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Record W4322002938 · doi:10.5194/egusphere-egu23-12543

Active tectonics from UAS-HR-DSM combined with PSInSAR: Case example along the Longitudinal Valley - Eastern Taiwan

2023· preprint· en· W4322002938 on OpenAlexaff
Benoı̂t Deffontaines, Kuo-Jen Chang, Ren-Fan Li, Chii-Wen Lin, Paolo Pasquali, samuel Magalhaes, Gérardo Fortunato

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsAlpha Technologies (Canada)
Fundersnot available
KeywordsGeologySeismologyGeodesyTectonicsDigital elevation modelVolcanoRange (aeronautics)Displacement (psychology)Elevation (ballistics)TerrainMagnitude (astronomy)GeographyRemote sensingCartographyGeometryPhysics

Abstract

fetched live from OpenAlex

Taiwan result in the active collision of both Eurasian and Philippine Sea Plates characterized by an annual average convergence rate close to 10 cm.y-1. The Longitudinal Valley is parallel and eastward of the Central (Backbone) Range which is made of metamorphic rocks, and is also situated to the west of the Coastal Range (of volcanic affinity). In between both, lay the Longitudinal Valley (125km long and N020°E trending) which behave as the active crustal suture zone. The latter presents both inter-seismic creeping displacement (Champenois et al., 2013, Deffontaines et al., 2018) and was hit by 7 major earthquakes of magnitudes larger than 5 during the last 70 years which highlights its high seismic hazards.We combine herein a preseismic UAS survey (May 20, 2015) with one done immediately after the last large earthquake on the eastern Central Range (Oct 07, 2022). We therefore study both (1) the differences from a quantitative point of view; and (2) from a morpho-structural qualitative analysis point of view.We acquired so many high-resolution photographs using several drones flying at 350 meters above the ground. After photogrammetric processing, we calculate both (1) a high-resolution Digital Elevation Model (UAS-HR-DSM) that takes into account buildings and vegetations, and deduce (2) a Digital Terrain Model (UAS-HR-DSM) corresponding to the ground. Our ground validation (GCP’s) leads us to get a 7cm planimetric resolution (X, Y) and below 40cm vertical accuracy.This UAS-HR-DSM combined with field work and the preliminary PSInSAR (PALSAR-JAXA) processing led us to better characterize the active tectonic features through a detailed morphostructural analysis. It also permit us to map into much details the active structures and consequently to up-date the pre-existing geological mappings (e.g. CGS geological maps, Lin et al., 2009; Shyu et al., 2005, 2006, 2007, 2008). Then we up-date and combined our new structural scheme with geodetic data (levelings, GPS…) and PALSAR PSInSAR results acquired during the same monitoring time period to locate, characterize and quantify the active tectonic structures, taking into account previous works (e.g. Yu et al., 1997; Lee et al., 2008; Hsu et al., 2009; Huang et al., 2010…). We then precise structural geometries and some geological processes as well as the location of active folds and active faults during the PSInSAR monitoring time-period.This may lead us to better constrain the seismic hazards and the earthquake cycles of the place.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.065
GPT teacher head0.233
Teacher spread0.168 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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