Eruption of the mud volcano “Lokbatan”
Bibliographic record
Abstract
Relevance. Mud volcanoes are the main mechanism that creates the process of migration of hydrocarbons in a three-dimensional geological environment. At the same time, eruptive channels of mud volcanoes and tensile cracks created by the process of formation of diapiric structures serve as migration channels. If we take into account that almost all oil fields located in the territory of mud volcanism development are complicated by eruptive channels of mud volcanoes associated with Maikop deposits, then the mechanism of filling reservoirs of the productive stratum due to vertical migration of hydrocarbons is proved very convincingly, which is relevant for our country. The aim of this article was to study the eruptions of the Lokbatan Mud Volcano – which is one of the most famous mud volcanoes in the world due to its frequent fiery eruptions. Methods. Due to the high resolution of digital seismic equipment manufactured by Kinemetrics (USA) of the Republican Seismic Survey Center at ANAS (RSSC), records of mud volcano eruptions were recorded and analyzed. In addition, in 2022, within the framework of the international project “Expansion of the seismic network in the Caucasus and Central Asia”, 22 seismic stations manufactured by Nanometrics (Canada) were installed in Azerbaijan. For the first time in the world, in order to study the dynamics of mud volcanoes, 12 such stations were installed around the mud volcanoes Lokbatan and Otman-Bozdag. Results. Thus, it was found that the eruption of the Lokbatan volcano in 2012 during the day was characterized by 3 periods of activation. The first one occurred in the morning at 04:59:17.7 (eruption duration 7 minutes, focus depth 5 km, Е=0.64×107 J), the next ones at 09:14:40 (eruption duration 4 minutes, focus depth 5.5 km, Е= 0.70×107 J) and at 09:23:00 (duration of eruption 12 minutes, focus depth 1.2 km, Е=3.15×107 J).
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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 | 0.000 |
| 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".