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Empirical approaches for fast robust inversion of seismic moment tensor from surface waves
Author(s) -
Reymond D.,
Crusem R.,
Barriot J. P.
Publication year - 2010
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2009gl041542
Subject(s) - inversion (geology) , weighting , focal mechanism , covariance matrix , iterative method , covariance , outlier , algorithm , inverse problem , geology , computer science , seismology , mathematics , mathematical analysis , statistics , physics , induced seismicity , acoustics , artificial intelligence , tectonics
We present a method of robust inversion (also called IRLS: Iterative Reweighted Least Squares) which is surprisingly insensitive to outlier data points, as it discards automatically the aberrant points, without the necessity for careful human inspection and control of data quality. Different criteria based on residuals and signal‐to‐noise ratio are injected into the covariance matrix (acting like a weighting function), to perform the robust inversion using the iterative generalized discrete inverse method of Tarantola‐Valette. From a practical point of view, this algorithm is used as the Preliminary Determination of Focal Mechanism (PDFM) method, which is a project for rapid estimation of source parameters of strong earthquakes in the context of tsunami warning. The input data to be inverted are spectra of long period surface waves, and as an output, the computed result is the seismic moment tensor, from which focal geometry of an earthquake, and principal stress axes are obtained. This method can be applied to any other method of non‐linear (iterative) inversions confronted to the problem of outlier points polluting the data sets.