Continuous wavelet transformation of seismic data for feature extraction

Ali, Amjad; Chen, Sheng‐Chang; Shah, Munawar

doi:10.1007/s42452-020-03618-w

Cited by 12 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CWT enhances seismic data resolution in thin bed detection, aiding in the identification of subtle changes associated with thin stratigraphic layers [68]. It is essential to fracture analysis and improves our understanding of reservoir structure [50]. CWT quantifies reservoir heterogeneity by analyzing variations in seismic attributes across different scales [69].…”

Section: Implication For Reservoir Studies and Managementmentioning

confidence: 99%

“…In resource identification, CWT is applied to identify gas hydrate-bearing sediments, effectively analyzing their distribution and concentration within reservoirs [70] [71]. For time-lapse reservoir monitoring, CWT aids in identifying changes in reservoir properties over time, providing crucial information for informed reservoir management [50]. Its simultaneous consideration of time and frequency information enhances our understanding of subsurface structures and refines reservoir characterization [8] International Journal of Geosciences…”

Section: Implication For Reservoir Studies and Managementmentioning

confidence: 99%

See 1 more Smart Citation

Performance of Continuous Wavelet Transform over Fourier Transform in Features Resolutions

Appiah,

Danuor,

Bienibuor

2024

IJG

View full text Add to dashboard Cite

This study presents a comparative analysis of two image enhancement techniques, Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT), in the context of improving the clarity of high-quality 3D seismic data obtained from the Tano Basin in West Africa, Ghana. The research focuses on a comparative analysis of image clarity in seismic attribute analysis to facilitate the identification of reservoir features within the subsurface structures. The findings of the study indicate that CWT has a significant advantage over FFT in terms of image quality and identifying subsurface structures. The results demonstrate the superior performance of CWT in providing a better representation, making it more effective for seismic attribute analysis. The study highlights the importance of choosing the appropriate image enhancement technique based on the specific application needs and the broader context of the study. While CWT provides high-quality images and superior performance in identifying subsurface structures, the selection between these methods should be made judiciously, taking into account the objectives of the study and the characteristics of the signals being analyzed. The research provides valuable insights into the decision-making process for selecting image enhancement techniques in seismic data analysis, helping researchers and practitioners make informed choices that cater to the unique requirements of their studies. Ultimately, this study contributes to the advancement of the field of subsurface imaging and geological feature identification.

show abstract

Section: Implication For Reservoir Studies and Managementmentioning

confidence: 99%

Section: Implication For Reservoir Studies and Managementmentioning

confidence: 99%

Performance of Continuous Wavelet Transform over Fourier Transform in Features Resolutions

Appiah,

Danuor,

Bienibuor

2024

IJG

View full text Add to dashboard Cite

show abstract

“…The concept behind the Continuous Wavelet Transform (CWT) is to analyse a signal by convolving it with a set of continuously varying wavelet functions, known as the analyzing wavelets. These wavelets are scaled and translated versions of a mother wavelet function [15]. The CWT evaluates the signal to wavelet versions that have been stretched, shifted, or compressed as shown in figure (1).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Multiple Fault Detection in Taper Roller Bearings Based on Sensor Fusion of Acoustic Emission and Vibration Data Using Deep Learning Techniques

Mahajan,

Vasu,

Srin

et al. 2023

Preprint

View full text Add to dashboard Cite

In rotating machinery, taper roller bearings are essential parts whose failure can have fatal consequences. To ensure safe and effective operation, these bearings must undergo condition monitoring and fault diagnosis. This study uses sensor fusion of vibration and acoustic emission data to demonstrate an integrated approach for multiple fault detection in taper roller bearings. Most of the literature classified bearing faults in 3 or 4 classes. Classification for lesser faults is an easy task as compared to classification of multiple faults. The proposed experiment demonstrate that the suggested method is highly accurate in detecting multiple bearing faults, and the integration of acoustic emission and vibration data improves the fault detection performance compared to using either data source alone. The proposed method involves collecting acoustic and vibration data using a DAQ system for multiple bearing faults and converting that data into scalogram images. This study also focused on importance of data denoising and use of optimal hyperparameter. As per proposed method data pre-processing and setting ideal hyperparameter have significantly increased the accuracy of bearing fault classification. This study uses deep learning CNN model to classify the scalogram images of bearing faults. CNN automatically extract features from scalogram images and classify images based on their similarities. The proposed approach can be used to monitorthe conditions and diagnose various faults of taper roller bearings in various industrial applications.

show abstract

“…In Equation ( 2) the parameters g and h of Equation ( 1) were changed to be the functions of integers p and q, respectively. k is a variable integer and is equal to the sample number of an input signal [26][27][28][29][30][31][32][33].…”

Section: Wavelet Transformsmentioning

confidence: 99%

Study of Transmission Line Boundary Protection Using a Multilayer Perceptron Neural Network with Back Propagation and Wavelet Transform

Okojie¹,

Idoko²,

Herbert³

et al. 2021

ASI

View full text Add to dashboard Cite

Protection schemes are usually implemented in the planning of transmission line operations. These schemes are expected to protect not only the network of transmission lines but also the entire power systems network during fault conditions. However, it is often a challenge for these schemes to differentiate accurately between various fault locations. This study analyses the deficiencies identified in existing protection schemes and investigates a different method that proposes to overcome these shortcomings. The proposed scheme operates by performing a wavelet transform on the fault-generated signal, which reduces the signal into frequency components. These components are then used as the input data for a multilayer perceptron neural network with backpropagation that can classify between different fault locations in the system. The study uses the transient signal generated during fault conditions to identify faults. The scientific research paradigm was adopted for the study. It also adopted the deduction research approach as it requires data collection via simulation using the Simscape electrical sub-program of Simulink within Matrix laboratory (MATLAB). The outcome of the study shows that the simulation correctly classifies 70.59% of the faults when tested. This implies that the majority of the faults can be detected and accurately isolated using boundary protection of transmission lines with the help of wavelet transforms and a neural network. The outcome also shows that more accurate fault identification and classification are achievable by using neural network than by the conventional system currently in use.

show abstract

Continuous wavelet transformation of seismic data for feature extraction

Cited by 12 publications

References 26 publications

Performance of Continuous Wavelet Transform over Fourier Transform in Features Resolutions

Performance of Continuous Wavelet Transform over Fourier Transform in Features Resolutions

Multiple Fault Detection in Taper Roller Bearings Based on Sensor Fusion of Acoustic Emission and Vibration Data Using Deep Learning Techniques

Study of Transmission Line Boundary Protection Using a Multilayer Perceptron Neural Network with Back Propagation and Wavelet Transform

Contact Info

Product

Resources

About