Preconditioning seismic data for channel detection

2015 ◽  
Vol 3 (1) ◽  
pp. T1-T4 ◽  
Author(s):  
Saleh Al-Dossary
Keyword(s):  
Seismic Data ◽  
Seismic Noise ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Small Scale ◽  
High Signal ◽  
Polynomial Fitting ◽  
Spatial Filters ◽  
Channel Detection ◽  
Channel Patterns

Random seismic noise, present in all 3D seismic data sets, hampers manual interpretation by geoscientists and automatic analysis by a computer program. As a result, many noise-suppression techniques have been developed to enhance image quality. Accurately suppressing seismic noise without damaging image details is crucial in preserving small-scale geologic features for channel detection. The automatic detection of channel patterns theoretically should be easy because of their unique spatial signatures and scales, which differentiate them from other common 3D geobodies. For example, one notable channel characteristic has high local linearity: Spatial coherency is much greater in one direction than in other directions. A variety of techniques, such as spatial filters, can be used to enhance this “slender” channel feature in areas of high signal-to-noise ratio (S/N). Unfortunately, these spatial filters may also reduce the edge detectability in areas of low S/N. In this paper, I compared the effectiveness of three noise reduction filters: (1) running average, (2) redundant wavelet transform (RWT), and (3) polynomial fitting. I demonstrated the usefulness of these filters prior to edge detection to enhance channel patterns in seismic data collected from Saudi Arabia. The data examples demonstrated that RWT and polynomial fitting can successfully preserve, enhance, and delineate channel edges that were not visible in conventional seismic amplitude displays, whereas the running average filter further smeared the detectability of channel edges.

Assessment of GRACE-FO Laser Ranging Interferometer measurements

2021 ◽  
Author(s):  
Athina Peidou ◽  
Felix Landerer ◽  
David Wiese ◽  
Matthias Ellmer ◽  
Eugene Fahnestock ◽  
...  
Keyword(s):  
High Frequency ◽  
Signal To Noise Ratio ◽  
Small Scale ◽  
Laser Ranging ◽  
High Signal ◽  
Measurement Sensitivity ◽  
One Year ◽  
Mass Transfers ◽  
Gravity Recovery ◽  
System Errors

<p>The performance of Gravity Recovery and Climate Experiment Follow‐On (GRACE-FO) laser ranging interferometer (LRI) system is assessed in both space and frequency domains. With LRI’s measurement sensitivity being as small as 0.05 nm/s<sup>2</sup> at GRACE-FO altitude we perform a thorough assessment on the ability of the instrument to detect real small-scale high-frequency gravity signals. Analysis of range acceleration measurements along the orbit for nearly one year of daily solutions suggests that LRI can detect signals induced by mass perturbation up to 26 mHz, i.e., ~145 km spatial resolution. Additionally, high frequency signals that are not adequately modeled by dealiasing models are clearly detected and their magnitude is shown to reach 2-3 nm/s<sup>2</sup>. The alternative K‐band microwave ranging system (KBR) is also examined and results demonstrate the inability of KBR to retrieve signals above 15mHz (i.e., shorter than ~200 km) as the noise of the KBR range acceleration increases rapidly. Overall, the first stream of LRI measurements shows that the high signal to noise ratio allows for detection of mass transfers in finer scales, however the ability to fully exploit the high-quality signal measured by the LRI in Level 2 products is still constrained by noise of background models and other onboard instrumentation and measurement system errors.</p><p>Copyright Acknowledgment: This work was performed at the California Institute of Technology's Jet Propulsion Laboratory under a contract with the National Aeronautics and Space Administration's Cryosphere Science Program.</p>

Triangle Ranch Headquarters field development using shallow core holes and high‐resolution seismic data

Geophysics ◽  
1989 ◽  
Vol 54 (11) ◽  
pp. 1384-1396
Author(s):  
Howard Renick ◽  
R. D. Gunn
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Processing Parameters ◽  
Mapping Method ◽  
High Signal ◽  
Signal To Noise ◽  
Near Surface ◽  
Field Development ◽  
Reef Structure ◽  
Noise Ratio

The Triangle Ranch Headquarters Canyon Reef field is long and narrow and in an area where near‐surface evaporites and associated collapse features degrade seismic data quality and interpretational reliability. Below this disturbed section, the structure of rocks is similar to the deeper Canyon Reef structure. The shallow structure exhibits very gentle relief and can be mapped by drilling shallow holes on a broad grid. The shallow structural interpretation provides a valuable reference datum for mapping, as well as providing a basis for planning a seismic program. By computing an isopach between the variable seismic datum and the Canyon Reef reflection and subtracting the isopach map from the datum map, we map Canyon Reef structure. The datum map is extrapolated from the shallow core holes. In the area, near‐surface complexities produce seismic noise and severe static variations. The crux of the exploration problem is to balance seismic signal‐to‐noise ratio and geologic resolution. Adequate geologic resolution is impossible without understanding the exploration target. As we understood the target better, we modified our seismic acquisition parameters. Studying examples of data with high signal‐to‐noise ratio and poor resolution and examples of better defined structure on apparently noisier data led us to design an acquisition program for resolution and to reduce noise with arithmetic processes that do not reduce structural resolution. Combining acquisition and processing parameters for optimum structural resolution with the isopach mapping method has improved wildcat success from about 1 in 20 to better than 1 in 2. It has also enabled an 80 percent development drilling success ratio as opposed to slightly over 50 percent in all previous drilling.

A denoising framework for microseismic and reflection seismic data based on block matching

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. V283-V292 ◽  
Author(s):  
Chao Zhang ◽  
Mirko van der Baan
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Block Matching ◽  
The Novel ◽  
Noise Levels ◽  
Reflection Seismic ◽  
3D Data ◽  
2D Data ◽  
Incoherent Noise

Microseismic and seismic data with a low signal-to-noise ratio affect the accuracy and reliability of processing results and their subsequent interpretation. Thus, denoising is of great importance. We have developed an effective denoising framework for surface (micro)-seismic data using block matching. The novel idea of the proposed framework is to enhance coherent features by grouping similar 2D data blocks into 3D data arrays. The high similarities in the 3D data arrays benefit any filtering strategy suitable for multidimensional noise suppression. We test the performance of this framework on synthetic and field data with different noise levels. The results demonstrate that the block-matching-based framework achieves state-of-the-art denoising performance in terms of incoherent-noise attenuation and signal preservation.

Extended stable factor method for the inverse Q-filter

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. T155-T163
Author(s):  
Yong Li ◽  
Gulan Zhang ◽  
Jing Duan ◽  
Chengjie He ◽  
Hao Du ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Time Frequency ◽  
Stable Factor ◽  
Factor Method ◽  
Data Application ◽  
Two Parameters ◽  
Limit Assumption

The commonly used stable factor methods for the inverse [Formula: see text]-filter achieve good performance in seismic data processing; however, the constant gain-limit assumption in these methods is not associated with the effective frequency band of seismic data and cannot obtain desirable results with high resolution and high signal-to-noise ratio (S/N). Our extended stable factor method for the inverse [Formula: see text]-filter extends these methods by introducing two parameters and constant or self-adaptive gain limit to achieve the desirable high-resolution and high-S/N result. The extended stable factor method for the inverse [Formula: see text]-filter can be implemented in the frequency or time-frequency domain; the latter implementation achieves a higher S/N. Analysis of synthetic signals and field seismic data application illustrate that our method can produce a desirable high-resolution and high-S/N result.

A new technique for first-arrival picking of refracted seismic data based on digital image segmentation

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. V79-V89 ◽  
Author(s):  
Wail A. Mousa ◽  
Abdullatif A. Al-Shuhail ◽  
Ayman Al-Lehyani
Keyword(s):  
Image Segmentation ◽  
Seismic Data ◽  
Convex Sets ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Color Image Segmentation ◽  
High Signal ◽  
Data Sets ◽  
First Arrival

We introduce a new method for first-arrival picking based on digital color-image segmentation of energy ratios of refracted seismic data. The method uses a new color-image segmentation scheme based on projection onto convex sets (POCS). The POCS requires a reference color for the first break and one iteration to segment the first-break amplitudes from other arrivals. We tested the segmentation method on synthetic seismic data sets with various amounts of additive Gaussian noise. The proposed method gives similar performance to a modified version of Coppens’ method for traces with high signal-to-noise ratio and medium-to-large offsets. Finally, we applied our method and used as well the modified first-arrival picking method based on Coppens’ method to pick the first arrivals on four real data sets, where both were compared to the first breaks that were picked manually and then interpolated. Based on an assessment error of a 20-ms window with respect to manual picks that are interpolated, we find that our method gives comparable performance to Coppens’ method, depending on the data difficulty of picking first arrivals. Therefore, we believe that our proposed method is a good new addition to the existing methods of first-arrival picking.

scholarly journals A Natural Images Pre-Trained Deep Learning Method for Seismic Random Noise Attenuation

2022 ◽  
Vol 14 (2) ◽  
pp. 263
Author(s):  
Haixia Zhao ◽  
Tingting Bai ◽  
Zhiqiang Wang
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Field Data ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Natural Images ◽  
Training Data ◽  
Learning Approaches ◽  
Learning Method

Seismic field data are usually contaminated by random or complex noise, which seriously affect the quality of seismic data contaminating seismic imaging and seismic interpretation. Improving the signal-to-noise ratio (SNR) of seismic data has always been a key step in seismic data processing. Deep learning approaches have been successfully applied to suppress seismic random noise. The training examples are essential in deep learning methods, especially for the geophysical problems, where the complete training data are not easy to be acquired due to high cost of acquisition. In this work, we propose a natural images pre-trained deep learning method to suppress seismic random noise through insight of the transfer learning. Our network contains pre-trained and post-trained networks: the former is trained by natural images to obtain the preliminary denoising results, while the latter is trained by a small amount of seismic images to fine-tune the denoising effects by semi-supervised learning to enhance the continuity of geological structures. The results of four types of synthetic seismic data and six field data demonstrate that our network has great performance in seismic random noise suppression in terms of both quantitative metrics and intuitive effects.

Denoising of seismic data in desert environment based on a variational mode decomposition and a convolutional neural network

2020 ◽  
Vol 221 (2) ◽  
pp. 1211-1225 ◽  
Author(s):  
Y X Zhao ◽  
Y Li ◽  
B J Yang
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Convolutional Neural Network ◽  
Seismic Data ◽  
Negative Impact ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Seismic Data Processing ◽  
Underground Structures ◽  
Seismic Records

SUMMARY One of the difficulties in desert seismic data processing is the large spectral overlap between noise and reflected signals. Existing denoising algorithms usually have a negative impact on the resolution and fidelity of seismic data when denoising, which is not conducive to the acquisition of underground structures and lithology related information. Aiming at this problem, we combine traditional method with deep learning, and propose a new feature extraction and denoising strategy based on a convolutional neural network, namely VMDCNN. In addition, we also build a training set using field seismic data and synthetic seismic data to optimize network parameters. The processing results of synthetic seismic records and field seismic records show that the proposed method can effectively suppress the noise that shares the same frequency band with the reflected signals, and the reflected signals have almost no energy loss. The processing results meet the requirements of high signal-to-noise ratio, high resolution and high fidelity for seismic data processing.

scholarly journals Amplitude-Preserved Wave Equation: An Example to Image the Gas Hydrate System

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3700
Author(s):  
Jiachun You ◽  
Sha Song ◽  
Umberta Tinivella ◽  
Michela Giustiniani ◽  
Iván Vargas-Cordero
Keyword(s):  
Wave Equation ◽  
Gas Hydrate ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Matrix Decomposition ◽  
Real Data ◽  
High Signal ◽  
Imaging Result ◽  
Imaging Results ◽  
Seismic Records

Natural gas hydrate is an important energy source. Therefore, it is extremely important to provide a clear imaging profile to determine its distribution for energy exploration. In view of the problems existing in conventional migration methods, e.g., the limited imaging angles, we proposed to utilize an amplitude-preserved one-way wave equation migration based on matrix decomposition to deal with primary and multiple waves. With respect to seismic data gathered at the Chilean continental margin, a conventional processing flow to obtain seismic records with a high signal-to-noise ratio is introduced. Then, the imaging results of the conventional and amplitude-preserved one-way wave equation migration methods based on primary waves are compared, to demonstrate the necessity of implementing amplitude-preserving migration. Moreover, a simple two-layer model is imaged by using primary and multiple waves, which proves the superiority of multiple waves in imaging compared with primary waves and lays the foundation for further application. For the real data, the imaging sections of primary and multiple waves are compared. We found that multiple waves are able to provide a wider imaging illumination while primary waves fail to illuminate, especially for the imaging of bottom simulating reflections (BSRs), because multiple waves have a longer travelling path and carry more information. By imaging the actual seismic data, we can make a conclusion that the imaging result generated by multiple waves can be viewed as a supplementary for the imaging result of primary waves, and it has some guiding values for further hydrate and in general shallow gas exploration.

scholarly journals Application of an improved particle filter for random seismic noise suppression

2021 ◽  
Vol 18 (6) ◽  
pp. 943-953
Author(s):  
Jingquan Zhang ◽  
Dian Wang ◽  
Peng Li ◽  
Shiyu Liu ◽  
Han Yu ◽  
...  
Keyword(s):  
Particle Filter ◽  
Seismic Data ◽  
Particle Filtering ◽  
Seismic Noise ◽  
Firefly Algorithm ◽  
Field Experiments ◽  
Noise Suppression ◽  
Random Noise ◽  
Seismic Signals ◽  
And Performance

Abstract Random noise is inevitable during seismic prospecting. Seismic signals, which are variable in time and space, are damaged by conventional random noise suppression methods, and this limits the accuracy in seismic data imaging. In this paper, an improved particle filtering strategy based on the firefly algorithm is proposed to suppress seismic noise. To address particle degradation problems during the particle filter resampling process, this method introduces a firefly algorithm that moves the particles distributed at the tail of the probability to the high-likelihood area, thereby improving the particle quality and performance of the algorithm. Finally, this method allows the particles to carry adequate seismic information, thereby enhancing the accuracy of the estimation. Synthetic and field experiments indicate that this method can effectively suppress random seismic noise.

Image processing of seismic attributes for automatic fault extraction

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. O25-O37 ◽  
Author(s):  
Jie Qi ◽  
Bin Lyu ◽  
Abdulmohsen AlAli ◽  
Gabriel Machado ◽  
Ying Hu ◽  
...  
Keyword(s):  
Image Processing ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Data Interpretation ◽  
Machine Learning Techniques ◽  
High Signal ◽  
Good Resolution ◽  
Low Coherence ◽  
Amplitude Data

Along with horizon picking, fault identification and interpretation is one of the key components for successful seismic data interpretation. Significant effort has been invested in accelerating seismic fault interpretation over the past three decades. Seismic amplitude data exhibiting good resolution and a high signal-to-noise ratio are key to identifying structural discontinuities using coherence or other edge-detection attributes, which in turn serve as inputs for automatic fault extraction using image processing or machine learning techniques. Because seismic data exhibit not only structural reflectors but also seismic noise, we have developed a fault attribute workflow that contains footprint suppression, structure-oriented filtering, attribute computation, “unconformity” suppression, and our new iterative energy-weighted directional Laplacian of a Gaussian (LoG) operator. In general, tracking faults that exhibit a finite offset through a suite of conformal reflectors is relatively easy. Instead, we evaluate the effectiveness of this workflow by tracking faults through an incoherent mass-transport deposit, where the low-frequency contribution of multispectral coherence provides a good fault image. Multispectral coherence also reduces the “stair-step” fault artifacts seen on broadband data. Application of statistical filtering can preserve the discontinuity’s boundaries and reject incoherent backgrounds. Finally, iterative application of an energy-weighted directional LoG operator provides improved fault image by sharpening low-coherence anomalies perpendicular and smoothing low-coherence anomalies parallel to fault surfaces, while at the same time attenuating locally nonplanar anomalies.

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