Study and application analysis of random noise adaptive morphological filter algorithm reconstruction for seismic signals

Abstract We introduce a new detection algorithm with improved local and regional seismic signal recognition. The method is based on the difference between seismic signals and background random noise in terms of fractal dimension D. We compare the new method extensively with standard methods currently in use at the Seismic Network of the Istituto Nazionale di Geofisica. Results from the comparisons show that the new method recognizes seismic phases detected by existing procedures, and in addition, it features a greater sensitivity to smaller signals, without an increase in the number of false alarms. The new method was tested on real continuous data and artificially simulated high-noise conditions and demonstrated a capability to recognize seismic signals in the presence of high noise. The efficiency of the method is due to a radically different approach to the topic, in that the assertion that a signal is fractal implies a relationship between the spectral amplitude of different frequencies. This relationship allows, for the fractal detector, a complete analysis of the entire frequency range under consideration.

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Application of an improved particle filter for random seismic noise suppression

Journal of Geophysics and Engineering ◽

10.1093/jge/gxab064 ◽

2021 ◽

Vol 18 (6) ◽

pp. 943-953

Author(s):

Jingquan Zhang ◽

Dian Wang ◽

Peng Li ◽

Shiyu Liu ◽

Han Yu ◽

...

Keyword(s):

Particle Filter ◽

Seismic Data ◽

Particle Filtering ◽

Seismic Noise ◽

Firefly Algorithm ◽

Field Experiments ◽

Noise Suppression ◽

Random Noise ◽

Seismic Signals ◽

And Performance

Abstract Random noise is inevitable during seismic prospecting. Seismic signals, which are variable in time and space, are damaged by conventional random noise suppression methods, and this limits the accuracy in seismic data imaging. In this paper, an improved particle filtering strategy based on the firefly algorithm is proposed to suppress seismic noise. To address particle degradation problems during the particle filter resampling process, this method introduces a firefly algorithm that moves the particles distributed at the tail of the probability to the high-likelihood area, thereby improving the particle quality and performance of the algorithm. Finally, this method allows the particles to carry adequate seismic information, thereby enhancing the accuracy of the estimation. Synthetic and field experiments indicate that this method can effectively suppress random seismic noise.

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Random Noise Suppression Algorithm for Seismic Signals Based on Principal Component Analysis

Wireless Personal Communications ◽

10.1007/s11277-017-5081-7 ◽

2017 ◽

Vol 102 (2) ◽

pp. 653-665 ◽

Cited By ~ 2

Author(s):

Yuan-Jia Ma ◽

Ming-Yue Zhai

Keyword(s):

Principal Component Analysis ◽

Noise Suppression ◽

Random Noise ◽

Principal Component ◽

Component Analysis ◽

Seismic Signals

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A method for direct measurement of specimen noise in HREM images

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100148125 ◽

1993 ◽

Vol 51 ◽

pp. 458-459

Author(s):

Sidnei Paciornik ◽

Roar Kilaas ◽

Ulrich Dahmen ◽

Michael Adrian O'Keefe

Keyword(s):

Random Noise ◽

Image Interpretation ◽

Thin Foil ◽

High Resolution Electron Microscopy ◽

Black Dot ◽

Atomic Column ◽

Amorphous Oxide ◽

Resolution Electron ◽

Imaging Conditions ◽

Image Simulations

High resolution electron microscopy (HREM) is a primary tool for studying the atomic structure of defects in crystals. However, the quantitative analysis of defect structures is often seriously limited by specimen noise due to contamination or oxide layers on the surfaces of a thin foil.For simple monatomic structures such as fcc or bcc metals observed in directions where the crystal projects into well-separated atomic columns, HREM image interpretation is relatively simple: under weak phase object, Scherzer imaging conditions, each atomic column is imaged as a black dot. Variations in intensity and position of individual image dots can be due to variations in composition or location of atomic columns. Unfortunately, both types of variation may also arise from random noise superimposed on the periodic image due to an amorphous oxide or contamination film on the surfaces of the thin foil. For example, image simulations have shown that a layer of amorphous oxide (random noise) on the surfaces of a thin foil of perfect crystalline Si can lead to significant shifts in image intensities and centroid positions for individual atomic columns.

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