Further improvement of temporal resolution of seismic data by autoregressive (AR) spectral extrapolation

Karsli, H.

doi:10.1016/j.jappgeo.2005.11.001

Cited by 19 publications

(11 citation statements)

References 24 publications

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“…They also modified the AR approach of Lines and Clayton (1977) by using the negative frequency band to fill the gap in the frequency band centred at zero frequency. In recent examples, Karsli (2006) combined Wiener filtering and the AR extrapolation approach of Honarvar et al (2004) for improving temporal resolution of reflection seismic data. Ulrych and Sacchi (2005) described a number of informationbased approaches for inversion as well as extrapolation of geophysical data.…”

Section: Autoregressive Methodsmentioning

confidence: 99%

Frequency extrapolation to enhance the deconvolution of transmitted seismic waves

Dasgupta

Nowack

2008

J. Geophys. Eng.

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We investigate the enhanced deconvolution of transmitted seismic waves from distant natural sources using autoregressive extrapolation (AR) and extended time-domain deconvolution. The amplitude spectrum of deconvolved seismograms is often restricted to a reduced frequency range from the use of a water table for the deconvolution. The attenuation effects on the teleseismic seismic waves also reduce the frequency content of the data. We compare the deconvolved spectra obtained from an AR-extended deconvolution (EARD) and an extended time-domain deconvolution (ETDD) technique for teleseismic waves. For EARD, we analyse the spectral content for the deconvolved spectra to differentiate between the domains of known and unknown spectral values. A prediction error filter is used to perform the autoregressive extrapolation to estimate the unknown spectral values. This procedure is applied on 1D and 2D synthetic data to test the approach. The EARD approach is then compared with the ETDD approach which applies an extended high-pass filter to the time-domain deconvolution approach. Both the EARD and ETDD approaches for extending the effective frequency range of the deconvolution results are then compared using observed teleseismic data recorded in southern India.

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Section: Autoregressive Methodsmentioning

confidence: 99%

Frequency extrapolation to enhance the deconvolution of transmitted seismic waves

Dasgupta

Nowack

2008

J. Geophys. Eng.

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“…However, when the inspected material is not homogeneous, conventional ultrasonic imaging techniques are not effective, and we need to employ signal processing techniques to improve the temporal resolution of the ultrasonic signal [22]. Some studies suggest to use a combination of Wiener filtering and autoregressive (AR) spectral extrapolation to improve signal-to-noise ratio (SNR) and temporal (axial) resolution of the ultrasonic inspection [23][24][25]. They use Wiener filter for deconvolution, then, using a part of the deconvolved spectrum with high SNR, an AR model of the process is built.…”

Section: Introductionmentioning

confidence: 99%

Improving Depth Resolution of Ultrasonic Phased Array Imaging to Inspect Aerospace Composite Structures

Mohammadkhani

Fragonara

et al. 2020

Sensors

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In this paper, we present challenges and achievements in development and use of a compact ultrasonic Phased Array (PA) module with signal processing and imaging technology for autonomous non-destructive evaluation of composite aerospace structures. We analyse two different sets of ultrasonic scan data, acquired from 5 MHz and 10 MHz PA transducers. Although higher frequency transducers promise higher axial (depth) resolution in PA imaging, we face several signal processing challenges to detect defects in composite specimens at 10 MHz. One of the challenges is the presence of multiple echoes at the boundary of the composite layers called structural noise. Here, we propose a wavelet transform-based algorithm that is able to detect and characterize defects (depth, size, and shape in 3D plots). This algorithm uses a smart thresholding technique based on the extracted statistical mean and standard deviation of the structural noise. Finally, we use the proposed algorithm to detect and characterize defects in a standard calibration specimen and validate the results by comparing to the designed depth information.

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“…Although deconvolution techniques extend the frequency band of the seismic data, they cannot completely recover lower and higher frequencies outside the spectral band. Thus, the classical deconvolution applications yield results that are still limited in bandwidth (Walker and Ulrych 1983; Cooke and Schneider 1983; Karslı 2006). Single trace deconvolution techniques also tend to boost noise, as they try to balance total amplitude values without regard to whether it is signal or noise.…”

Section: Introductionmentioning

confidence: 99%

An application of the autoregressive extrapolation technique to enhance deconvolution results: a 2D marine data example

Karsli

2010

Geophysical Prospecting

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A B S T R A C TInterpreting a post-stack seismic section is difficult due to the band-limited nature of the seismic data even post deconvolution. Deconvolution is a process that is universally applied to extend the bandwidth of seismic data. However, deconvolution falls short of this task as low and high frequencies of the deconvolved data are either still missing or contaminated by noise. In this paper we use the autoregressive extrapolation technique to recover these missing frequencies, using the high signal-to-noise ratio (S/N) portions of the spectrum of deconvolved data.I introduce here an algorithm to extend the bandwidth of deconvolved data. This is achieved via an autoregressive extrapolation technique, which has been widely used to replace missing or corrupted samples of data in signal processing. This method is performed in the spectral domain. The spectral band to be extrapolated using autoregressive prediction filters is first selected from the part of the spectrum that has a high signal-to-noise ratio (S/N) and is then extended. As there can be more than one zone of good S/N in the spectrum, the results of prediction filter design and extrapolation from three different bands are averaged.When the spectrum of deconvolved data is extended in this way, the results show higher vertical resolution to a degree that the final seismic data closely resemble what is considered to be a reflectivity sequence of the layered medium. This helps to obtain acoustic impedance with inversion by stable integration. The results show that autoregressive spectral extrapolation highly increases vertical resolution and improves horizon tracking to determine continuities and faults. This increase in coherence ultimately yields a more interpretable seismic section.

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Further improvement of temporal resolution of seismic data by autoregressive (AR) spectral extrapolation

Cited by 19 publications

References 24 publications

Frequency extrapolation to enhance the deconvolution of transmitted seismic waves

Frequency extrapolation to enhance the deconvolution of transmitted seismic waves

Improving Depth Resolution of Ultrasonic Phased Array Imaging to Inspect Aerospace Composite Structures

An application of the autoregressive extrapolation technique to enhance deconvolution results: a 2D marine data example

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