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Record W1656573966 · doi:10.1111/1365-2478.12277

Seismic imaging through the volcanic rocks of the Snake River Plain: insights from Project Hotspot

2015· article· en· W1656573966 on OpenAlexaff
Lee M. Liberty, Douglas R. Schmitt, John W. Shervais

Bibliographic record

VenueGeophysical Prospecting · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeologyHotspot (geology)VolcanoVolcanismSeismologyVolcanic rockGeochemistryEarth scienceRegional geologyPaleontologyTectonics

Abstract

fetched live from OpenAlex

ABSTRACT Hotspot: The Snake River Geothermal Drilling Project was undertaken to better understand geothermal systems across the Snake River Plain volcanic province. A series of surface and borehole seismic profiles were obtained to provide insights into volcanic stratigraphy and test the capabilities of engineering‐scale seismic imaging in such terranes. The Kimberly site drilled through 1.9 km of mostly rhyolite, with thin sedimentary interbeds in the upper part of the section. The Kimama site drilled through 1.9 km of mostly basalt with sedimentary interbeds at ∼200 m depth and 1700 m depth. The Mountain Home site contained numerous sediment and volcanic rock layers. Downhole and surface vibroseis seismic results suggest sedimentary interbeds at depth correspond with low‐velocity, high‐temperature zones that relate to reflections on seismic profiles. Our results suggest that eruption flow volumes can be estimated and flow boundaries can be imaged with surface seismic methods using relatively high‐fold and wide‐angle coverage. High‐frequency attenuation is observed at all sites, and this deficit may be countered by acquisition design and a focus on signal processing steps. Separation of surface and body waves was obtained by muting, and the potential for large static effects was identified and addressed in processing. An accurate velocity model and lithology contacts derived from borehole information improved the confidence of our seismic interpretations.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.926

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 science0.0010.000
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 head0.019
GPT teacher head0.225
Teacher spread0.206 · 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 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

Citations5
Published2015
Admission routes1
Has abstractyes

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