High-Precision Detection of Clock Errors in Seismograms from the Ordos Block Using Multicomponent Noise Cross Correlations
Bibliographic record
Abstract
Abstract The accuracy of absolute timing in seismic records is critical for applications such as earthquake location, seismic tomography, and earthquake early warning. This study develops and applies a high-precision method for detecting clock errors based on continuous three-component waveform data recorded from 2019 to 2023 at 188 permanent seismic stations in and around the Ordos block. The method combines multicomponent ambient noise cross correlation and weighted stacking to estimate daily clock errors by analyzing the symmetry deviation of cross-correlation functions. Constraining each station’s drift estimate with multiple neighboring pairs through weighted averaging improves the stability and robustness of the detection, making it well suited for monitoring of large-scale seismic networks. The results show that: (1) A total of 72 stations (38.3%) exhibited significant clock drift over the 5-year period, with an average annual anomaly rate of ∼11.6%; some stations experienced clock drift for over 10 months in a single year. (2) The maximum drift exceeded ±10 s, whereas drifts larger than ∼0.5 s can be detected and validated, typically using teleseismic events. The anomalous stations were spatially dispersed, indicating that the drift was primarily caused by station-specific issues rather than regional environmental interference. (3) The types of clock drift observed include linear, nonlinear, abrupt, and compound patterns, reflecting a range of instrumental failures or environmental factors. In addition, validation using an adjacent-teleseism double-differential timing method successfully identified a subtle drift of ∼0.2 s at station SX.LOF, demonstrating the method’s potential sub-second sensitivity, though such detections near the threshold are less stable. This study establishes a 5-year database of clock errors for seismic stations in the Ordos region, providing essential data support for seismological applications and offering a methodological reference for quality control in large seismic networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".