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Record W3000382179 · doi:10.1093/gji/ggz574

The upper mantle beneath the South Atlantic Ocean, South America and Africa from waveform tomography with massive data sets

2020· article· en· W3000382179 on OpenAlexaff
Nicolas Celli, Sergei Lebedev, A. J. Schaeffer, Matteo Ravenna, Carmen Gaina

Bibliographic record

VenueGeophysical Journal International · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
FundersEuropean Regional Development FundGeological Survey of IrelandScience Foundation IrelandCentre of Excellence for Environmental Decisions, Australian Research Council
KeywordsCratonGeologyLithosphereCrustMantle (geology)SeismologySeismogramGeophysicsTectonics

Abstract

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SUMMARY We present a tomographic model of the crust, upper mantle and transition zone beneath the South Atlantic, South America and Africa. Taking advantage of the recent growth in broadband data sampling, we compute the model using waveform fits of over 1.2 million vertical-component seismograms, obtained with the automated multimode inversion of surface, S and multiple S waves. Each waveform provides a set of linear equations constraining perturbations with respect to a 3-D reference model within an approximate sensitivity volume. We then combine all equations into a large linear system and solve it for a 3-D model of S- and P-wave speeds and azimuthal anisotropy within the crust, upper mantle and uppermost lower mantle. In South America and Africa, our new model SA2019 reveals detailed structure of the lithosphere, with structure of the cratons within the continents much more complex than seen previously. In South America, lower seismic velocities underneath the transbrasilian lineament (TBL) separate the high-velocity anomalies beneath the Amazon Craton from those beneath the São Francisco and Paraná Cratons. We image the buried portions of the Amazon Craton, the thick cratonic lithosphere of the Paraná and Parnaíba Basins and an apparently cratonic block wedged between western Guyana and the slab to the west of it, unexposed at the surface. Thick cratonic lithosphere is absent under the Archean crust of the São Luis, Luis Álves and Rio de La Plata Cratons, next to the continental margin. The Guyana Highlands are underlain by low velocities, indicating hot asthenosphere. In the transition zone, we map the subduction of the Nazca Plate and the Chile Rise under Patagonia. Cratonic lithosphere beneath Africa is more fragmented than seen previously, with separate cratonic units observed within the West African and Congo Cratons, and with cratonic lithosphere absent beneath large portions of Archean crust. We image the lateral extent of the Niassa Craton, hypothesized previously and identify a new unit, the Cubango Craton, near the southeast boundary of the grater Congo Craton, with both of these smaller cratons unexposed at the surface. In the South Atlantic, the model reveals the patterns of interaction between the Mid-Atlantic Ridge (MAR) and the nearby hotspots. Low-velocity anomalies beneath major hotspots extend substantially deeper than those beneath the MAR. The Vema Hotspot, in particular, displays a pronounced low-velocity anomaly under the thick, high-velocity lithosphere of the Cape Basin. A strong low velocity anomaly also underlies the Cameroon Volcanic Line and its offshore extension, between Africa and the MAR. Subtracting the global, age-dependent VS averages from those in the South Atlantic Basins, we observe areas where the cooling lithosphere is locally hotter than average, corresponding to the location of the Tristan da Cunha, Vema and Trindade hotspots. Beneath the anomalously deep Argentine Basin, we image unusually thick, high-velocity lithosphere, which suggests that its anomalously great depth can be explained, at least to a large extent, by isostatic, negative lithospheric buoyancy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.210
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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

Citations129
Published2020
Admission routes1
Has abstractyes

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