and the scale and sparsity of seismic heterogeneity

Sergey Voronin

Princeton University

Princeton NJ 08544, USA

Université Libre de Bruxelles

1050 Brussels, Belgium

Université de Nice

06560 Sophia Antipolis, France

Princeton University

Princeton NJ 08544, USA

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We propose a class of spherical wavelet bases for the analysis of geophysical models and for the tomographic inversion of global seismic data. Its multiresolution character allows for modeling with an effective spatial resolution that varies with position within the Earth. Our procedure is numerically efficient and can be implemented with parallel computing. We discuss two possible types of discrete wavelet transforms in the angular dimension of the cubed sphere. We discuss benefits and drawbacks of these constructions and apply them to analyze the information present in two published seismic wavespeed models of the mantle, for the statistics and power of wavelet coefficients across scales. Localization and sparsity properties allow finding a sparse solution to the inverse problem by iterative minimization of a combination of the

l2 norm of data fit and thelnorm on the wavelet coefficients. By validation with realistic synthetic experiments we illustrate the likely gains of using our new approach in future inversions of finite-frequency seismic data.

- Figure 01
Geometry of the cubed sphere in a three-dimensional view

- Figure 02
Geometry of the cubed sphere in a two-dimensional view, and nomenclature

- Figure 03
The looks of the D4 wavelets as they are being used on the cubed sphere - Figure 04
The looks of the CDF4.2 wavelets as they are being used on the cubed sphere - Figure 05
Preconditioned interval wavelet transforms on the faces of the cubed sphere: thresholding, compression, and coefficient statistics - Figure 06A
Analysis of wavelet sparsity in the seismic model of Montelli using preconditioned D4 wavelets on the cubed sphere - Figure 06B
Analysis of wavelet sparsity in the seismic model of Ritsema using preconditioned D4 wavelets on the cubed sphere - Figure 07A
Analysis of the scale and sparsity in the seismic model of Montelli using preconditioned D4 wavelets on the cubed sphere - Figure 07B
Analysis of the scale and sparsity in the seismic model of Ritsema using preconditioned D4 wavelets on the cubed sphere - Figure 08
Scale lengths of seismic heterogeneity in the seismic models of Montelli and Ritsema, broken down per chunk - Figure 09
Correlation between velocity anomalies in the seismic models of Montelli and Ritsema - Figure 12
Performance of the algorithm on a semi-realistic synthetic test

Frederik Simons Last modified: Sat Aug 29 15:28:45 EDT 2020