3‐D<i>F‐K</i>filtering

Stewart, Robert R.; Schieck, David G.

doi:10.1190/1.1889857

Cited by 4 publications

(6 citation statements)

References 3 publications

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“…It is found that the 3-D two-pass FKK filtering is a tetrahedral shape, while 3-D one-pass FKK filtering is a cone, so there are some distinct differences between them. Here, we refer the readers to (Stewart, 1989;Peardon and Bacon, 1992).…”

Section: Methodsmentioning

confidence: 99%

“…Currently, 3-D seismic exploration is a popular technique (e.g., Yan et al, 2017), but the 3-D filtering has not developed significantly. The common approach is to first filter data inline and then crossline (two-pass filtering method, Stewart, 1989;Peardon and Bacon, 1992;Li and Larner, 1993) to replace the true 3-D filtering, which is low efficiency because of switching data between the horizontal and vertical directions.…”

Section: Introductionmentioning

confidence: 99%

“…Peardon and Bacon (1992) used a marine seismic section that was a single line from a 3-D survey for examples of 3-D FKK filtering application. In this paper, we extended their research and applied 3-D FKK filtering method (Stewart, 1989) on a 3-D high-density onshore seismic field data from a coal mines in western China, which was typically noisier than marine seismic field data. Both theoretical synthetic models and the 3-D high-density onshore seismic field data numerical filtering experiments demonstrate that the feasibility of our 3-D FKK filtering software.…”

Section: Introductionmentioning

confidence: 99%

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3-D Butterworth Filtering for 3-D High-density Onshore Seismic Field Data

Liao

Liu

et al. 2018

JEEG

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Three-dimensional seismic survey is widely applied, but 3-D filtering technology has yet to be fully utilized for the analysis of seismic field data. The common approach is to first filter inline and then crossline. However, an effective 3-D filtering method is expected to eliminate coherent noise, such as the ground roll. We propose a 3-D Butterworth filtering method in the time-space domain. Firstly, a Butterworth-type filter in the frequency-wavenumber-domain is designed to suppress the linear noise with a specific apparent velocity. Secondly, transforming this filter to the time and space domain yields 3-D partial differential equations (PDEs), which are applied to suppress the linear noise. Factorizing the finite-difference equations in a different direction other than decreasing the 3-D PDEs to 2-D PDEs produces a highly accurate and efficient algorithm. Designing the 3-D Butterworth filter, selecting the filtering parameters, and showing its application to synthetic data and a 3-D high-density onshore seismic field data from a region in western China are discussed in detail. Numerical experiments with 3-D high-density onshore seismic field data demonstrate that it is more effective than the 3-D frequency-wavenumber-wavenumber (FKK) filtering method.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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3-D Butterworth Filtering for 3-D High-density Onshore Seismic Field Data

Liao

Liu

et al. 2018

JEEG

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“…Methods that filter noise based on the coherency (i.e., smoothness) of the signal include local mean (e.g., Claerbout, 1976), median (e.g., Zhu et al, 2004) and nonlocal means (e.g., Bonar & Sacchi, 2012) filtering. On the other hand, methods that try to separate signal and noise in an alternative domain use Fourier (e.g., Stewart & Schieck, 1989;Hashemi et al, 2008), wavelet (e.g., Mousavi & Langston, 2016;Irani Mehr & Abedi, 2017), curvelet (e.g., Neelamani et al, 2008), seislet (e.g., Fomel & Liu, 2010), Radon (e.g., Trad et al, 2003) and other transforms. Recent data-driven methods for noise attenuation include dictionary learning (e.g., Nazari Siahsar et al, 2017;X.…”

Section: Introductionmentioning

confidence: 99%

Semi‐blind‐trace algorithm for self‐supervised attenuation of trace‐wise coherent noise

Abedi,

Pardo,

Alkhalifah

2023

Geophysical Prospecting

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Trace‐wise noise is a type of noise often seen in seismic data, which is characterized by vertical coherency and horizontal incoherency. Using self‐supervised deep learning to attenuate this type of noise, the conventional blind‐trace deep learning trains a network to blindly reconstruct each trace in the data from its surrounding traces; it attenuates isolated trace‐wise noise but causes signal leakage in clean and noisy traces and reconstruction errors next to each noisy trace. To reduce signal leakage and improve denoising, we propose a new loss function and masking procedure in a semi‐blind‐trace deep learning framework. Our hybrid loss function has weighted active zones that cover masked and non‐masked traces. Therefore, the network is not blinded to clean traces during their reconstruction. During training, we dynamically change the masks' characteristics. The goal is to train the network to learn the characteristics of the signal instead of noise. The proposed algorithm enables the designed U‐net to detect and attenuate trace‐wise noise without having prior information about the noise. A new hyperparameter of our method is the relative weight between the masked and non‐masked traces' contribution to the loss function. Numerical experiments show that selecting a small value for this parameter is enough to significantly decrease signal leakage. The proposed algorithm is tested on synthetic and real off‐shore and land datasets with different noises. The results show the superb ability of the method to attenuate trace‐wise noise while preserving other events. An implementation of the proposed algorithm as a Python code is also made available.This article is protected by copyright. All rights reserved

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“…For more information: https://seg.org/Publications/Policies-and-Permissions/Open-Access-Policy Introduction Currently, 3-D seismic exploration is a popular technique (e.g., Yan et al, 2017), but the 3-D filtering has not developed significantly. The common approach is to first filter data inline and then crossline (two-pass filtering method, Stewart, 1989;Peardon and Bacon, 1992;Li and Larner, 1993) to replace the true 3-D filtering, which is low efficiency because of switching data between the horizontal and vertical directions.…”

mentioning

confidence: 99%

Application of 3-D FKK Filtering in 3-D High-density Onshore Seismic Field Data

Liao

et al. 2018

JEEG

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For linear noise such as seismic ground roll, 3-D frequency-wavenumber-wavenumber (3-D FKK) domain filtering suppression is better than 2-D frequency-wavenumber (F-K) domain filtering. In recent years, with the continuous development of computer processing speed and memory capacity, high-density data acquisition in seismic exploration has been widely applied in the hydrocarbon industry, opening up the application of 3-D FKK filtering methods. We applied the 3-D FKK filtering software to a 3-D high-density onshore seismic field dataset from a coal mine in western China. The case study demonstrates that the linearity of the noise in the field data is better represented by constructing the single shot records as a minimum dataset. Both theoretical synthetic models and the 3-D high-density onshore seismic field data numerical filtering experiments demonstrate that the feasibility of 3-D FKK filtering.

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3‐DF‐Kfiltering

Cited by 4 publications

References 3 publications

3-D Butterworth Filtering for 3-D High-density Onshore Seismic Field Data

3-D Butterworth Filtering for 3-D High-density Onshore Seismic Field Data

Semi‐blind‐trace algorithm for self‐supervised attenuation of trace‐wise coherent noise

Application of 3-D FKK Filtering in 3-D High-density Onshore Seismic Field Data

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