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Inversion of first-arrival seismic traveltimes without rays, implemented on unstructured grids

2011· article· en· W1959728579 on OpenAlexafffundabout
Peter G. Lelièvre, Colin G. Farquharson, Charles A. Hurich

Bibliographic record

VenueGeophysical Journal International · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInversion (geology)Ray tracing (physics)DiscretizationAlgorithmGeologyComputer scienceSeismologyMathematical analysisMathematicsPhysicsOpticsTectonics

Abstract

fetched live from OpenAlex

We develop a method for inverting first-arrival seismic traveltimes without using ray tracing in the forward solution, nor to calculate sensitivity information. We consider unstructured 2-D triangular and 3-D tetrahedral grids for discretizing the subsurface velocity distribution. Unstructured grids provide some computational advantages when dealing with complicated shapes that are difficult to represent with rectilinear grids. However, we stress that the key details of our inversion approach that avoid ray tracing are equally relevant to rectilinear grids. The forward problem is solved using the Fast Marching Method, an efficient numerical algorithm that can be used for propagating first-arrival seismic wave fronts through a velocity distribution. Our minimum structure inversion algorithm uses sensitivity information calculated directly during the forward solution. This calculation relies on explicit symbolic differentiation of the forward modelling equations and, as such, our inversion approach does not require ray tracing and uses sensitivity information that is numerically consistent with the forward solution. Our method is applied to 2-D and 3-D geologically realistic synthetic scenarios based on the Voisey's Bay massive sulfide deposit in Labrador, Canada.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.997

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)0.0040.000

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 head0.020
GPT teacher head0.232
Teacher spread0.212 · 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.

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

Citations38
Published2011
Admission routes3
Has abstractyes

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