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

2018 ◽  
Vol 23 (2) ◽  
pp. 223-233
Author(s):  
Jianping Liao ◽  
Hexiu Liu ◽  
Weibo Li ◽  
Zhenwei Guo ◽  
Lixin Wang ◽  
...  
Keyword(s):  
Field Data ◽  
Synthetic Data ◽  
High Density ◽  
Western China ◽  
Seismic Survey ◽  
Apparent Velocity ◽  
Coherent Noise ◽  
Space Domain ◽  
Filtering Method ◽  
Time Space

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.

Post-stack depth migration in the frequency–space domain

1986 ◽  
Vol 23 (6) ◽  
pp. 839-848 ◽  
Author(s):  
Panos G. Kelamis ◽  
Einar Kjartansson ◽  
E George Marlin
Keyword(s):  
Synthetic Data ◽  
Real Data ◽  
Wave Number ◽  
Lateral Velocity ◽  
Space Domain ◽  
Frequency Wave ◽  
Depth Migration ◽  
Frequency Space ◽  
Time Space ◽  
Z Domain

The 45 °monochromatic one-way wave equation, along with the thin-lens term, is used, and a depth-migration algorithm is developed in the frequency–space (ω, x) domain. Using this approach, an unmigrated stack section is directly transformed into a depth-migrated section taking into account both vertical and lateral velocity variations. In practice, the algorithm can accommodate steep events with dips of the order of 60–65°. The use of the frequency–space domain offers several advantages over the conventional time–space and frequency–wave-number domains. Time derivatives are evaluated exactly by a simple multiplication, while the use of the space (x, z) domain facilitates the handling of lateral velocity inhomogeneities. The performance of the depth-migration algorithm is tested with synthetic data from complicated models and real data from the Foothills area of western Canada.

scholarly journals Convolutional sparse coding for noise attenuation in seismic data

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. V23-V30
Author(s):  
Zhaolun Liu ◽  
Kai Lu
Keyword(s):  
Sparse Coding ◽  
Seismic Data ◽  
Field Data ◽  
Synthetic Data ◽  
Basis Functions ◽  
High Signal ◽  
Noise Attenuation ◽  
Coherent Noise ◽  
Denoising Method ◽  
Data Set

We have developed convolutional sparse coding (CSC) to attenuate noise in seismic data. CSC gives a data-driven set of basis functions whose coefficients form a sparse distribution. The noise attenuation method by CSC can be divided into the training and denoising phases. Seismic data with a relatively high signal-to-noise ratio are chosen for training to get the learned basis functions. Then, we use all (or a subset) of the basis functions to attenuate the random or coherent noise in the seismic data. Numerical experiments on synthetic data show that CSC can learn a set of shifted invariant filters, which can reduce the redundancy of learned filters in the traditional sparse-coding denoising method. CSC achieves good denoising performance when training with the noisy data and better performance when training on a similar but noiseless data set. The numerical results from the field data test indicate that CSC can effectively suppress seismic noise in complex field data. By excluding filters with coherent noise features, our method can further attenuate coherent noise and separate ground roll.

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

2018 ◽  
Vol 23 (3) ◽  
pp. 369-376
Author(s):  
Jianping Liao ◽  
Songyuan Fu ◽  
Yungui Xu ◽  
Weibo Li ◽  
Jianxiong Chen ◽  
...  
Keyword(s):  
Field Data ◽  
High Density ◽  
Western China ◽  
Seismic Exploration ◽  
Single Shot ◽  
Ground Roll ◽  
Minimum Dataset ◽  
Synthetic Models ◽  
Better Than

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.

High-density point-receiver seismic survey of a deep carbonate cave reservoir system: a case study from the Tarim Basin, Western China

First Break ◽  
2015 ◽  
Vol 33 (10) ◽  
Author(s):  
Xianghao Liang ◽  
Yi Zhou ◽  
Gengxin Peng ◽  
Wensheng Duan ◽  
Duoming Zheng ◽  
...  
Keyword(s):  
Tarim Basin ◽  
High Density ◽  
Western China ◽  
Seismic Survey ◽  
Density Point ◽  
Reservoir System ◽  
System A

Deep-learning-based seismic data interpolation: A preliminary result

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. V11-V20 ◽  
Author(s):  
Benfeng Wang ◽  
Ning Zhang ◽  
Wenkai Lu ◽  
Jialin Wang
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Field Data ◽  
Training Data ◽  
Low Rank ◽  
Seismic Survey ◽  
Data Interpolation ◽  
Spatial Direction ◽  
Time Space ◽  
Dense Data

Seismic data interpolation is a longstanding issue. Most current methods are only suitable for randomly missing cases. To deal with regularly missing cases, an antialiasing strategy should be included. However, seismic survey design using a random distribution of shots and receivers is always operationally challenging and impractical. We have used deep-learning-based approaches for seismic data antialiasing interpolation, which could extract deeper features of the training data in a nonlinear way by self-learning. It can also avoid linear events, sparsity, and low-rank assumptions of the traditional interpolation methods. Based on convolutional neural networks, eight-layers residual learning networks (ResNets) with a better back-propagation property for deep layers is designed for interpolation. Detailed training analysis is also performed. A set of simulated data is used to train the designed ResNets. The performance is assessed with several synthetic and field data. Numerical examples indicate that the trained ResNets can help to reconstruct regularly missing traces with high accuracy. The interpolated results in the time-space domain and the frequency-wavenumber ([Formula: see text]-[Formula: see text]) domain demonstrate the validity of the trained ResNets. Even though the accuracy decreases with the increase of the feature difference between the test and training data, the proposed method can still provide reasonable interpolation results. Finally, the trained ResNets is used to reconstruct dense data with halved trace intervals for synthetic and field data. The reconstructed dense data are more continuous along the spatial direction, and the spatial aliasing effects disappear in the [Formula: see text]-[Formula: see text] domain. The reconstructed dense data have the potential to improve the accuracy of subsequent seismic data processing and inversion.

Dukhan 3D: An Ultra High Density, Full Wide Azimuth Seismic Survey for the Future

2009 ◽  
Author(s):  
Salva R. Seeni ◽  
Scott Robinson ◽  
Michel Denis ◽  
Patrick Sauzedde
Keyword(s):  
High Density ◽  
Seismic Survey ◽  
The Future

scholarly journals Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach

2010 ◽  
Vol 14 (3) ◽  
pp. 545-556 ◽  
Author(s):  
J. Rings ◽  
J. A. Huisman ◽  
H. Vereecken
Keyword(s):  
Water Content ◽  
Particle Filtering ◽  
Field Data ◽  
Synthetic Data ◽  
Time Domain Reflectometry ◽  
Second Step ◽  
Parameter Estimates ◽  
Posterior Distributions ◽  
Data Set ◽  
Resistivity Tomography

Abstract. Coupled hydrogeophysical methods infer hydrological and petrophysical parameters directly from geophysical measurements. Widespread methods do not explicitly recognize uncertainty in parameter estimates. Therefore, we apply a sequential Bayesian framework that provides updates of state, parameters and their uncertainty whenever measurements become available. We have coupled a hydrological and an electrical resistivity tomography (ERT) forward code in a particle filtering framework. First, we analyze a synthetic data set of lysimeter infiltration monitored with ERT. In a second step, we apply the approach to field data measured during an infiltration event on a full-scale dike model. For the synthetic data, the water content distribution and the hydraulic conductivity are accurately estimated after a few time steps. For the field data, hydraulic parameters are successfully estimated from water content measurements made with spatial time domain reflectometry and ERT, and the development of their posterior distributions is shown.

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