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Record W4408427716 · doi:10.5194/egusphere-egu25-11735

Artificial Smoker: Geophysical characterization of an ultraslow ridge system for sustainable resource management

2025· preprint· en· W4408427716 on OpenAlexaff
Bhargav Boddupalli, Børge Arntsen, T. A. Minshull, Ketil Hokstad, Sylvie Leroy, Ståle Emil Johansen, Louise Watremez, Ana Corbalán, Lars Sørum

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCharacterization (materials science)RidgeResource (disambiguation)BusinessEnvironmental resource managementGeologyEnvironmental scienceComputer scienceNanotechnologyMaterials sciencePaleontology

Abstract

fetched live from OpenAlex

Hydrothermal circulation is a fundamental Earth process that transfers elements and minerals from the crust and mantle to the oceans. This circulation commonly occurs along tectonic plate boundaries in the oceans, where heat sources are located at relatively shallow depths (~2–3 km). Cold seawater percolates downward, becomes heated, and is enriched with minerals from the host rock and magmatic volatiles. The resulting hot fluids (exceeding 300°C) rise buoyantly and are expelled into the ocean through chimney-like structures on the seafloor, commonly referred to as "Black Smokers." The ejected particles settle on the seafloor, forming rich mineral deposits known as "Seafloor Massive Sulfide" (SMS) deposits, making mid-ocean ridges highly attractive for meeting future mineral demands. Moreover, ridge settings hold significant potential for geothermal energy, white hydrogen production, and other valuable resources. However, harnessing these resources requires a thorough understanding of the complex hydrothermal systems to develop sustainable resource management strategies.Hydrothermal venting sites are widespread along the mid-ocean ridge system, occurring at all spreading rates and across diverse geological settings. However, the mechanisms driving hydrothermal processes vary depending on factors such as the presence of magma bodies, permeable zones, tectonic activity, and temperature. At ultraslow spreading ridges, where spreading rates are less than 20 mm/yr—such as the Southwest Indian Ridge, Mohns Ridge, and Knipovich Ridge—tectonic processes dominate over magmatic activity, resulting in the exhumation of ultramafic material to the seafloor along large-scale detachment faults.In this study, we developed two-dimensional, high-resolution velocity models through the crust and uppermost mantle of the Southwest Indian Ridge using wide-angle ocean-bottom seismic data. We present two ~150 km-long, high-resolution P-wave velocity models orthogonal to each other, running across and along the ridge axis at 64°30’E. We employed a state-of-the-art imaging technique known as full waveform inversion (FWI) using data from 32 ocean-bottom seismometers positioned along the two profiles. FWI is a data-fitting method in which the forward operator iteratively predicts the observed data by backpropagating the misfits to update the velocity model, thereby producing higher-resolution images of the subsurface.Based on our high-resolution velocity models, we observe finer patterns of velocity anomalies compared to traveltime models, revealing more detailed variations in the degree of fluid-rock interaction. These interactions are influenced by the presence of faults and the extent of tectonic damage, aiding in the mapping of hydrothermal circulation. Additionally, our high-resolution images provide an improved understanding of the distribution of serpentinization and its correlation to mode of spreading. Overall, the high-resolution velocity models support the assessment of the feasibility of "Artificial Smoker," which replicates natural smokers, for the environmentally sustainable extraction of minerals, white hydrogen, and geothermal resources.

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 score0.869
Threshold uncertainty score0.708

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 head0.014
GPT teacher head0.211
Teacher spread0.197 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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