Multiscale Analysis of Geophysical Signals Using the 2D Continuous Wavelet Transform

Ouadfeul, Sid‐Ali; Aliouane, Leila; Hamoudi, Mohamed; Boudella, Amar; Eladj, Said

doi:10.5772/35307

Cited by 10 publications

(6 citation statements)

References 21 publications

(18 reference statements)

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“…where ( ) s t is the seismic trace, ( ) r t is the reflectivity, ( ) , R m f represents ( ) r t shifted by m samples related to ( ) s t in the time domain, f is the frequency, S(f) is the Fourier transform of s(t), and stab is a small factor that guarantees the stability of the coherence function. This function calculates the proportion of energy in the seismic traces that can be predicted by the well data [8,10,11]. The maximum of this function corresponds to the best alignment between the well and the seismic data, from which the wavelet is extracted by performing a simple deconvolution in the frequency domain [12].…”

Section: Roy White Wavelet Extraction Methodsmentioning

confidence: 99%

Lithological Characterization by Simultaneous Seismic Inversion in Algerian South Eastern Field

Eladj

Lounissi

Doghmane

et al. 2020

Eng. Technol. Appl. Sci. Res.

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The main goal of this paper is to characterize a reservoir situated in the southeast of Algeria based on AVO seismic inversion. The seismic inversion model has been built by the iterative method of Aki and Richards’s approximation and it has been correlated with four-existing wells in the studied zone. The correlation rate between the inversion model and logging data is good (varying from 72% to 85%). Reservoir characterization of this field has been given in detail. The lithological description is used to construct a Geomechanical model that is useful for new wells’ drilling decisions. The high correlated results allowed us to have a vision on the horizontal variation of Petrophysical parameters such as density and lithological variation of three facies clay, tight limestone, and porous limestone. Moreover, this classification is used in the best way to determine the interesting zone with higher porosity values, so that the exploration strategy becomes more efficient with minimized uncertainties. Therefore, it is highly recommended to use the constructed model to propose new wells as well-5 in this study.

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Section: Roy White Wavelet Extraction Methodsmentioning

confidence: 99%

Lithological Characterization by Simultaneous Seismic Inversion in Algerian South Eastern Field

Eladj

Lounissi

Doghmane

et al. 2020

Eng. Technol. Appl. Sci. Res.

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“…ΔY is the grid dimension in the Y direction. Ouadfeul and Aliouane (2011, 2013 have shown that the CWT method is less sensitive to noise compared to the horizontal and full gradient methods; this is because CWT is a low-pass filter which attenuates highfrequency noise, whereas the horizontal and full gradient techniques are based on derivative operations which amplify high-frequency noise. One can observe that the DCWT process is able to detect with excellent precision the boundaries of each body.…”

Section: The Meter Readermentioning

confidence: 99%

Structural edge delimitation from gravity anomaly data using the directional continuous wavelet transform with an example from the Basin and Range Province of the United States

Ouadfeul

Aliouane

2013

The Leading Edge

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In this article, we demonstrate the use of the 2D directional continuous wavelet transform (DCWT) for structural boundary delimitation. We include an example of Bouguer gravity anomaly data analysis from the Basin and Range Province of the United States. Our approach is based on mapping maxima in the modulus of the directional continuous wavelet transform for various scales. We initially validate our concept using synthetic gravity anomaly fields generated from cylindrical and prismatic model bodies (source geometries). Subsequent application of our concept to real gravity anomaly data shows that the method is versatile and robust.

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“…In seismic data processing the CWT is used for noise attenuation and seismic imaging (Ouadfeul et al, 2011a;Ouadfeul and Aliouane, 2011b). In potential field data analysis, it was used for structural boundaries delimitation and causative sources characterization (Ouadfeul et al, 2011c;Ouadfeul et al, 2012a;2012b). In seismology the wavelet transform is used for earthquakes characterization (Dimri et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Noise Effect Reduction on the 3d GPR Data Analysis Using the 2d Continuous Wavelet Transform

Ouadfeul

Aliouane²

2013

Symposium on the Application of Geophysics to Engineering and Environmental Problems 2013

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This paper is a tentative to reduce the noise effect on the 2D wavelet transform coefficients of the 3D Ground Penetrating Radar (GPR) data. The proposed idea is based on the low pass filtering using a Gaussian low pass filter of the modulus of the 2D Continuous Wavelet Transform (CWT) for small scales. Application to noisy 3D GPR data shows that the proposed technique can greatly reduce the noise effect and improve the information provided by the multiscale by the CWT analysis of GPR data for land topographic obstacles identification from 3D ground penetrating radar data.

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Multiscale Analysis of Geophysical Signals Using the 2D Continuous Wavelet Transform

Cited by 10 publications

References 21 publications

Lithological Characterization by Simultaneous Seismic Inversion in Algerian South Eastern Field

Lithological Characterization by Simultaneous Seismic Inversion in Algerian South Eastern Field

Structural edge delimitation from gravity anomaly data using the directional continuous wavelet transform with an example from the Basin and Range Province of the United States

Noise Effect Reduction on the 3d GPR Data Analysis Using the 2d Continuous Wavelet Transform

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