Recent earthquakes on unmapped faults highlight hidden seismic hazards within the Golden Triangle region of Laos, Thailand and Myanmar
Bibliographic record
Abstract
SUMMARY In the past decade, six $M_w\, \ge$5.5 earthquakes struck the mountainous Golden Triangle region (Laos, Thailand, Myanmar) of the southeast India–Eurasia collision zone. The largest of them, the 2019 $M_w$ 6.2 Sainyabuli earthquake in western Laos, shook river communities, dams and a UNESCO World Heritage Site, prompting a need to understand regional earthquake potential. We used Interferometric Synthetic Aperture Radar (InSAR) data and modelling to solve for the 2019 main shock source parameters, revealing right-lateral strike-slip along a 24 km-long NNW-trending fault which has limited topographic expression and was previously unmapped. InSAR modelling of its largest ($M_w$ 5.5) aftershock in 2021 revealed a 7 km-long splay fault, also previously unrecognized. The 2022 $M_w$ 5.9 Keng Tung earthquake in the northern Golden Triangle also ruptured an unknown, NW-trending right-lateral fault conjugate to longer, NE-trending faults nearby. Collectively, this shows that the region contains faults which are little evident in global digital topography and/or obscured by vegetation but long enough to generate sizeable earthquakes that should be accounted for in seismic hazard assessments. We relocated well-recorded aftershocks and other background seismicity (1978–2023) from across the Golden Triangle using the mloc software. Calibrated hypocentres span focal depths of 5–24 km and are distributed away from the main InSAR-modelled fault traces, another indication of fault structural immaturity. For the three 2019–2022 InSAR-constrained events, we also obtained moment tensor solutions from regional seismic waveform inversion. InSAR-derived peak slip depths and seismological centroid depths are mostly shallow (3–5 km), while focal depths are generally located in areas of low coseismic slip near the bottom of InSAR model faults. More broadly, we estimate a regional seismogenic thickness of $\sim$17 km (the 90 $\rm \,per\,cent$ seismicity cut-off depth), a crucial parameter for seismic hazard calculations and building codes. Our integration of remote-sensing and seismologic analyses could be a blueprint for assessing earthquake potential of other regions with sparse instrumentation and limited topographic fault expression.
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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 | 0.000 | 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) | 0.000 | 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".