Seismic depositional sequence characterization by using enhanced multi-channel variational mode decomposition

2021 ◽  
pp. 1-61
Author(s):  
Yajun Tian ◽  
Jinghuai Gao
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Scale Space ◽  
Space Representation ◽  
Variational Mode Decomposition ◽  
Depositional Sequence ◽  
Sequence Characterization ◽  
Sequence Stratigraphic ◽  
Mode Decomposition ◽  
Seismic Sequence Stratigraphy

For the seismic stratigraphy, a key issue is distinguishing the characteristics of seismic reflections generated by geological events with different scales, which in turn assists the sequence stratigraphic interpretation. The data-driven signal decomposition approaches, like variational mode decomposition (VMD) and multi-channel variational mode decomposition (MVMD), can utilize waveform similarity to decompose seismic data into several IMFs. Unfortunately, it is a hard task to define the number of IMFs. To overcome the shortcoming of the previous works, we constructed an enhanced multi-channel variational mode decomposition (EMVMD) and then proposed a workflow to decompose seismic data. We first explained the relationship between the IMFs and structures with different scales. Then, we proposed a method to set the number of IMFs by introduce the contraction operator mapping (COM) and the scale-space representation (SSR). Finally, we provided a workflow and applied it to synthetic and field data to identify seismic sequence stratigraphy boundaries. Synthetic and field data examples show that our workflow preserves lateral continuity and precisely extracts IMFs caused by depositional sequences with different geologic scales, facilitating the interpretation of subtle depositional patterns.

Depositional sequence characterization based on seismic variational mode decomposition

2017 ◽  
Vol 5 (2) ◽  
pp. SE97-SE106 ◽  
Author(s):  
Fangyu Li ◽  
Bo Zhang ◽  
Rui Zhai ◽  
Huailai Zhou ◽  
Kurt J. Marfurt
Keyword(s):  
Center Frequency ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Depositional Sequence ◽  
Sequence Characterization ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Data Adaptive ◽  
Band Limited ◽  
Sequence Boundaries

Subtle variations in otherwise similar seismic data can be highlighted in specific spectral components. Our goal is to highlight repetitive sequence boundaries to help define the depositional environment, which in turn provides an interpretation framework. Variational mode decomposition (VMD) is a novel data-driven signal decomposition method that provides several useful features compared with the commonly used time-frequency analysis. Rather than using predefined spectral bands, the VMD method adaptively decomposes a signal into an ensemble of band-limited intrinsic mode functions, each with its own center frequency. Because it is data adaptive, modes can vary rapidly between neighboring traces. We address this shortcoming of previous work by constructing a laterally consistent VMD method that preserves lateral continuity, facilitating the extraction of subtle depositional patterns. We validate the accuracy of our method using a synthetic depositional cycle example, and then we apply it to identify seismic sequence stratigraphy boundaries for a survey acquired in the Dutch sector, North Sea.

A new fault diagnosis method based on adaptive spectrum mode extraction

2021 ◽  
pp. 147592172098694
Author(s):  
Zhijian Wang ◽  
Ningning Yang ◽  
Naipeng Li ◽  
Wenhua Du ◽  
Junyuan Wang
Keyword(s):  
Scale Space ◽  
Variational Mode Decomposition ◽  
Rolling Bearings ◽  
Mode Decomposition ◽  
Penalty Factor ◽  
Diagnosis Method ◽  
Mode Extraction ◽  
Non Stationary Signal ◽  
Envelope Spectrum ◽  
Fault Feature Extraction

Variational mode decomposition provides a feasible method for non-stationary signal analysis, but the method is still not adaptive, which greatly limits the wide application of the method. Therefore, an adaptive spectrum mode extraction method is proposed in this article. The proposed method is mainly composed of spectral segmentation, mode extraction, and feedback adjustment. In the spectral segmentation part, considering the lack of robustness of classical scale space in a strong noise environment, this article proposes a method of fault feature mapping, which solves over-decomposition of variational mode decomposition guided by classical scale space. In the mode extraction part, for insufficient self-adaptability of the single penalty factor in the variational mode decomposition method, this article proposes a method of spectral aggregation factor, which solves multiple penalty factors that conform to different intrinsic modal functions. In the feedback adjustment part, this article proposes the method of transboundary criterion, which makes the result of variational mode decomposition within a preset range. Finally, using dispersion entropy and kurtosis indicators, intrinsic modal function components containing fault frequencies are extracted for envelope spectrum analysis to extract fault characteristic frequencies. In order to verify the effectiveness of the proposed method, the proposed method is applied to simulation signals and bearing fault signals. Comparing the decomposition results of different methods, the conclusion shows that the proposed method is more advantageous for the fault feature extraction of rolling bearings.

White noise attenuation of seismic trace by integrating variational mode decomposition with convolutional neural network

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. V307-V317 ◽  
Author(s):  
Hao Wu ◽  
Bo Zhang ◽  
Tengfei Lin ◽  
Fangyu Li ◽  
Naihao Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
White Noise ◽  
Seismic Data ◽  
Prior Information ◽  
Training Procedure ◽  
Noise Attenuation ◽  
Variational Mode Decomposition ◽  
Time Frequency ◽  
Mode Decomposition

Seismic noise attenuation is an important step in seismic data processing. Most noise attenuation algorithms are based on the analysis of time-frequency characteristics of the seismic data and noise. We have aimed to attenuate white noise of seismic data using the convolutional neural network (CNN). Traditional CNN-based noise attenuation algorithms need prior information (the “clean” seismic data or the noise contained in the seismic) in the training process. However, it is difficult to obtain such prior information in practice. We assume that the white noise contained in the seismic data can be simulated by a sufficient number of user-generated white noise realizations. We then attenuate the seismic white noise using the modified denoising CNN (MDnCNN). The MDnCNN does not need prior clean seismic data nor pure noise in the training procedure. To accurately and efficiently learn the features of seismic data and band-limited noise at different frequency bandwidths, we first decomposed the seismic data into several intrinsic mode functions (IMFs) using variational mode decomposition and then apply our denoising process to the IMFs. We use synthetic and field data examples to illustrate the robustness and superiority of our method over the traditional methods. The experiments demonstrate that our method can not only attenuate most of the white noise but it also rejects the migration artifacts.

scholarly journals A Rolling Bearing Fault Diagnosis-Optimized Scale-Space Representation for the Empirical Wavelet Transform

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Zechao Liu ◽  
Jianming Ding ◽  
Jianhui Lin ◽  
Yan Huang
Keyword(s):  
Wavelet Transform ◽  
Rolling Bearing ◽  
Scale Space ◽  
Ensemble Empirical Mode Decomposition ◽  
Space Representation ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Engineering Practices ◽  
Fault Information

Rolling element bearings have been widely used in mechanical systems, such as electric motors, generators, pumps, gearboxes, railway axles, and turbines, etc. Therefore, the detection of rolling bearing faults has been a hot research topic in engineering practices. Envelope demodulation represents a fundamental method for extracting effective fault information from measured vibration signals. However, the performance of envelope demodulation depends heavily on the selection of the filter band and central frequencies. The empirical wavelet transform (EWT), a new signal decomposition method, provides a framework for arbitrarily segmenting the Fourier spectrum of an analysed signal. Scale-space representation (SSR) can adaptively detect the boundaries of the EWT; however, it has two shortcomings: slow calculation speeds and invalid boundary detection results. Accordingly, an EWT method based on optimized scale-space representation (OSSR), namely, the EWTOSSR, is proposed. The effectiveness of the EWTOSSR is verified by comparisons between the simulation and the experimental signals. The results show that the EWTOSSR can automatically and effectively segment the EWT spectrum to extract fault information. Compared with three well-known methods (the traditional EWT, ensemble empirical mode decomposition (EEMD), and the fast kurtogram), the EWTOSSR exhibits a much better fault detection performance.

DEPOSITIONAL SEQUENCE MODEL FOR THE POST-BARROW GROUP NEOCOMIAN SUCCESSION, BARROW AND EXMOUTH SUB-BASINS, WESTERN AUSTRALIA

1993 ◽  
Vol 33 (1) ◽  
pp. 151
Author(s):  
Peter A. Arditto
Keyword(s):  
Seismic Data ◽  
Sensu Stricto ◽  
Well Log ◽  
Depositional Sequence ◽  
Sequence Stratigraphic ◽  
Exmouth Plateau ◽  
Stratigraphic Analysis ◽  
Cast Doubt ◽  
Well Data ◽  
Stratigraphic Nomenclature

Structural traps at the top 'Barrow Group' are the most successful oil exploration targets in the Barrow/Exmouth Sub-basins. However, a reinterpretation of recent exploration activities undertaken by BHP Petroleum Pty Ltd, combined with regional investigations on the Exmouth Plateau, has cast doubt on the validity of accepted stratigraphic nomenclature for the Neocomian succession. A more geologically rational subdivision of the upper part of the Neocomian succession into two discrete sequence stratigraphic units is proposed.Key seismic data from the Exmouth Plateau, tied into wells with good age control, have enabled precise recognition of the Intra-Valanginian Unconformity within the currently-defined Barrow Group. The Barrow Group (sensu stricto) is redefined in this paper as the Barrow Megasequence (restricted to a Berriasian age succession), comprising a rapid progradational phase, which was abruptly terminated by the Intra-Valanginian event.Local erosion of the Barrow Megasequence along the Novara Arch through an Early Valanginian uplift, during the ensuing Valanginian regional transgression, contributed to the development of a parasitic clastic wedge, previously referred to as the Upper Barrow Delta on the Exmouth Plateau and here named the Zeepaard Sequence, with a nominated section in Zeepaard-1. The Zeepaard sequence is terminated by a Top Valanginian unconformity, upon which a final deltaic clastic pulse was deposited as the Birdrong Sequence. Each successive clastic wedge had a more limited development, with the basinward progradation terminating well short of the underlying stratigraphic unit. The Birdrong Sequence was terminated by an Intra-Hauterivian unconformity upon which the highly glauconitic, thin, Mardie Greensand Member of the Muderong Sequence was developed.The Zeepaard Sequence and overlying Birdrong Sequence can be characterised using both seismic and well log character. Well data in particular has enabled detailed stratigraphic mapping of the Birdrong Sequence which is thin and generally not seismically resolvable across the Barrow Sub-basin. This paper presents a detailed sequence stratigraphic analysis of the Birdrong Sequence using well log data.

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