A study on the Gaussianity and stationarity of the random noise in the seismic exploration

Wang, Dongmei; Li, Yue; Nie, Pengfei

doi:10.1016/j.jappgeo.2014.08.001

Cited by 8 publications

(8 citation statements)

References 25 publications

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“…The magnified part of this spectrogram at the 100-200 s interval of this time series (Fig. (2b)), clearly verifies the existence of non-stationary signals at the 1-4 Hz, but without using Time-Frequency analysis we cannot confidently decide about the length of stationarity in this interval (Wang, 2014;Zhong, 2015). Therefore, before going further, the degree of stationarity of signal should be searched in advance.…”

Section: Resultsmentioning

confidence: 76%

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Constraining on the Stationarity of Signal with Time-Frequency Surrogates to Enhance the Reliability of Singularity Spectrum Attributes of Random Seismic Noise Wavefield

Hamed

Shomali

Moradi

2017

Preprint

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Abstract.Existence of a self-affine long range persistence in the seismic noise time series evidences that the current state of system is not in the pure diffused regime and transition from coherent to incoherent motion is still on progress. Rate of this 15 evolving transition can be indirectly linked to the degree of heterogeneity of medium, thus in this paper we tried to gain an insight into the heterogeneity of the medium by analyzing the width, extreme and asymmetrical trend of multifractal spectrum of seismic records. Nonetheless, toward high frequency ranges a seismic signal itself loses its stationarity short while after its recording onset time. Experimentally, the long-range correlation of a stationary time series (with 0 < ℎ(2) < 1) can be discerned from a non-stationary process (with ℎ(2) > 1) by examining the values of scaling exponent ℎ(2), 20 however, changing in the fractal properties in the crossover time scales in time series don't permit us to ascribe a single amount for ℎ(2) and without executing additional analysis on the stationarity length of signals, direct calculation of such long range correlation and fractal dimensions might be biased. Hence, in this paper we examined the inherent stationarity of a signal relative to the different observation scales in the stochastic contexts before feeding the signal into the cycle of DFA.This method is based on the comparison between global and local features of the original signal and its synthesized time-25 frequency surrogates; therefor it can effectively improve the accuracy of results. Our approach proves the existence of a high-velocity anomalous feature in the right flank of Sahand inactive volcano where it is surrounded by heterogeneous lowvelocity structures and extended to the shallower than ~3 km depth beneath this region at the northwestern of Iran.

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Section: Resultsmentioning

confidence: 76%

“…This interval, in the long period bandwidth (0.1-1 Hz) and high frequency range (1-4 Hz), might be in the order of 1-1.5 h and 3-24 s long, respectively (Gorbatikov and Stepanova, 2008;Wang et al, 2014;Zhong et al, 2015a, b). This characteristic is substantially important.…”

mentioning

confidence: 97%

Constraining on the Stationarity of Signal with Time-Frequency Surrogates to Enhance the Reliability of Singularity Spectrum Attributes of Random Seismic Noise Wavefield

Hamed

Shomali

Moradi

2017

Preprint

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“…Several aspects of ambient noise such as its azimuthal isotropy, e.g., [ 42 ], the energy partition between different vibration modes, e.g., [ 43 ] and the stationarity, e.g., [ 44 , 45 ] have been investigated in previous work. Even though clear daily and weekly patterns are found in seismic noise in populated areas, e.g., [ 46 ], it often appears quasi-stationary during periods of a few hours in time scales of

s according to [ 42 ] for natural illumination.…”

Section: Discussion and Conclusionmentioning

confidence: 99%

Horizontal-to-Vertical Spectral Ratio of Ambient Vibration Obtained with Hilbert–Huang Transform

Neukirch

García‐Jerez

Villaseñor

et al. 2021

Sensors

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The Horizontal-to-Vertical Spectral Ratio (HVSR) of ambient vibration measurements is a common tool to explore near surface shear wave velocity (Vs) structure. HVSR is often applied for earthquake risk assessments and civil engineering projects. Ambient vibration signal originates from the combination of a multitude of natural and man-made sources. Ambient vibration sources can be any ground motion inducing phenomena, e.g., ocean waves, wind, industrial activity or road traffic, where each source does not need to be strictly stationary even during short times. Typically, the Fast Fourier Transform (FFT) is applied to obtain spectral information from the measured time series in order to estimate the HVSR, even though possible non-stationarity may bias the spectra and HVSR estimates. This problem can be alleviated by employing the Hilbert–Huang Transform (HHT) instead of FFT. Comparing 1D inversion results for FFT and HHT-based HVSR estimates from data measured at a well studied, urban, permanent station, we find that HHT-based inversion models may yield a lower data misfit χ2 by up to a factor of 25, a more appropriate Vs model according to available well-log lithology, and higher confidence in the achieved model.

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“…With regards to linearity and Gaussianity, a bi-spectral based method [18], [19] is implemented. Wang et al [15] uses Shapiro-Wilk test [6] along with the surrogate and time-frequency based approach for testing stationarity.…”

Section: Literature Reviewmentioning

confidence: 99%

Rigorous Predictive Noise Modeling Approach for Model-Based Onset Detection and Enhanced Picking of P-Waves in Seismic Signals

2022

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Detecting and picking the onset of P-waves in seismic signals has a fairly rich literature, among which model-based (predictive) approaches hold immense promise. A majority of these models are usually built on certain critical assumptions, namely, stationarity, linearity, and Gaussianity. Despite their criticality, very little reported literature exists on validating these assumptions on real seismic data. Furthermore, the predictive capabilities of these models are not utilized to their best potential in detection. Therefore, there exists a strong need for a rigorous approach to model seismic noise and develop a method that builds on the statistical properties of residuals resulting from the noise models. The objectives of this work are (i) to critically study certain long-held assumptions in seismic noise modeling, (ii) to develop rigorous time-series models for background noise that are commensurate with the noise properties, so as to (iii) devise a residual-based method for detection and enhanced picking of P-waves. An important finding of this work, arising from our study on 185 historical data sets, is that these standard assumptions do not hold for most of the data sets under study; rather, they exhibit additional special features such as heteroskedasticity and integrating effects. Consequent to these novel discoveries, we develop auto-regressive integrated moving average-generalized auto-regressive conditionally heteroskedastic (ARIMA-GARCH) models for seismic noise. The proposed residual-based detector and picker is found to be highly effective with a 90% detection rate while picking 91% of the events with an accuracy of ≤ 0.625 seconds based on tests with 100 historical data sets. Further, when the noise model is used in combination with the existing AIC-based pickers, the number of events picked with an accuracy of ≤ 0.625 seconds is 50% more than the existing AR-AIC picker. Therefore, the proposed method can be considered highly competitive and effective in P-wave detection and picking.

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A study on the Gaussianity and stationarity of the random noise in the seismic exploration

Cited by 8 publications

References 25 publications

Constraining on the Stationarity of Signal with Time-Frequency Surrogates to Enhance the Reliability of Singularity Spectrum Attributes of Random Seismic Noise Wavefield

Constraining on the Stationarity of Signal with Time-Frequency Surrogates to Enhance the Reliability of Singularity Spectrum Attributes of Random Seismic Noise Wavefield

Horizontal-to-Vertical Spectral Ratio of Ambient Vibration Obtained with Hilbert–Huang Transform

Rigorous Predictive Noise Modeling Approach for Model-Based Onset Detection and Enhanced Picking of P-Waves in Seismic Signals

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