Directional illumination technique of electromagnetic vibrator system

Tao, Jiang; Cheng, Defu; Wang, Jun; Liu, Chang

doi:10.1109/wcica.2010.5554204

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…Consequently, a GARCH model is fit to the residuals. Through a systematic analysis, we identify GARCH(1, 1) as the most suitable model, as reported in (4). Both the residuals and squared residuals of the estimated GARCH model exhibit white noise characteristics as indicated by Fig.…”

Section: (C) and 5(d))mentioning

confidence: 91%

“…A majority of the seismic applications, especially modelbased detection approaches, assume that seismic noise is sta- VOLUME 4, 2016 tionary or locally stationary, linear and is driven by Gaussian distributed shock waves [2], [3], [4], [5]. Contrary to these assumptions, seismic noise may possess different characteristics that vary with geographical location and time of the day and year, depending on various natural and human-induced factors.…”

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confidence: 99%

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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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Section: (C) and 5(d))mentioning

confidence: 91%

mentioning

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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“…A majority of the seismic applications assume that seismic noise possesses certain properties such as stationary or locally stationary and that it is linear and driven by Gaussian distributed shock waves ( [2], [3], [4], [5]). Contrary to these assumptions, seismic noise may possess different characteristics that vary with space and time depending on various natural and human-induced factors.…”

Section: /21mentioning

confidence: 99%

Statistical characterization and time-series modeling of seismic noise

Aggarwal,

Mukhopadhyay,

Tangirala

2020

Preprint

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Developing statistical models for seismic noise is an exercise of high value in seismic data analysis since these models play a critical role in detecting the onset of seismic events. 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. The objectives of this work are (i) to critically study these long-held assumptions and (ii) to propose a systematic procedure for developing appropriate time-series models. A rigorous statistical analysis reveals 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. Resting on these novel discoveries, ARIMA-GARCH models are developed for seismic noise. Studies are carried out on 185 real-time data sets over different time intervals to study the daily and seasonal variations in noise characteristics and model structure. Nearly all datasets tested positive for first-order non-stationarity, heteroskedasticity, and Gaussianity, while 19% tested negative for linearity. Analysis of the structural uniformity of the developed models with respect to daily and seasonal variations is also presented.

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“…The distribution of the random noise is almost stationary in the time domain. The characteristics of the random noise are assumed generally to be Gaussianity, stationarity, ergodicity and others in the reduction noise process (Broadhead, and Saudi Aramco, Dhahran, 2008;Jiang et al, 2010;Tang and Ma, 2011;Wang et al, 2009). The differences in noise directions in different frequency bands are analyzed, and phase velocity dispersion curves from both beamforming and noise cross-correlations are measured, as well as group velocity from the latter.…”

Section: Introductionmentioning

confidence: 99%