Seismicity During the Initial Stages of the Guy‐Greenbrier, Arkansas, Earthquake Sequence
Bibliographic record
Abstract
Abstract We analyze the background seismicity, initiation, and earliest stages of the Guy‐Greenbrier, Arkansas, earthquake sequence, which was potentially induced by wastewater injection starting in July 2010, during the 3 month time period 1 June to 1 September 2010. High‐resolution observations of low‐magnitude seismicity, and the high‐quality Arkansas public well database, facilitate detailed analysis of spatial and temporal correlations between earthquakes, wastewater injection, and hydraulic fracturing. We detected 14,604 earthquakes, with magnitudes −1.5≤ML≤2.9, using two sensitive, waveform similarity‐based event detection methods in parallel: Fingerprint And Similarity Thresholding, and template matching. We located the 1,740 largest earthquakes that form 16 spatially compact clusters, using P and S phases from 3 stations with the double‐difference relocation algorithm and an improved velocity model constrained by the location of quarry blasts. We enhanced the temporal resolution of these event clusters by assigning smaller unlocated events to a cluster based on waveform similarity. Most clustered earthquakes during this time were both spatially and temporally correlated with hydraulic fracturing stimulation at several production wells. For one cluster, microseismicity was correlated with individual stages of stimulation. Many other wells had no detectable nearby seismicity during stimulation. We found a smaller number of events located on the Guy‐Greenbrier Fault that were likely induced by wastewater injection. The concurrent presence of seismicity induced by hydraulic fracturing and wastewater injection presents a challenge for attribution and seismic hazard characterization, but the combination of precision seismology and high‐quality well information allows us to disentangle the effects of these two processes.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".