Automatic Denoising of Seismograms using Fingerprints: Algorithms, Properties, Limitations

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This article describes the new technique for automatically preparing a noisy seismic record for further analysis using expert information systems. The technique is based on seismogram fingerprints, which, due to their concise but informative pattern, allow the development of a reliable algorithm for finding important noise characteristics. The search for the optimal cutoff frequency for a high-pass filter is especially important under conditions of partial overlap of the signal and noise spectra at a high intensity of the latter. It is precisely this difficult case that this study aims to address. The article analyzes the developed methodology on the example of several hundred registrations of regional earthquakes and explosions. The analysis showed that reliable results can be achieved in more than 90% of cases. In addition to all the problems and limitations of the method, which are an extension of its capabilities, are mentioned. Appendix to the article contains detailed description of the algorithm underlying the method.

作者简介

K. Silkin

Geophysical Survey of the RAS

编辑信件的主要联系方式.
Email: const.silkin@ya.ru
俄罗斯联邦, Lenina ave., 189, Obninsk, 249035

参考

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2. Fig. 1. Distribution over the epicenter distance of the number of analyzed events depending on the type of source.

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3. Fig. 2. The main elements of binary fingerprint technology. a – recording of the BHZ channel of the LVZ station of the earthquake on April 1, 2020 at 04h44m27c: green and turquoise lines are the marks of the entry of P and S waves; b – an energogram according to the recording waveletogram, the time axis is relative, the color scale is the logarithm of the amplitude values, the arrow “S” shows the superimposed amplitude spectrum of the recording, the horizontal axis the values of which are red at the top, the vertical axis corresponds to the axis of the energogram, the arrow with the designation “fB – fN” marks the expert interval of the permissible limits of the background boundary (horizontal blue dotted lines) and points to its middle; c – binary prints of the energogram; d, e – redundant binary prints (d – sliding wavelet spectrum, horizontal the axis is the frequency of the wavelet decomposition, the vertical axis is the position of the scanning frequency window, the color scale of values is the conditional density of prints, the arrows “LP” and “PP” mark the left and right bands for calculating the sections of the sliding screen

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4. Fig. 3. Histogram of the width distribution of the expert frequency range containing the permissible value of the upper noise limit for optimal filtering.

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5. Fig. 4. Histograms of the distribution of the quality function of the results of the binary fingerprint algorithm divided into classes. The symbols contain the types of calculated quality estimates for: PRE–binary prints (simple); IDO SV From the N – right profile of the sliding wavelet spectrum; IDO SV From the L – similarly to the left profile; IDO SV O – profile of the sliding wavelet envelope; IDO General – the final generalization of the results from different sides of the IDO.

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6. Fig. 5. The scattering diagram of the quality values of the results of simple binary prints (UP to) and the final generalized redundant binary prints (IDO).

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7. Fig. 6. Histograms of the distribution of the quality values of the solution results for simple prints (a) and redundant (b) at different ratios of the energy of the useful signal and low-frequency noise (gradations in symbols) divided into quality classes. The quality classes correspond to Fig. 3.

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8. Fig. 7. Illustration of the technique for determining the characteristic spectral parameters of background recording. a is a waveletogram of recording before the entry of the P-wave, the color scale is the amplitude in microns/s; b is the average wavelet spectrum, the vertical axis coincides with the waveletogram (1 is a graph of the spectrum, 2 is an approximating polynomial for the main maximum of the spectrum, 3 is the mark of the 1 percent level, 4 is the frequency of the beginning of attenuation the predominant spectral component of the background).

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9. Figure 8. An example of a profile graph for redundant binary prints. The vertical axis is the conditional density of prints. 1 – the profile graph; 2 – the extremes of the graph that were removed at the stage of eliminating minor features; 3 – the extremes left; 4 – the point of the strongest negative gradient; 5 – the midpoint between the strongest extremes; 6 – the point of the first minimum (of those left) after the strongest gradient.

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