data indirectly observe the deformation process, parameters of a physical model that quantifies the deformation process are inferred through the inverse problem; which is under-determined. This requires several assumptions to be made about Earth structure and other aspects that affect the source parameter estimation. These assumptions primarily include a simplified seismic velocity model of the Earth waveform and noise models. The specific model choices affect data residuals and can lead to biased source parameter estimations and unrealistic assessment of the associated source-parameter uncertainties. While data errors are routinely included in parameter estimation for full centroid moment tensors, less attention has been paid to theory errors related to velocity model uncertainties and how these affect the resulting moment-tensor uncertainties. Here, we study non-linear full moment tensors with several simulated data sets and demonstrate that subsurface structure uncertainties can profoundly affect parameter estimation and that their inclusion leads to more realistic parameter uncertainty quantification. We present a solution to include model errors by estimating non-stationary (non-Toeplitz) error covariance matrices that lead to appropriate source-parameter estimates and uncertainties. Finally, we demonstrate the influence of these noise parameterisations on real regional seismic data of the ML 4.4, 13 June 2015 Fox Creek event, Canada. Including uncertainties in Earth-structure resulted in robust source parameter estimates in case the structure was poorly known.
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 | 0.002 | 0.023 |
| 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".