Raton Basin Induced Seismicity Is Hosted by Networks of Short Basement Faults and Mimics Tectonic Earthquake Statistics
Bibliographic record
Abstract
Abstract The Raton Basin has been an area of injection induced seismicity for the past two decades. Previously, the reactivated fault zone structures and spatiotemporal response of seismicity to evolving injection have been poorly constrained due to sparse publicly available seismic monitoring. The application of a machine‐learning phase picker to 4 years of continuous seismic data from a local array enables the detection and location of ∼38,000 earthquakes. The events from 2016 to 2020 are ∼2.5–6 km below sea level and range from M L < −1 to 4.2. Most earthquakes occur within previously identified ∼N‐S zones of seismicity, however our new catalog illuminates that these zones are composed of many short faults with variable orientations. The two most active zones, the Vermejo Park and Tercio zones, are potentially linked by small intermediate faults. In total, we find ∼60 short (<3 km long) basement faults with strikes from WNW to NNE. Faulting mechanisms are predominantly normal but some variability, including reverse dip‐slip and oblique‐slip, is observed. The Trinidad fault zone, which previously hosted a M w 5.3 earthquake in 2011, is quiescent during 2016–2020, likely in response to both slow accumulation of tectonic strain after the 2011 sequence, and the significant decrease (80% reduction) in nearby wastewater injection from 2012 to 2016. Unlike some other regions, where induced seismicity was triggered in response to higher injection rates, the Raton Basin's frequency‐magnitude and spatiotemporal statistics are not distinguishable from tectonic seismicity. The similarity suggests that seismicity in the Raton Basin is predominantly releasing tectonic stress.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".