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Record W4367595587 · doi:10.1093/gji/ggad183

Regional-scale resistivity structure of the middle and lower crust and uppermost mantle beneath the southeastern Canadian Cordillera and insights into its causes

2023· article· en· W4367595587 on OpenAlexafffundabout
Cedar Hanneson, Martyn Unsworth

Bibliographic record

VenueGeophysical Journal International · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaUniversity of Alberta
KeywordsGeologySubductionMagnetotelluricsLithosphereCrustAsthenosphereMantle (geology)Geothermal gradientGeodynamicsSeismologyPlate tectonicsGeophysicsElectrical resistivity and conductivityTectonics

Abstract

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SUMMARY Subduction zones are recognized as an important class of plate boundaries and are the location of a number of important geological processes. They are also important because of the mineral and geothermal energy resources formed by plate convergence. While subduction zones around the world have a number of common features, there are also significant differences among them. The Cascadia subduction zone in southern British Columbia is characterized by a relatively hot subducting plate, and a broad backarc region that is believed to exhibit a shallow, convecting asthenosphere. The magnetotelluric (MT) method is a useful tool to study subduction zones and backarc regions because measurements of subsurface resistivity are sensitive to the presence of fluids. A number of previous MT studies have taken place in this region, but they were limited to a 2-D approach to data analysis. As the MT method has developed, it has become clear that there is a significant advantage to using a 3-D approach to data analysis. This paper presents the first regional-scale 3-D resistivity model of the southern Canadian Cordillera and provides new insights into the lithospheric structure and the distribution of fluids. The southeastern Canadian Cordillera has high heat flow and numerous thermal springs, the locations of which are often controlled by faults. However, the deeper thermal structure and origin of the fluids are poorly understood. To develop an improved understanding of the structure of this area, MT data measured at 331 locations were used to create a 3-D model of subsurface electrical resistivity. This study is primarily focused on the Omineca and Foreland morphogeological belts in southeastern British Columbia, which are separated by the southern Rocky Mountain Trench. The resistivity model is presented to a depth of 100 km and a number of conductive features are observed in the crust and uppermost mantle of the southeastern Cordillera. The locations of these conductors broadly matched previously reported conductors, but the 3-D inversion revealed new details of their geometry. The previously reported Canadian Cordilleran Regional conductor was modelled as a number of discrete conductors in the depth range 15–55 km beneath the Omineca belt. Temperatures approximately in the range 400–700 °C are expected at depths of 15–26 km and saline aqueous fluids are likely the cause of the low resistivity. Temperatures approximately in the range 700–1300 °C are expected at depths of 26–55 km and small volumes of partial melt may explain the low resistivity. The Southern Alberta–British Columbia conductor, Red Deer conductor and Loverna conductor were imaged as a single connected conductor, whose low resistivity is likely caused by sulphide mineralization. A group of conductors was imaged near the southern Rocky Mountain Trench in the depth range 10–70 km and their low resistivity is likely caused by interconnected saline fluids and possibly interconnected graphite films. To understand if the distribution of thermal springs was correlated with the 3-D resistivity model, a statistical study was undertaken. This showed no clear correlation between crustal conductance and the distribution of thermal springs.

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 score0.517
Threshold uncertainty score0.999

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 head0.021
GPT teacher head0.228
Teacher spread0.207 · 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

Citations9
Published2023
Admission routes3
Has abstractyes

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