Well-log decomposition using variational mode decomposition in assisting the sequence stratigraphy analysis of a conglomerate reservoir

Sequence stratigraphy analysis is one of the most important tasks in evaluating and characterizing the reservoir system within a basin. However, it is very hard to identify the system tracts and lithofacies using well logs for the conglomerate reservoirs because of the strong lithology heterogeneity. Based on the fact that the system tracts and lithofacies usually illustrate cycle features within the basin, we decompose the well logs into different intrinsic modes to characterize the sequence units and lithofacies at different scale. First, we analyze the log response to lithologies to determine the well logs used for sequence analysis. Then, we use variational mode decomposition to decompose the selected well logs into an ensemble of different band-limited intrinsic mode functions, each with its center wavenumber. Finally, we interpret the sequence stratigraphy and lithofacies using corresponding decomposed modes. We validate the effectiveness of our method in the lithofacies and sequence identification for a conglomerate reservoir in the Shengli oil field, Bohai Bay Basin, east China. The decomposed intrinsic modes with a larger center wavenumber perfectly characterize the sequence units at a larger scale, whereas the decomposed intrinsic modes with a smaller center wavenumber reveal the lithofacies changes at a smaller scale. The application illustrates that it is much more convenient and easier for sequence stratigraphy analysis to integrate the original and decomposed logs.

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Denoising of the Fiber Bragg Grating Deformation Spectrum Signal Using Variational Mode Decomposition Combined with Wavelet Thresholding

Applied Sciences ◽

10.3390/app9010180 ◽

2019 ◽

Vol 9 (1) ◽

pp. 180 ◽

Cited By ~ 4

Author(s):

Weifang Zhang ◽

Meng Zhang ◽

Yan Zhao ◽

Bo Jin ◽

Wei Dai

Keyword(s):

Discrete Wavelet ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Mode Decomposition ◽

Noisy Signals ◽

Band Limited ◽

Adaptive Signal Decomposition ◽

Adaptive Signal ◽

The Impact

Damage detection using an FBG sensor is a critical process for an assessment of any inspection technology classified as structural health monitoring (SHM). FBG signals containing noise in experiments are developed to detect flaws. In this paper, we propose a novel signal denoising method that combines variational mode decomposition (VMD) and changed thresholding wavelets to denoise experimental and mixed signals. VMD is a recently introduced adaptive signal decomposition algorithm. Compared with traditional empirical mode decomposition (EMD), and it is well founded theoretically and more robust to noise samples. First, input signals were broken down into a given number of K band-limited intrinsic mode functions (BLIMFs) by VMD. For the purpose of avoiding the impact of overbinning or underbinning on VMD denoising, the mixed signals, which were obtained by adding different signal/noise ratio (SNR) noises to the experimental signals, were designed to select the best decomposition number K and data-fidelity constraint parameter α. After that, the realistic experimental signals were processed using four denoising algorithms to evaluate denoising performance. The results show that, upon adding additional noisy signals and realistic signals, the proposed algorithm delivers excellent performance over the EMD-based denoising method and discrete wavelet transform filtering.

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Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition

Entropy ◽

10.3390/e23121567 ◽

2021 ◽

Vol 23 (12) ◽

pp. 1567

Author(s):

Ragavesh Dhandapani ◽

Imene Mitiche ◽

Scott McMeekin ◽

Venkateswara Sarma Mallela ◽

Gordon Morison

Keyword(s):

Performance Metrics ◽

Regularization Parameter ◽

Signal To Noise Ratio ◽

Partial Discharge ◽

Sensor Data ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Mode Decomposition ◽

Band Limited

This paper presents a new approach for denoising Partial Discharge (PD) signals using a hybrid algorithm combining the adaptive decomposition technique with Entropy measures and Group-Sparse Total Variation (GSTV). Initially, the Empirical Mode Decomposition (EMD) technique is applied to decompose a noisy sensor data into the Intrinsic Mode Functions (IMFs), Mutual Information (MI) analysis between IMFs is carried out to set the mode length K. Then, the Variational Mode Decomposition (VMD) technique decomposes a noisy sensor data into K number of Band Limited IMFs (BLIMFs). The BLIMFs are separated as noise, noise-dominant, and signal-dominant BLIMFs by calculating the MI between BLIMFs. Eventually, the noise BLIMFs are discarded from further processing, noise-dominant BLIMFs are denoised using GSTV, and the signal BLIMFs are added to reconstruct the output signal. The regularization parameter λ for GSTV is automatically selected based on the values of Dispersion Entropy of the noise-dominant BLIMFs. The effectiveness of the proposed denoising method is evaluated in terms of performance metrics such as Signal-to-Noise Ratio, Root Mean Square Error, and Correlation Coefficient, which are are compared to EMD variants, and the results demonstrated that the proposed approach is able to effectively denoise the synthetic Blocks, Bumps, Doppler, Heavy Sine, PD pulses and real PD signals.

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Analysis of hydropower unit vibration signals based on variational mode decomposition

Journal of Vibration and Control ◽

10.1177/1077546315605240 ◽

2015 ◽

Vol 23 (12) ◽

pp. 1938-1953 ◽

Cited By ~ 12

Author(s):

Xueli An ◽

Luoping Pan ◽

Fei Zhang

Keyword(s):

Hydraulic Turbine ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Accurate Analysis ◽

Vibration Signals ◽

Mode Decomposition ◽

Empirical Mode Decomposition Method ◽

The Face ◽

Band Limited ◽

Nonlinear Signal

The vibration signals of hydropower units are nonstationary when serious vortex occurs in the draft tube of the hydraulic turbine. The traditional signal analysis method based on Fourier transform is not suitable for the nonstationary signals. In the face of the nonstationarity of such signals and the limitation of the empirical mode decomposition method, a new nonstationary and nonlinear signal analyzing method based on variational mode decomposition (VMD) is introduced into hydropower unit vibration signals analysis. Firstly, VMD is used to decompose the signal into an ensemble of band-limited intrinsic mode functions components. Then, frequency spectrum analysis of these components is conducted to obtain the characteristic frequencies of the signal caused by the serious vortex of hydraulic turbine. Analysis of real test data shows that this proposed method can effectively suppress mode mixing. It can realize accurate analysis of nonstationary vibration signals. This provides a new way for analyzing hydropower unit vibration signals.

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The use of variational mode decomposition in assisting sedimentary cyclicity analysis: A case study from an Albian carbonate reservoir, Campos Basin, southeast Brazil

Geophysics ◽

10.1190/geo2019-0447.1 ◽

2020 ◽

Vol 85 (3) ◽

pp. B77-B86

Author(s):

Leandro Hartleben Melani ◽

Bruno César Zanardo Honório ◽

Ulisses Miguel da Costa Correia ◽

Alexandre Campane Vidal

Keyword(s):

Gamma Ray ◽

Carbonate Reservoir ◽

Well Log ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Short Term ◽

Time Frequency ◽

Mode Decomposition ◽

Campos Basin ◽

Band Limited

The sedimentary cyclicity analysis investigates the cyclic patterns and the different hierarchical orders of cyclicity in the stratigraphic record. The detection of cyclic depositional patterns is a key element of quantitative stratigraphy. It is often based on well-log data, which can be challenging due to the presence of superimposed cycles and nongeologic artifacts. We have developed an approach to assist the detection of sedimentary cyclicity in well-log signals based on a multiscale spectral analysis method. First, we apply variational mode decomposition to decompose the gamma-ray logs into band-limited subsignals, the intrinsic mode functions (IMFs), to investigate different orders of smoothness, signal-to-noise ratio, and the cyclicity embedded in the geologic record. Conventional time-domain analysis is carried out to understand the general trends in the IMFs, which enables us to better identify long-term cycles associated with transgressive-regressive (T-R) sequences. Then, by appropriately selecting a given IMF and extracting the instantaneous frequency (IF) and its mirrored version, we build a cyclicity log that can map expressive behavior change in the time-frequency domain. Because the IF is more sensitive to the signal variations, we could highlight the short-term cycles throughout the formation in detail. The detected short-term cycles are in agreement with the T-R sequence. We apply our method to the Albian carbonate succession of Macaé Group, Campos Basin, Brazil. We understand that our method can be a valuable tool for semiautomated detection of sedimentary cycles, assisting in the characterization of different hierarchical orders of cyclicity.

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Depositional sequence characterization based on seismic variational mode decomposition

Interpretation ◽

10.1190/int-2016-0069.1 ◽

2017 ◽

Vol 5 (2) ◽

pp. SE97-SE106 ◽

Cited By ~ 20

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.

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Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition

Sensors ◽

10.3390/s18103510 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3510 ◽

Cited By ~ 24

Author(s):

Zhijian Wang ◽

Junyuan Wang ◽

Wenhua Du

Keyword(s):

Fault Diagnosis ◽

Signal To Noise Ratio ◽

Optimal Number ◽

Original Signal ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Multiple Fault ◽

Mode Decomposition ◽

Fault Features ◽

Reconstructed Signal

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified.

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Identification of sequence stratigraphy in the Dongying Formation of the Liaoxi low uplift, Bohai Bay Basin, China

Canadian Journal of Earth Sciences ◽

10.1139/cjes-2018-0276 ◽

2020 ◽

Vol 57 (5) ◽

pp. 647-661

Author(s):

Guangzhao Zhou ◽

Zhiming Hu ◽

Xianggang Duan ◽

Jin Chang

Keyword(s):

Trace Elements ◽

Sequence Stratigraphy ◽

Lake Level ◽

Three Dimensional ◽

Major And Trace Elements ◽

Bohai Bay ◽

Stratigraphic Sequence ◽

Accommodation Space ◽

System Tracts ◽

Mixed Layers

A change in sea level (or lake level) causes a change in accommodation space and sediment compensation, which then controls the formation and evolution of sequences. A multidisciplinary approach based on the integration of clay minerals, major and trace elements, paleosalinity, and three-dimensional seismic data were applied to interpret the stratigraphic sequence of the Dongying Formation in the Liaoxi low uplift. The sequence surface indicates that multistage tectonic action, paleoclimate, paleosalinity, and lake level change jointly controlled the sequence stratigraphy. The illite and illite–smectite mixed layers indicate an arid to semi-arid climate, whereas kaolinite implies humid conditions. Based on this principle, it is clear that the stratigraphic sequence is closely related to paleoclimate. Interpretation of the continental or marine character of the sediments via paleosalinity is increasingly based on the concentration of some trace elements. Hence, the sequence surface can be better evaluated through paleosalinity data. Based on the Sr/Ba and boron content, paleosalinity was evaluated and the sequence boundary was identified. Analysis of the concentration of major and trace elements was used to identify the lithology of the sedimentary region and further discern the stratigraphic sequence. These sequences are composed of lowstand, transgressive, and highstand system tracts. The work herein aimed to understand the reasons for the changes in sequences under the paleoclimate. In addition, major and trace elements, studied together with paleosalinity and clay mineral content, result in the determination of implications for sequence stratigraphy in many other basins. This paper can provide a novel method for comprehensively discerning a sequence surface.

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The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy

Energies ◽

10.3390/en12030359 ◽

2019 ◽

Vol 12 (3) ◽

pp. 359 ◽

Cited By ~ 14

Author(s):

Yuxing Li ◽

Xiao Chen ◽

Jing Yu ◽

Xiaohui Yang ◽

Huijun Yang

Keyword(s):

Feature Extraction ◽

Optimization Method ◽

Sample Entropy ◽

Maximum Energy ◽

Data Driven ◽

Center Frequency ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Radiated Noise ◽

Mode Decomposition

The data-driven method is an important tool in the field of underwater acoustic signal processing. In order to realize the feature extraction of ship-radiated noise (S-RN), we proposed a data-driven optimization method called improved variational mode decomposition (IVMD). IVMD, as an improved method of variational mode decomposition (VMD), solved the problem of choosing decomposition layers for VMD by using a frequency-aided method. Furthermore, a novel method of feature extraction for S-RN, which combines IVMD and sample entropy (SE), is put forward in this paper. In this study, four types of S-RN signals are decomposed into a group of intrinsic mode functions (IMFs) by IVMD. Then, SEs of all IMFs are calculated. SEs are different in the maximum energy IMFs (EIMFs), thus, SE of the EIMF is seen as a novel feature for S-RN. To verify the effectiveness of the proposed method, a comparison has been conducted by comparing features of center frequency and SE of the EIMF by IVMD, empirical mode decomposition (EMD) and ensemble EMD (EEMD). The analysis results show that the feature of S-RN can be obtain efficiently and accurately by using the proposed method.

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Application of variational mode decomposition to seismic random noise reduction

Journal of Geophysics and Engineering ◽

10.1093/jge/aa6b28 ◽

2017 ◽

Vol 14 (4) ◽

pp. 888-898 ◽

Cited By ~ 6

Author(s):

Wei Liu ◽

Siyuan Cao ◽

Zhiming Wang

Keyword(s):

Noise Reduction ◽

Empirical Mode Decomposition ◽

Random Noise ◽

Computational Cost ◽

Superior Performance ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Mode Decomposition ◽

Reconstruction Performance ◽

Non Stationary Signal

Abstract We have proposed a new denoising method for the simultaneous noise reduction and preservation of seismic signals based on variational mode decomposition (VMD). VMD is a recently developed adaptive signal decomposition method and an advance in non-stationary signal analysis. It solves the mode-mixing and non-optimal reconstruction performance problems of empirical mode decomposition that have existed for a long time. By using VMD, a multi-component signal can be non-recursively decomposed into a series of quasi-orthogonal intrinsic mode functions (IMFs), each of which has a relatively local frequency range. Meanwhile, the signal will focus on a smaller number of obtained IMFs after decomposition, and thus the denoised result is able to be obtained by reconstructing these signal-dominant IMFs. Synthetic examples are given to demonstrate the effectiveness of the proposed approach and comparison is made with the complete ensemble empirical mode decomposition, which demonstrates that the VMD algorithm has lower computational cost and better random noise elimination performance. The application of on field seismic data further illustrates the superior performance of our method in both random noise attenuation and the recovery of seismic events.

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Applications of variational mode decomposition in seismic time-frequency analysis

Geophysics ◽

10.1190/geo2015-0489.1 ◽

2016 ◽

Vol 81 (5) ◽

pp. V365-V378 ◽

Cited By ~ 74

Author(s):

Wei Liu ◽

Siyuan Cao ◽

Yangkang Chen

Keyword(s):

Empirical Mode Decomposition ◽

Frequency Analysis ◽

Synthetic Data ◽

Ensemble Empirical Mode Decomposition ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Decomposition Approach ◽

Time Frequency Analysis ◽

Time Frequency ◽

Mode Decomposition

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

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