Biomarkers heat up during earthquakes: New evidence of seismic slip in the rock record
Bibliographic record
Abstract
During earthquakes, faults heat up due to frictional work. However, evidence of heating from paleoearthquakes along exhumed faults remains scarce. Here we describe a method using thermal maturation of organic molecules in sedimentary rock to determine whether a fault has experienced differential heating compared to surrounding rocks. We demonstrate the utility of this method on an ancient, pseudotachylyte-hosting megathrust at Pasagshak Point, Alaska. Measurements of the ratio of thermally stable to thermally unstable compounds (diamondoids/n-alkanes) show that the melt-bearing rocks have higher thermal maturity than surrounding rocks. Furthermore, the mineralogy of the survivor grains and the presence of any organic molecules allow us to constrain the temperature rise during the ancient earthquakes to 840–1170 °C above ambient temperatures of ∼260 °C. From this temperature rise, we estimate that the frictional work of the earthquake was ∼105–228 MJ/m2. Using experimental friction measurements as a constraint, we estimate that the minimum slip necessary for heating was ∼1–8 m. This paper demonstrates that biomarkers will be a useful tool to identify seismic slip along faults without frictional melt.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".