Seismic instruments: article

Seismogram fingerprints as a tool for automatic filtering of low-frequency noise
K.Yu. Silkin
Federal Research Center “Geophysical Survey of the Russian Academy of Sciences”
Journal: Seismic instruments
Tome: 59
Number: 2
Year: 2023
Pages: 26–45
UDK: 550.(34.(012:062):344.2)
DOI: 10.21455/si2023.2-3
Keywords: fingerprints, earthquake, explosion, wavelet transform, continuous wavelet transform, two-dimensional discrete wavelet transform, lineament, fractal, machine learning, recognition
Аnnotation: The article begins with a review of publications on low frequency noise suppression techniques. Denoising seismograms of earthquakes, explosions and other seismic events is the goal of the study. It demonstrates that a branch in the theory and practice of processing seismograms is currently being actively developed, in which their analysis is carried out in a two-dimensional time-frequency plane. Additional add-ons of the second and third levels appear in addition to the existing methods, which makes it difficult both to understand the essence of the methods and to interpret their results. In our article, we tried to put things in order in them. As an alternative to numerous additions to the time-frequency analysis, we proposed our own approach. We believe that it will not only make the analysis clearer, but also increase its accuracy. Our method is based on the application of finger-print technology to the results of continuous wavelet transform of a seismogram. In difficult cases, we recom-mend using a more advanced version of it – the method of redundant fingerprints. It provides a convenient opportunity to objectively evaluate the frequency characteristics of all components of the seismogram. Based on the results of the analysis, the automatic information system can select the optimal cutoff frequency for the filter in order to clear the seismogram from low-frequency noise and minimally distort the signal shape. This is especially important if the spectra of both partially overlap, and also if the noise intensity is high. The proposed method can be in demand for automatic classification of seismic events by the nature of their source using machine learning technologies.