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Record W4408483878 · doi:10.5194/egusphere-egu25-20653

The turbulence of solids: a multifractal plate tectonic model with Guttenberg-Richter plate “quakes” 

2025· preprint· en· W4408483878 on OpenAlexaff
S. Lovejoy, Andrej Spiridonov, Lauras Balakauskas

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsMultifractal systemTurbulenceGeologyPhysicsMathematicsMechanicsFractalMathematical analysis

Abstract

fetched live from OpenAlex

Over thirty years ago, Y. Kagan speculated that seismicity could fruitfully be considered as “the turbulence of solids”. Indeed, fluid turbulence and seismicity have many common features: they are both highly nonlinear with huge numbers of degrees of freedom. Beyond that, Kagan recognized that they are both riddled with scaling laws in space and in time as well as displaying power law extreme variability and – we could add – multifractal statistics.Kagan was referring to seismicity as usually conceived, as a sudden rupture process occurring over very short time periods. We argue that even at one million year time scales, that the movement of tectonic plates is “quake-like” and is quantitatively close to seismicity, in spite of being caused by relatively smooth mantle convection. To demonstrate this, we develop a multifractal model grounded in convection theory and the analysis of the GPlates data-base of 1000 point trajectories over the last 200 Myrs. We analyzed the statistics of the dynamically important vector velocity differences where Dr is the great circle distance between two points and Dt is the corresponding time lag. The longitudinal and transverse velocity components were analysed separately. The longitudinal scaling of the mean longitudinal difference follows the scaling law ≈ ΔrH with empirical H close to the mantle convection theory value H = 1. This high value implies that mean fluctuations vary smoothly with distance. Yet at the same time, the intermittency exponent C1 is extremely high (C1 ≈ 0.55) implying that from time to time there are enormous “jumps” in velocity: “Plate quakes”. For comparison, laminar (nonturbulent) flow has H = 1 but is not intermittent (C1 = 0), whereas fully developed isotropic fluid turbulence has the (less smooth) value H = 1/3 (Kolmolgorov) but with non-negligible intermittency C1 ≈ 0.07 and seismicity has very large C1 ≈ 1.3. Our study thus quantitatively shows how smooth fluid-like behaviour for the longitudinal velocity component can co-exist with highly intermittent quake-like behaviour.Whereas the longitudinal component is well modelled by (highly intermittent) convection, the transverse velocity is well modelled by Brownian motion. In the temporal domain both components (including their strong correlations) display such diffusion behaviour (i.e. with classical exponent H = ½), but are highly intermittent (C1time = C1space/2 ≈ 0.27). Finally, the extreme velocity differences (that appear as occasional spikes in the velocities) have power law probability tails; the “Guttenberg-Richter” exponents in the seismology literature.The advection - diffusion model is based on an underlying multifractal space-time cascade process. Using mantle convection theory, we show how the driving multifractal flux (ψ) is related to vertical heat fluxes, expansion coefficients, densities, viscosities and specific heats. Taking typical values predict driving fluxes very close to the observed mean ≈ 1/(400 Myrs). Trace moment analysis shows that the outer space-time scales of the cascade process are ≈17000 km in space and ≈ 50Myrs in time. Whereas the former corresponds to half the Earth’s circumference, the latter is the typical time required for a plate to randomly “walk” the same distance.

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)0.001
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 score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.246
Teacher spread0.201 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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