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Record W4408055206 · doi:10.1190/geo2024-0486.1

Reflection and Love-wave imaging of a buried valley using 2D 3C land-streamer seismic data

2025· article· en· W4408055206 on OpenAlexaffabout
Brian Villamizar, Aaron DesRoches

Bibliographic record

VenueGeophysics · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsNuclear Waste Management Organization
Fundersnot available
KeywordsReflection (computer programming)SeismologyGeologyRemote sensingComputer science

Abstract

fetched live from OpenAlex

ABSTRACT We conduct reflection and Love-wave imaging on three 2D, 3C land-streamer seismic lines to characterize a buried bedrock valley underlying glacial deposits situated within southern Ontario, Canada. Understanding the valley’s features is important for designing above-ground structures, investigating groundwater and surface water interactions, and understanding erosion processes driven by glacial cycles. These characteristics include width, extension, overburden thickness, spatial facies distribution, and geometric configuration of the overburden-bedrock contact. Reflection imaging on the west-southwest–east-northeast trending profiles reveals the bedrock geometry and quaternary sediment stratigraphy, including a shallow reflection associated with a lithostratigraphic transition zone. We use the dispersive properties of Love waves to derive pseudo-2D S-wave velocity profiles along the seismic transects. The inversion process is constrained by the P-wave refraction velocities from reflection processing. We emphasize the significance of data preconditioning prior to surface-wave analysis and develop an optimal processing sequence for moderately to highly noisy gathers. Likewise, we construct bedrock-reaching velocity profiles for depth conversion of reflectivity sections by using S-wave velocity from Love-wave inversion, regression analysis, and water well data. Our signal preconditioning and integrated velocity modeling approach can be implemented in other areas where land-streamer seismic surveys are available, enhancing quantitative imaging and depth conversions. Blind borehole records demonstrate average depth-conversion errors of less than 2% following this approach. The Love-wave velocity imaging and the body-wave reflection imaging correlate well with each other and with lithologic changes observed in water wells. This is demonstrated by the alignment of velocity contrasts with the geometry of the shallow reflection event at the lithostratigraphic transition zone. We find that the bedrock valley is approximately 60 m deep with a southward thinning width, possibly due to the valley’s geometry shifting from a north–south to an east–west orientation.

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 score0.903
Threshold uncertainty score0.995

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 head0.034
GPT teacher head0.262
Teacher spread0.228 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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