Using ICA for analysis of seismic events

Independent Component Analysis (ICA) is a statistical and computational technique for
revealing hidden factors that underlie set of random variables, measurements, or signals. ICA is a
general purpose technique which is used to linearly transform the observed random data into
components. The ICA can be estimated by using the concept of maximum nonGaussianity,
maximum
likelihood estimation, or minimisation of mutual information. This paper applies ICA to seismic
acceleration time histories in order to locate any hidden components of ground rotational motion or
tilts. Normally the three components of seismically induced rotations are not recorded in most of the
available seismic instruments, primarily because previous devices did not provide the required
sensitivity to observe rotations in a wide frequency band and distance range (the two horizontal
components, equal to tilt at the free surface, are generally recorded at low frequencies) Igel et al 2003.
From the x, y and z components usually recorded the Extended Generalised Lambda Distributions
(EGLD) – ICA model was used to examine whether rotational or tilt trends were embedded within the
3 components. The algorithm tries to fit a matrix from the data which will separate any other trends
within the available components. The results show that the EGLDICA
separates trends within the 3
components; however these are not yet identified as tilts or rotations.

Citation:
Thiyagu, R. and Chanerley, A. (2009) ‘Using ICA for analysis of seismic events’ Proceedings of Advances in Computing and Technology, (AC&T) The School of Computing and Technology 4th Annual Conference, University of East London, pp.160-166.