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Record W4220695315 · doi:10.5194/egusphere-egu22-10601

Quantifying the effects of injection parameters on fault response under spatially homogenous and heterogenous pore-fluid conditions

2022· preprint· en· W4220695315 on OpenAlexaff
Riddhi Mandal, Semechah K. Y. Lui

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInduced seismicitySlip (aerodynamics)Geothermal gradientGeologyPerturbation (astronomy)Fluid pressureFault (geology)SeismologyPore water pressureVolume (thermodynamics)MechanicsGeotechnical engineeringThermodynamicsPhysicsGeophysics

Abstract

fetched live from OpenAlex

Heightened seismic activity due to human activities, such as wastewater injection, carbon storage and geothermal energy production, has been a rising problem in recent years. Various injection parameters and geological conditions have been shown to affect fault behaviour differently when fluid is injected on the faults, although existing observational studies about their effects often show contradictory results. Aseismic slip is also known to affect seismicity, but its exact contribution remains elusive. To address these, we perform numerical modelling to study the effects of various injection parameters on fault slip behaviour. Our fully dynamic fault model is governed by the rate-and-state friction laws and spontaneously resolves all stages of an earthquake cycle and long-term fault slip. Our results show several interesting observations on the role of injection volume and rate: First, the injected volume can advance or delay the next earthquake if no earthquakes are directly triggered during perturbation. Second, if earthquakes are triggered, the number of triggered earthquakes is controlled by the rate at which fluid is injected, while the timings of the triggered earthquakes are controlled by the injected volume. Large triggered earthquakes are usually preceded by smaller precursors. Third, the pore-pressure threshold at which earthquakes are triggered changes depending on the injection parameters. In most cases, it increases with the volume of injected fluid, but in some cases when the injection is slow, it can also depend on the rate of injection. The change with respect to injection rate is not a smooth positive trend, however, as increasing the rate causes aseismic transients to grow stronger and transition into seismic events, thus advancing the triggering time and causing decrease in the threshold pore pressure in the process. Overall, the effects of perturbation do not end as soon as injection stops. Instead, heightened aseismic activities, as well as oscillating earthquake timings and magnitudes occur for multiple seismic cycles after the end of pore-pressure perturbation. We also see large variations in aseismic moment release under different perturbation scenarios and its intricate relationship with the resulted seismicity pattern, which confirms the vital role of aseismic slip in earthquake triggering. Similar to previous studies, we find that energy on the fault is primarily released aseismically. Our results thus far are based on spatially uniform pore-pressure evolution, and we are currently developing models that resemble environments with temporally and spatially heterogeneous pore pressure by coupling the temporal evolution of pore pressure with spatial diffusion. We are also incorporating geologic information of the crustal medium, which will be more fitting for modelling realistic scenarios such as the injection-induced earthquakes in Oklahoma.

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)0.002
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.036
GPT teacher head0.264
Teacher spread0.228 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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