scholarly journals Nonlinear AVA Inversion Based on a Novel Quadratic Approximation for Fluid Discrimination

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Lin Zhou ◽  
Xingye Liu ◽  
Tianchun Yang ◽  
Jianping Liao ◽  
Mingfeng Zhu ◽  
...  
Keyword(s):  
Objective Function ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Quadratic Approximation ◽  
Nonlinear Inversion ◽  
Zoeppritz Equations ◽  
Linear Approximations ◽  
Inversion Methods ◽  
Fluid Factor ◽  
Highly Nonlinear

Fluid discrimination plays an important role in reservoir exploration and development. At present, the fluid factors used for fluid discrimination are estimated by linear AVA inversion methods based on the linear approximations of the Zoeppritz equations. However, the Zoeppritz equations show that the relationship between prestack AVA reflection coefficients and reservoir parameters is highly nonlinear. Therefore, inversion methods based on linear approximations will seriously influence the nonuniqueness and uncertainty of inversion results. In this paper, a nonlinear inversion based on the quadratic approximation is carried out to reduce the nonuniqueness and uncertainty of fluid factor. Firstly, in order to directly invert the fluid factor, a novel quadratic approximation in terms of the fluid factor ( ρ f ), shear modulus, and density on both sides of the reflection interface is derived based on poroelasticity theory. Then, a nonlinear inversion objective function is constructed using the novel quadratic approximation in a Bayesian framework, and the Gauss-Newton method is adopted to minimize this objective function. The synthetic data example shows that the new method can obtain reasonable fluid factor inversion results even in low SNR (signal-to-noise ratio) case. Finally, the proposed method is also applied to field data which shows that it can effectively discriminate reservoir fluids.

Robust AVO inversion for the fluid factor and shear modulus

Geophysics ◽  
2021 ◽  
pp. 1-51
Author(s):  
Lin Zhou ◽  
Xingye Liu ◽  
Jingye Li ◽  
Jianping Liao
Keyword(s):  
Shear Modulus ◽  
Objective Function ◽  
Velocity Ratio ◽  
Estimation Accuracy ◽  
Amplitude Variation With Offset ◽  
Fluid Identification ◽  
Zoeppritz Equations ◽  
Avo Inversion ◽  
Fluid Factor ◽  
Two Parameters

Seismic estimation of the fluid factor and shear modulus plays an important role in reservoir fluid identification and characterization. Various amplitude variation with offset inversion methods have been used to estimate these two parameters, which generally based on approximate formulations of the Zoeppritz equations. However, the accuracy of these methods is limited because the forward modeling ability of approximate equations is incorrect under the conditions of strong impedance contrast and large incidence angles. Therefore, to improve the estimation accuracy, we use the Zoeppritz equations to directly invert for the fluid factor and shear modulus. Based on poroelasticity theory, we derive the Zoeppritz equations in a new form containing the fluid factor, shear modulus, density and dry-rock velocity ratio squared. The objective function is then constructed using these equations in a Bayesian framework with the addition of a differentiable Laplace distribution blockiness constraint term to the prior model to enhance fluid boundaries. Finally, the nonlinear objective function is solved by combining the Taylor expansion and the iterative reweighed least-squares algorithm. Numerical experiments indicate that the inversion accuracy of the proposed method may heavily depends on the parameter of the dry-rock velocity ratio square that is assumed static. However, tests on synthetic and field data show that the proposed method can estimate the fluid factor and shear modulus with satisfactory accuracy in the case of choosing a reasonable static value of this parameter. In addition, we demonstrate that the accuracy of this method is higher than that of the linearized formulation.

A simultaneous inversion for background velocity and impedance maps

Geophysics ◽  
1990 ◽  
Vol 55 (4) ◽  
pp. 458-469 ◽  
Author(s):  
D. Cao ◽  
W. B. Beydoun ◽  
S. C. Singh ◽  
A. Tarantola
Keyword(s):  
Traditional Approach ◽  
Waveform Inversion ◽  
Synthetic Data ◽  
Inversion Method ◽  
Nonlinear Inversion ◽  
Seismic Velocities ◽  
Inversion Algorithm ◽  
Seismic Reflection Data ◽  
Simultaneous Inversion ◽  
Highly Nonlinear

Full‐waveform inversion of seismic reflection data is highly nonlinear because of the irregular form of the function measuring the misfit between the observed and the synthetic data. Since the nonlinearity results mainly from the parameters describing seismic velocities, an alternative to the full nonlinear inversion is to have an inversion method which remains nonlinear with respect to velocities but linear with respect to impedance contrasts. The traditional approach is to decouple the nonlinear and linear parts by first estimating the background velocity from traveltimes, using either traveltime inversion or velocity analysis, and then estimating impedance contrasts from waveforms, using either waveform inversion or conventional migration. A more sophisticated strategy is to obtain both the subsurface background velocities and impedance contrasts simultaneously by using a single least‐squares norm waveform‐fit criterion. The background velocity that adequately represents the gross features of the medium is parameterized using a sparse grid, whereas the impedance contrasts use a dense grid. For each updated velocity model, the impedance contrasts are computed using a linearized inversion algorithm. For a 1-D velocity background, it is very efficient to perform inversion in the f-k domain by using the WKBJ and Born approximations. The method performs well both with synthetic and field data.

scholarly journals Quaternion Processing Techniques for Color Synthesized NDT Thermography

2021 ◽  
Vol 11 (2) ◽  
pp. 790
Author(s):  
Pablo Venegas ◽  
Rubén Usamentiaga ◽  
Juan Perán ◽  
Idurre Sáez de Ocáriz
Keyword(s):  
Signal To Noise Ratio ◽  
High Capacity ◽  
Synthetic Data ◽  
Relevant Information ◽  
Color Images ◽  
Destructive Testing ◽  
Application Procedure ◽  
Definition Of ◽  
Characterization Procedure ◽  
Processing Techniques

Infrared thermography is a widely used technology that has been successfully applied to many and varied applications. These applications include the use as a non-destructive testing tool to assess the integrity state of materials. The current level of development of this application is high and its effectiveness is widely verified. There are application protocols and methodologies that have demonstrated a high capacity to extract relevant information from the captured thermal signals and guarantee the detection of anomalies in the inspected materials. However, there is still room for improvement in certain aspects, such as the increase of the detection capacity and the definition of a detailed characterization procedure of indications, that must be investigated further to reduce uncertainties and optimize this technology. In this work, an innovative thermographic data analysis methodology is proposed that extracts a greater amount of information from the recorded sequences by applying advanced processing techniques to the results. The extracted information is synthesized into three channels that may be represented through real color images and processed by quaternion algebra techniques to improve the detection level and facilitate the classification of defects. To validate the proposed methodology, synthetic data and actual experimental sequences have been analyzed. Seven different definitions of signal-to-noise ratio (SNR) have been used to assess the increment in the detection capacity, and a generalized application procedure has been proposed to extend their use to color images. The results verify the capacity of this methodology, showing significant increments in the SNR compared to conventional processing techniques in thermographic NDT.

scholarly journals Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Maja B. Rosić ◽  
Mirjana I. Simić ◽  
Predrag V. Pejović
Keyword(s):  
Differential Evolution ◽  
Objective Function ◽  
Weighted Least Squares ◽  
Target Localization ◽  
Localization Problem ◽  
Ml Estimation ◽  
Comparison Results ◽  
Adaptive Differential Evolution ◽  
Highly Nonlinear ◽  
Wide Range

This paper considers a passive target localization problem in Wireless Sensor Networks (WSNs) using the noisy time of arrival (TOA) measurements, obtained from multiple receivers and a single transmitter. The objective function is formulated as a maximum likelihood (ML) estimation problem under the Gaussian noise assumption. Consequently, the objective function of the ML estimator is a highly nonlinear and nonconvex function, where conventional optimization methods are not suitable for this type of problem. Hence, an improved algorithm based on the hybridization of an adaptive differential evolution (ADE) and Nelder-Mead (NM) algorithms, named HADENM, is proposed to find the estimated position of a passive target. In this paper, the control parameters of the ADE algorithm are adaptively updated during the evolution process. In addition, an adaptive adjustment parameter is designed to provide a balance between the global exploration and the local exploitation abilities. Furthermore, the exploitation is strengthened using the NM method by improving the accuracy of the best solution obtained from the ADE algorithm. Statistical analysis has been conducted, to evaluate the benefits of the proposed modifications on the optimization performance of the HADENM algorithm. The comparison results between HADENM algorithm and its versions indicate that the modifications proposed in this paper can improve the overall optimization performance. Furthermore, the simulation shows that the proposed HADENM algorithm can attain the Cramer-Rao lower bound (CRLB) and outperforms the constrained weighted least squares (CWLS) and differential evolution (DE) algorithms. The obtained results demonstrate the high accuracy and robustness of the proposed algorithm for solving the passive target localization problem for a wide range of measurement noise levels.

scholarly journals Continuum Power CCA: A Unified Approach for Isolating Coupled Modes

2015 ◽  
Vol 28 (3) ◽  
pp. 1016-1030 ◽  
Author(s):  
Erik Swenson
Keyword(s):  
Signal To Noise Ratio ◽  
Full Range ◽  
Synthetic Data ◽  
Principal Component Regression ◽  
Principal Component ◽  
Accurate Estimate ◽  
Unified Approach ◽  
Coupled Modes ◽  
Multivariate Statistical ◽  
Sample Covariance

Abstract Various multivariate statistical methods exist for analyzing covariance and isolating linear relationships between datasets. The most popular linear methods are based on singular value decomposition (SVD) and include canonical correlation analysis (CCA), maximum covariance analysis (MCA), and redundancy analysis (RDA). In this study, continuum power CCA (CPCCA) is introduced as one extension of continuum power regression for isolating pairs of coupled patterns whose temporal variation maximizes the squared covariance between partially whitened variables. Similar to the whitening transformation, the partial whitening transformation acts to decorrelate individual variables but only to a partial degree with the added benefit of preconditioning sample covariance matrices prior to inversion, providing a more accurate estimate of the population covariance. CPCCA is a unified approach in the sense that the full range of solutions bridges CCA, MCA, RDA, and principal component regression (PCR). Recommended CPCCA solutions include a regularization for CCA, a variance bias correction for MCA, and a regularization for RDA. Applied to synthetic data samples, such solutions yield relatively higher skill in isolating known coupled modes embedded in noise. Provided with some crude prior expectation of the signal-to-noise ratio, the use of asymmetric CPCCA solutions may be justifiable and beneficial. An objective parameter choice is offered for regularization with CPCCA based on the covariance estimate of O. Ledoit and M. Wolf, and the results are quite robust. CPCCA is encouraged for a range of applications.

Optimization of the Closure-Weld Region of Cylindrical Containers for Long-Term Corrosion Resistance Using the Successive Heuristic Quadratic Approximation Technique*

2003 ◽  
Vol 125 (3) ◽  
pp. 533-539 ◽  
Author(s):  
Zekai Ceylan ◽  
Mohamed B. Trabia
Keyword(s):  
Finite Element ◽  
Random Search ◽  
Nonlinear Problems ◽  
Search Space ◽  
Induction Coil ◽  
Quadratic Approximation ◽  
Approximation Technique ◽  
General Corrosion ◽  
Highly Nonlinear

Welded cylindrical containers are susceptible to stress corrosion cracking (SCC) in the closure-weld area. An induction coil heating technique may be used to relieve the residual stresses in the closure-weld. This technique involves localized heating of the material by the surrounding coils. The material is then cooled to room temperature by quenching. A two-dimensional axisymmetric finite element model is developed to study the effects of induction coil heating and subsequent quenching. The finite element results are validated through an experimental test. The container design is tuned to maximize the compressive stress from the outer surface to a depth that is equal to the long-term general corrosion rate of the container material multiplied by the desired container lifetime. The problem is subject to several geometrical and stress constraints. Two different solution methods are implemented for this purpose. First, an off-the-shelf optimization software is used. The results however were unsatisfactory because of the highly nonlinear nature of the problem. The paper proposes a novel alternative: the Successive Heuristic Quadratic Approximation (SHQA) technique. This algorithm combines successive quadratic approximation with an adaptive random search within varying search space. SHQA promises to be a suitable search method for computationally intensive, highly nonlinear problems.

scholarly journals Blind Source Separation Based on Quantum Slime Mould Algorithm in Impulse Noise

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zhiwei Zhang ◽  
Hongyuan Gao ◽  
Jingya Ma ◽  
Shihao Wang ◽  
Helin Sun
Keyword(s):  
Objective Function ◽  
Blind Source Separation ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Moving Average ◽  
Source Separation ◽  
Hybrid Optimization ◽  
Slime Mould ◽  
Stable Performance

In order to resolve engineering problems that the performance of the traditional blind source separation (BSS) methods deteriorates or even becomes invalid when the unknown source signals are interfered by impulse noise with a low signal-to-noise ratio (SNR), a more effective and robust BSS method is proposed. Based on dual-parameter variable tailing (DPVT) transformation function, moving average filtering (MAF), and median filtering (MF), a filtering system that can achieve noise suppression in an impulse noise environment is proposed, noted as MAF-DPVT-MF. A hybrid optimization objective function is designed based on the two independence criteria to achieve more effective and robust BSS. Meanwhile, combining quantum computation theory with slime mould algorithm (SMA), quantum slime mould algorithm (QSMA) is proposed and QSMA is used to solve the hybrid optimization objective function. The proposed method is called BSS based on QSMA (QSMA-BSS). The simulation results show that QSMA-BSS is superior to the traditional methods. Compared with previous BSS methods, QSMA-BSS has a wider applications range, more stable performance, and higher precision.

Nonlinear Measure for Nonlinear Dynamic Processes Using Convergence Area: Typical Case Studies

2021 ◽  
Author(s):  
Joanofarc Xavier ◽  
S.K. Patnayak ◽  
Rames Panda
Keyword(s):  
Phase Portrait ◽  
Case Studies ◽  
Initial Conditions ◽  
Operating Point ◽  
Linear Approximations ◽  
Promising Tool ◽  
Highly Nonlinear ◽  
Phase Portrait Analysis ◽  
Nonlinear Dynamic Processes ◽  
Nonlinear Measure

Abstract Several industrial chemical processes exhibit severe nonlinearity. This paper addresses the computational and nonlinear issues occurring in many typical industrial problems in aspects of its stability, strength of nonlinearity and input output dynamics. In this article, initially, a prospective investigation is conducted on various nonlinear processes through phase portrait analysis to understand their stability status at different initial conditions about the vicinity of the operating point of the process. To estimate the degree of nonlinearity, for input perturbations from its nominal value, a novel nonlinear measure is put forward, that anticipates on the converging area between the nonlinear and their linearized responses. The nonlinearity strength is fixed between 0 and 1 to classify processes to be mild, medium or highly nonlinear. The most suitable operating point, for which the system remains asymptotically stable is clearly identified from the phase portrait. The metric can be contemplated as a promising tool to measure the nonlinearity of Industrial case studies at different linear approximations. Numerical simulations are executed in Matlab to compute , which conveys that the nonlinear dynamics of each Industrial example is very sensitive to input perturbations at different linear approximations. In addition to the identified metric, nonlinear lemmas are framed to select appropriate control schemes for the processes based on its numerical value of nonlinearity..

Optimization of the Closure-Weld Region of Cylindrical Containers for Long-Term Corrosion Resistance

2001 ◽  
Author(s):  
Zekai Ceylan ◽  
Mohamed B. Trabia
Keyword(s):  
Finite Element ◽  
Random Search ◽  
Element Model ◽  
Stress Constraints ◽  
Induction Coil ◽  
Quadratic Approximation ◽  
General Corrosion ◽  
Highly Nonlinear ◽  
Weld Area

Abstract Welded cylindrical containers usually experience stress corrosion cracking (SCC) in the closure-weld area. Induction coil heating technique may be used to relieve the residual stresses from the closure-weld. This technique involves localized heating of the material by the surrounding coils. The material is then cooled to the room temperature by quenching. A two-dimensional axisymmetric finite element model is developed to study the effects of induction coil heating and subsequent quenching. The finite element results are validated through an experimental test. The parameters of the design are tuned to maximize the compressive stress within a layer of thickness from the outer surface that is equal to the long-term general corrosion of Alloy 22 (Appendix A). The problem is subject to geometrical and stress constraints. Two different solution methods are implemented for this purpose. First, an off-the-shelf optimization software is used to obtain an optimum solution. These results are not satisfactory because of the highly nonlinear nature of the problem. The paper proposes a novel alternative: the Successive Heuristic Quadratic Approximation (SHQA) technique. This algorithm combines successive quadratic approximation with an adaptive random search. Examples and discussion are included.

Failure Detection for Multiple Micro-Punches Outfitted in Progressive Piercing Processes With Artificial Intelligent Model

2019 ◽  
Author(s):  
Tsung-Liang Wu ◽  
Yu-Chun Hwang
Keyword(s):  
Neural Network ◽  
Rigid Body ◽  
Signal To Noise Ratio ◽  
Back Propagation ◽  
Vertical Direction ◽  
Failure Detection ◽  
Ensemble Empirical Mode Decomposition ◽  
Inversion Algorithm ◽  
Highly Nonlinear ◽  
Non Stationary Signal

Abstract The purpose of this study is to establish a model for the diagnosis of multiple micro-punch failures. The punch is assumed to a rigid body structure with a small change in the stiffness during the piercing process and its diameter is varied between Ø0.8–1.2 mm. Thus, the wearing trend of multiple punches in the piercing process and source of the interfered signals make it extremely difficult to analyze. The two major challenges that affect punch failure estimation are the poor signal-to-noise ratio within the factory environment and the rigid body mode disturbance in the signal. To acquire the vibratory signals of the piercing motion, uniaxial accelerometers were outfitted in the vertical direction on the progressive die. Since the piercing process is a series of highly nonlinear transient processes, the Ensemble Empirical Mode Decomposition (EEMD) is adopted as a decoupling operation tool for this kind of non-stationary signal. Furthermore, the dimension-reduced process can be manipulated by Intrinsic Mode Function (IMF) and a function representative of the feature is selected as the input for neural network model training. The training target is the most direct relationship with the product quality, the selected models are multi-layer perceptron and the back-propagation neural network (BPNN) of the error inversion algorithm. The artificial intelligence failure diagnosis of the piercing process is also realized.

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