The 2019-2020 Khalili (Iran) earthquake sequence - anthropogenic seismicity in the Zagros Simply Folded Belt?
Bibliographic record
Abstract
We investigate the origin of a long-lived earthquake cluster in the Fars arc of the Zagros Simply Folded Belt that is co-located with the major Shanul natural gas field near the small settlement of Khalili. The cluster emerged in January 2019 and initially comprised small events of w 5.4 and 5.7 earthquakes, which were followed by > 100 aftershocks. We assess the spatio-temporal evolution of the earthquake sequence using multiple event hypocenter relocations, waveform inversions, and Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) measurements and models. We find that the early part of the sequence is spatially distinct from the June 9, 2020 earthquakes and their aftershocks. Moment tensors, centroid depths, and source parameter uncertainties of fifteen of the largest ( M n ≥ 4.0) events show that the sequence is dominated by reverse faulting at shallow depths (mostly ≤ 4 km) within the sedimentary cover. InSAR modelling shows that the M w 5.7 mainshock occurred at depths of 2–8 km, with a rupture length and maximum slip of ~20 km and ~0.5 m, respectively. Our results strongly suggest that the 2019-2020 Khalili earthquake sequence was influenced by the operation of the Shanul field, making these the first known examples of gas extraction anthropogenic earthquakes in Zagros. Understanding the genesis of such events to distinguish man-made seismicity from natural earthquakes is helpful for hazard and risk assessment, notably in Iran which is both seismically-active and rich in oil and gas reserves.
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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 | 0.000 | 0.000 |
| 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".