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Record W4322010865 · doi:10.5194/egusphere-egu23-9686

Geologic Controls on the Genesis of the Arctic Permafrost and Sub-Permafrost Methane Hydrate-bearing System in the Beaufort–Mackenzie Delta

2023· preprint· en· W4322010865 on OpenAlexaboutno aff
Zhen Li, Elena Chabab, Erik Spangenberg, Judith M. Schicks, Thomas Kempka

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostGeologyArcticDeltaClathrate hydrateThermokarstPleistoceneMethaneGeomorphologyHydrogeologyEarth sciencePaleontologyHydrateOceanographyGeotechnical engineering

Abstract

fetched live from OpenAlex

The Canadian Mackenzie Delta (MD) is a river-mouth depocentre and the second-largest Arctic delta. It exhibits high resources of prospected sub-permafrost gas hydrates (GHs), mainly consisting of thermogenic methane (CH4) at the Mallik site, which migrated from deep source rocks. The objective of the present study is to confirm the sub-permafrost GHs formation mechanism proposed by Li et al. (2022a), stating that CH4-rich fluids were vertically transported from deep overpressurized zones via geologic fault systems and formed the present-day GH deposits in the shallower Kugmallit Sequence since the Late Pleistocene. Given this hypothesis, the coastal permafrost began to form since the early Pleistocene sea-level retreat, steadily increasing in thickness until 1 Million years (Ma) ago. Observations from well-logs and seismic profiles were used to establish the first field-scale static geologic 3D model of the Mallik site. A framework of equations of state to simulate the formation of GHs and permafrost (Li et al., 2022a, 2022b) has been developed and coupled with a numerical simulator for fluid flow as well as the transport of chemical species and heat in previous studies. Here, numerical simulations using the proven thermo-hydro-chemical simulation framework were employed to provide insights into the hydrogeologic role of the regional fault systems in view of the CH4-rich fluid migration and the spatial extent of sub-permafrost GH accumulations during the past 1 Ma. The simulated ice-bearing permafrost and GH interval thicknesses, as well as sub-permafrost temperature profiles, are consistent with the respective field observations, confirming our previously introduced hypothesis. In addition, simulation results demonstrate that the permafrost has been substantially heated to 0.8–1.3 °C, triggered by the global temperature increase of about 0.44 °C (IPCC, 2022) and further accelerated by Arctic amplification from the early 1970s to the mid-2000s. Overall, the good agreement between simulations and observations demonstrates that the present modeling study provides a valid representation of the geologic controls driving the complex permafrost-GH deposit system. The model’s applicability for predicting GH deposits in permafrost settings can provide relevant contributions to future GH exploration and exploitation activities.ReferencesIPCC, 2022: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, 3056 pp., doi:10.1017/9781009325844.Li, Z., Spangenberg, E., Schicks, J. M. & Kempka, T. Numerical Simulation of Coastal Sub-Permafrost Gas Hydrate Formation in the Mackenzie Delta, Canadian Arctic. Energies 15, 4986 (2022a). https://doi.org/10.3390/en15144986Li, Z., Spangenberg, E., Schicks, J. M. & Kempka, T. Numerical Simulation of Hydrate Formation in the LArge-Scale Reservoir Simulator (LARS). Energies 15, 1974 (2022b). https://doi.org/10.3390/en15061974

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.796
Threshold uncertainty score0.405

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.028
GPT teacher head0.231
Teacher spread0.203 · 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 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
Published2023
Admission routes1
Has abstractyes

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