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Record W2999909900 · doi:10.1111/1365-2478.12930

A hybrid approach to compute seismic travel times in three‐dimensional tetrahedral meshes

2020· article· en· W2999909900 on OpenAlexafffund
Maher Nasr, Bernard Giroux, J. Christian Dupuis

Bibliographic record

VenueGeophysical Prospecting · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversité LavalInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologies
KeywordsAlgorithmPolygon meshComputationComputer scienceInversion (geology)Environmental geologyLinear interpolationTravel timeInterpolation (computer graphics)Intersection (aeronautics)TetrahedronSlownessMathematical optimizationHydrogeologyGeologyMathematicsGeometryMetamorphic petrologySeismology

Abstract

fetched live from OpenAlex

ABSTRACT We propose an optimized method to compute travel times for seismic inversion problems. It is a hybrid method combining several approaches to deal with travel time computation accuracy in unstructured meshes based on tetrahedral elementary cells. As in the linear travel time interpolation method, the proposed approach computes travel times using seismic ray paths. The method operates in two sequential steps: At a first stage, travel times are computed for all nodes of the mesh using a modified version of the shortest path method. The difference with the standard version is that additional secondary nodes (called tertiary nodes) are added temporarily around seismic sources in order to improve accuracy with a reasonable increase in computational cost. During the second step, the steepest travel time gradient method is used to trace back ray paths for each source–receiver pair. Travel times at each receiver are then recomputed using slowness values at the intersection points between the ray path and the traversed cells. A number of numerical tests with an array of different velocity models, mesh resolutions and mesh topologies have been carried out. These tests showed that an average relative error in the order of 0.1% can be achieved at a computational cost that is suitable for travel time inversion.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.205
Teacher spread0.188 · 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 designSimulation or modeling
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

Citations8
Published2020
Admission routes2
Has abstractyes

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