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Calculating of Taftan Volcano Displacement Using PSI Technique and Sentinel 1 Images

2024· article· en· W4396780832 on OpenAlexaff
Mahdieh Shirmohammadi, Saied Pirasteh, Jie Shen, Jonathan Li

Bibliographic record

VenueISPRS annals of the photogrammetry, remote sensing and spatial information sciences · 2024
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVolcanoDisplacement (psychology)GeologySeismology

Abstract

fetched live from OpenAlex

Abstract. Taftan is a semi-active volcano located in southeast of Iran with a number of craters. The main objective of this study is to investigate whether subsidence or uplift in Taftan peak. A total number of 58 images of Sentinel 1-A acquired between January 2015 to December 2020 in the ascending orbit mode and 102 images of both Sentinel 1-A, Sentinel 1-B acquired between October 2014 to June 2020 in the descending orbit mode were pre-processed for this purpose. The interferograms with the permanent scatterer interferometry (PSI) method were created using SARPROZ and StaMPS software in which atmospheric corrections were made automatically and following that get surface displacement of Taftan volcano. The results of the Line Of Sight (LOS) displacement corresponding to the uplift was observed to be 0.5 mm to 1 mm yr-1 for the ascending orbit and 1 mm yr-1 for the descending orbit. Because of GPS station lack close to Taftan volcano, the GPS measurements of one station located in the study area (Saravan station) was used to check the accuracy of PSI method, the GPS station of SARAVAN has been located inside of town and it is appropriate to use and analyze PSI technique in this station. As a result, it was found that the PSI method is in good agreement with the GPS data.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.300
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes1
Has abstractyes

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