  In recent years more and more long-term broadband data sets are collected in
geosciences. Therefore there is an urgent need of algorithms which
semi-automatically analyse and decompose these data into separate periods which
are associated with different processes. Often Fourier and Wavelet Transform is
used to decompose the data into short and long period effects but these fail
often because of their simplicity. In this paper we investigate the novel
approaches Empircial Mode Decomposition and Sparse Decomposition for long-term
sea floor pressure data analysis and compare them with the classical ones. Our
results indicate that none of the methods fulfils all the requirements but
Sparse Decomposition performed best except for computing efficiency.
