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Record W4367676502 · doi:10.29169/1927-5129.2023.19.04

Geodynamics of the Caucasus – Anatolian-Arabian region and Turkey-Syria Earthquakes 2023

2023· article· en· W4367676502 on OpenAlexvenueno aff
Valentina Svalova

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeodynamicsGeologyInduced seismicitySeismologyLithosphereNatural hazardVolcanismNatural (archaeology)Natural disasterEurasian PlateTectonicsEarth scienceSubductionPaleontologyOceanography

Abstract

fetched live from OpenAlex

The activation of natural disasters in the world requires the development of new approaches to the study of geological processes, in particular, at the boundaries of lithospheric plates, characterized by earthquakes, increased seismicity, volcanism, landslide processes, tsunamis and other dangerous natural processes and hazards. Earthquake of M 7.8 struck south -eastern Turkey and north - western Syria on 6 February 2023. The M 7.8 earthquake is the largest in Turkey since the 1939 Erzincan earthquake, and the second-strongest recorded in the country, after the 1668 North Anatolia earthquake. More than 52,800 deaths were confirmed: more than 46,100 in Turkey, and more than 6,700 in Syria. It is the deadliest natural disaster in Turkey's modern history. The earthquakes caused over US$100 billion in damages. The geodynamic models construction for the deep structure of natural hazards regions is an important contribution to the study of active continental margins, which is necessary for the earthquake forecast, prediction and prognosis, assessing geoecological risks and preparing population actions in the event of natural disasters and catastrophes. The Caucasus - Arabian region is a complex highly-stressed geodynamic structure, characterized by increased heat flow, high seismicity, magmatism and volcanism. The geodynamics of the Caucasus - Arabian region is determined by the collision of the Eurasian and Arabian lithosphere plates, as well as the complex history of the development of the Alpine-Himalayan belt and surrounding territories. The problem solution of geological structures formation and evolution in various complex geodynamic settings and natural hazards forecast and prognosis requires an analysis of all available geological - geophysical data, as well as the formulation and solution of problems of mechanical and mathematical modeling. Slow lithospheric deformations are simulated by models of viscous flow in multi-layered, incompressible, high-viscosity Newtonian fluid, using Navier-Stocks equation and discontinuity equation. The solution of the inverse problem of geodynamics by the direct method is developed. The first inverse problem of geodynamics was solved - the restoration of the velocity fields, pressures and stresses at the depth of the lithosphere according to the available data on the velocities on the surface. The second inverse problem of geodynamics has been posed and solved - the determination of the movement of boundaries at the depth of the lithosphere based on the given movements of the surface. The solutions obtained can be used to analyze deep geodynamic problems, and together with geothermal modeling, geological and geophysical methods and seismic tomography can serve as a reliable apparatus for studying deep geodynamics due to the formation and evolution of geological structures and the lithosphere stress-strain state researches. The solution of the problem is analyzed on the example of the Caucasus - Arabian region geodynamics. The Geodynamic concept of geoenvironment has been developed. Geodynamic models of the regions of hazardous natural processes in order to predict and prevent natural disasters and catastrophes are constructed. An algorithm for creating monitoring systems is suggested.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.225
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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