Multiple attenuation using an apex-shifted Radon transform

Hargreaves, Neil; Wombell, R.; West, R. Ver

doi:10.3997/2214-4609.201405721

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…This may be explained by the observation that due to the aliased and non-hyperbolic nature of the multiple arrivals, the residuals and artefacts left by Radon processes tend to be high amplitude samples at near offsets. The application of both a non-standard parameterisation of parabolic Radon demultiple and a shiftedapex Radon demultiple algorithm (Hargreaves et al, 2003) on this dataset showed improvements over the standard highresolution Radon demultiple result, however, while improving the multiple attenuation both processes also increased the level of primary attenuation. It was therefore decided to only apply 2-D SRME and NS3D.…”

Section: Synthetic Examplementioning

confidence: 85%

Attenuation of multiple diffractions using a cascaded noise removal sequence

Brittan¹,

Wrench²

2004

SEG Technical Program Expanded Abstracts 2004

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Section: Synthetic Examplementioning

confidence: 85%

Attenuation of multiple diffractions using a cascaded noise removal sequence

Brittan¹,

Wrench²

2004

SEG Technical Program Expanded Abstracts 2004

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“…If more phases are needed to be picked, we can just add neural units in the classification layer and label recordings with the same strategies. Meanwhile, we post-processed GF features by applying post-processing technique to measure the GF flatness through a recursive scheme according to (7) and (8). Then GF is used for training the neural network to pick different phases automatically.…”

Section: Methodsmentioning

confidence: 99%

Automatic Phase Picking From Microseismic Recordings Using Feature Extraction and Neural Network

Jiang

Zheng

2020

IEEE Access

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High-accuracy microseismic phase picking is fundamental to microseismic signal processing. Phase picking methods based on deep learning show great potential dealing with low signal to noise ratio (SNR) data but need enormous training data. However, it's not easy to obtain a big size of field datasets and label them manually to train the neural network. In this paper, a novel method is proposed by applying feature extraction (Gammatone Feature) and neural networks to pick phases automatically. The feature extraction scheme aims to train the neural network using a relatively small size of training datasets. To test the performances of the proposed method, synthetic datasets were obtained by numerical simulation and used to train the neural network. Both synthetic datasets and field datasets were used to test the neural network for picking P-and S-phases of microseismic events. The results of phase picking illustrate that: (1) feature extraction scheme in the training stage can help reduce the size of training datasets; (2) The neural network can be trained well just by synthetic data and phase picking results are accurate and satisfying when the method was tested by both synthetic and field datasets. INDEX TERMS Feature extraction, Gammatone feature, neural networks, phase picking.

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“…Another popular class of demultiple techniques is based on variants of the Radon transform (Foster and Mosher, 1992). Several revised Radon transforms have been proposed for multiple attenuation (Hunt et al, 1996;Zhou and Greenhalgh, 1996;Hargreave et al, 2003;Wang, 2003a). Radontransform-based methods often fail to provide accurate separation because of their nonsparsity in characterizing seismic data, although they can be improved by high-resolution methods (Sacchi and Ulrych, 1995;Herrmann et al, 2000;Trad et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Signal and noise separation in prestack seismic data using velocity-dependent seislet transform

2015

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The seislet transform is a waveletlike transform that analyzes seismic data by following varying slopes of seismic events across different scales and provides a multiscale orthogonal basis for seismic data. It generalizes the discrete wavelet transform (DWT) in the sense that the DWT in the lateral direction is simply the seislet transform with a zero slope. Our earlier work used plane-wave destruction (PWD) to estimate smoothly varying slopes. However, the PWD operator can be sensitive to strong noise interference, which makes the seislet transform based on PWD (PWD-seislet transform) occasionally fail in providing a sparse multiscale representation for seismic field data. We adopted a new velocity-dependent (VD) formulation of the seislet transform, in which the normal moveout equation served as a bridge between local slope patterns and conventional moveout parameters in the common-midpoint domain. The VD slope has better resistance to strong random noise, which indicated the potential of VD seislets for random noise attenuation under 1D earth assumption. Different slope patterns for primaries and multiples further enabled a VD-seislet frame to separate primaries from multiples when the velocity models of primaries and multiples were well disjoint. We evaluated the results by applying the method to synthetic and field-data examples in which the VD-seislet transform helped in eliminating strong random noise. We performed synthetic and field-data tests that showed the effectiveness of the VD-seislet frame for separation of primaries and peg-leg multiples of different orders.

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Multiple attenuation using an apex-shifted Radon transform

Cited by 8 publications

References 8 publications

Attenuation of multiple diffractions using a cascaded noise removal sequence

Attenuation of multiple diffractions using a cascaded noise removal sequence

Automatic Phase Picking From Microseismic Recordings Using Feature Extraction and Neural Network

Signal and noise separation in prestack seismic data using velocity-dependent seislet transform

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