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Global Models from Sparse Data: A Robust Estimate of Earth's Residual Topography Spectrum
Geochemistry, Geophysics, Geosystems ( IF 3.275 ) Pub Date : 2020-07-31 , DOI: 10.1029/2020gc009240
A. P. Valentine; D. R. Davies

A significant component of Earth’s surface topography is maintained by stresses induced by underlying mantle flow. This `dynamic' topography cannot be directly observed, but it can be approximated ‐‐‐ particularly at longer wavelengths ‐‐‐ from measurements of residual topography, which are obtained by removing isostatic effects from the observed topography. However, as these measurements are made at discrete, unevenly‐distributed locations on Earth’s surface, inferences about global properties can be challenging. In this paper, we present and apply a new approach to transforming point‐wise measurements into a continuous global representation. The approach, based upon the statistical theory of Gaussian Processes, is markedly more stable than existing approaches ‐‐‐ especially for small datasets. We are therefore able to infer the spatial pattern, wavelength and amplitude of residual topography using only the highest‐quality oceanic spot measurements within the database of Hoggard et al. (2017). Our results indicate that the associated spherical harmonic power spectrum peaks at l=2, with power likely in the range 0.46‐‐0.76,km2. This decreases by over an order of magnitude to around 0.02,km2 at l=30. Around 85% of the total power is concentrated in degrees 1‐‐3. Our results therefore confirm previous findings: Earth's residual topography expression is principally driven by deep mantle flow, but shallow processes are also crucial in explaining the general form of the power spectrum. Finally, our approach allows us to determine the locations where collection of new data would most impact our knowledge of the spectrum.
更新日期:2020-08-01

 

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