Earthquake and Moment Tensor Catalogs of the 2015-2017 Pamir Earthquake Sequence
Bibliographic record
Abstract
A sequence of three strong (M W 7.2–6.4) and several moderate (M W 4.4–5.7) earthquakes struck the Pamir Plateau and surrounding mountain ranges of Tajikistan, China, and Kyrgyzstan in 2015–2017. With a local seismic network in operation in the Xinjiang province since August 2015, an aftershock network on the Pamir Plateau of Tajikistan since February 2016, and additional permanent regional seismic stations, we were able to record the succession of the fore-, main-, and aftershock sequences at local distances with good azimuthal coverage. We located 11,784 seismic events and determined the moment tensor for 33 earthquakes. The seismicity delineates the major tectonic structures of the Pamir, i.e., the thrusts that absorb shortening along the plateau thrust front, and the strike-slip and normal faults that dissect the Plateau into a westward extruding and a northward advancing block. Fault ruptures were activated subsequently at increasing distances from the initial M W 7.2 Sarez. All mainshock areas but the initial one exhibited foreshock seismicity which was not modulated by the occurrence of the earlier earthquakes. The tabular ASCII data of the seismic event catalog consist of origin date, time, location, depth and magnitude of the events, along with the quality measures: number of P- and S-wave arrival time picks, location root-mean-square misfit and localization method. The tabular ASCII data of the moment tensor catalog consist of origin date, time, location, the six independent components of the moment tensor, the moment magnitude, and the orientation of the preferred fault plane parameterized as fault strike, dip and rake. -------------------------- Version history: 2026-01-31: Version 2.0 Exchange of the file "2022-007_Bloch-et-al_moment_tensor_catalog.txt" with the new file "2022-007_Bloch-et-al_moment_tensor_catalog_correct_norm_v2.0.txt". The original file is available in the "previous-versions" folder. Reason: The normalization of the components of the moment tensor (columns mrr, mtt, mff, mrt, mrf, mtf, exp) was incorrect so that the resulting moment tensor had a too large absolute moment. The reported moment magnitude and the relative scaling of the moment tensor components was correct, thought.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".