Stochastic analysis of mantle convection experiments using two-point correlation functions

Details Stochastic analysis of mantle convection experiments using two-point correlation functions Stochastic analysis of mantle convection experiments using two-point correlation functions

Feb 1994

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1994georl..21..305p&link_type=abstract

Geophysical Research Letters (ISSN 0094-8276), vol. 21, no. 4, p. 305-308

Physics

Geophysics

10

Boundary Conditions, Convection, Earth Mantle, Geophysics, Geotemperature, Radial Flow, Seismology, Statistical Correlation, Stochastic Processes, Stratification, Tomography, Coordinates, Graphs (Charts), Harmonic Functions, Kinetic Energy, Low Pass Filters, Robustness (Mathematics), Viscosity

Scientific paper

This paper discusses the stochastic analysis of spatially complex, time-dependent flows in spherical and cylindrical geometries where the reference states, internal heating rates, and boundary conditions are temporally invariant and rotationally symmetric. Snapshots of the aspherical temperature anomalies delta T(r, omega, t) from a single convection run are taken to be samples of a stationary, rotationally invariant random field, and the spatial two-point correlation function C(sub TT)(r, r prime, delta) is constructed by averaging over rotational transformations of this ensemble. Three subfunctions are extracted: the rms variation, sigma(sub T)(r) = square root of C(sub TT)(r, r, 0), the radial correlation function, R(sub T)(r, r prime, 0)/(sigma(sub T)(r)sigma(sub T)(r prime), and the angular correlation function A(sub T)(r, delta) = C(sub TT)(r, r, delta)/(sigma(sub T)(exp 2)(r)). All three are useful in assessing the structural differences among mantle convection simulations, but the diagnostic properties of R(sub T) and its robustness with respect to low-pass filtering recommend it as a tool for testing stratification hypotheses against whole-mantle tomographic models.

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