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1.
Xintao Chai Ronghua Peng Genyang Tang Wei Chen Jingnan Li 《Geophysical Prospecting》2019,67(8):2003-2021
The subsurface media are not perfectly elastic, thus anelastic absorption, attenuation and dispersion (aka Q filtering) effects occur during wave propagation, diminishing seismic resolution. Compensating for anelastic effects is imperative for resolution enhancement. Q values are required for most of conventional Q-compensation methods, and the source wavelet is additionally required for some of them. Based on the previous work of non-stationary sparse reflectivity inversion, we evaluate a series of methods for Q-compensation with/without knowing Q and with/without knowing wavelet. We demonstrate that if Q-compensation takes the wavelet into account, it generates better results for the severely attenuated components, benefiting from the sparsity promotion. We then evaluate a two-phase Q-compensation method in the frequency domain to eliminate Q requirement. In phase 1, the observed seismogram is disintegrated into the least number of Q-filtered wavelets chosen from a dictionary by optimizing a basis pursuit denoising problem, where the dictionary is composed of the known wavelet with different propagation times, each filtered with a range of possible values. The elements of the dictionary are weighted by the infinity norm of the corresponding column and further preconditioned to provide wavelets of different values and different propagation times equal probability to entry into the solution space. In phase 2, we derive analytic solutions for estimates of reflectivity and Q and solve an over-determined equation to obtain the final reflectivity series and Q values, where both the amplitude and phase information are utilized to estimate the Q values. The evaluated inversion-based Q estimation method handles the wave-interference effects better than conventional spectral-ratio-based methods. For Q-compensation, we investigate why sparsity promoting does matter. Numerical and field data experiments indicate the feasibility of the evaluated method of Q-compensation without knowing Q but with wavelet given. 相似文献
2.
İrfan Akca Thomas Günther Mike Müller‐Petke Ahmet T. Başokur Ugur Yaramanci 《Geophysical Prospecting》2014,62(2):364-376
Magnetic resonance sounding (MRS) has increasingly become an important method in hydrogeophysics because it allows for estimations of essential hydraulic properties such as porosity and hydraulic conductivity. A resistivity model is required for magnetic resonance sounding modelling and inversion. Therefore, joint interpretation or inversion is favourable to reduce the ambiguities that arise in separate magnetic resonance sounding and vertical electrical sounding (VES) inversions. A new method is suggested for the joint inversion of magnetic resonance sounding and vertical electrical sounding data. A one‐dimensional blocky model with varying layer thicknesses is used for the subsurface discretization. Instead of conventional derivative‐based inversion schemes that are strongly dependent on initial models, a global multi‐objective optimization scheme (a genetic algorithm [GA] in this case) is preferred to examine a set of possible solutions in a predefined search space. Multi‐objective joint optimization avoids the domination of one objective over the other without applying a weighting scheme. The outcome is a group of non‐dominated optimal solutions referred to as the Pareto‐optimal set. Tests conducted using synthetic data show that the multi‐objective joint optimization approximates the joint model parameters within the experimental error level and illustrates the range of trade‐off solutions, which is useful for understanding the consistency and conflicts between two models and objectives. Overall, the Levenberg‐Marquardt inversion of field data measured during a survey on a North Sea island presents similar solutions. However, the multi‐objective genetic algorithm method presents an efficient method for exploring the search space by producing a set of non‐dominated solutions. Borehole data were used to provide a verification of the inversion outcomes and indicate that the suggested genetic algorithm method is complementary for derivative‐based inversions. 相似文献
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We develop a new Proper Orthogonal Decomposition (POD) reduced order model for saturated groundwater flow, and apply that model to an inverse problem for the hydraulic conductivity field. We use sensitivities as the POD basis. We compare the output when the optimizer uses the reduced order model against results obtained with a full PDE based model. The solutions generated using the POD reduced model are comparable in residual norm to the solutions formed using only the full-scale model. The material parameters are similarly comparable. The time to solution when using the reduced model is reduced by at least an order of magnitude, as are the number of calls to the full model. 相似文献
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This paper develops an adaptive fuzzy controller for the dynamic positioning (DP) system of vessels with unknown dynamic model parameters and unknown time-varying environmental disturbances. The controller is designed by combining the adaptive fuzzy system with the vectorial backstepping method. An adaptive fuzzy system is employed to approximate the uncertain term induced by unknown dynamic model parameters and unknown time-varying environmental disturbances. It is theoretically proved that the proposed adaptive fuzzy DP controller can make the vessel be maintained at the desired values of its position and heading with arbitrary accuracy, while guaranteeing the uniform ultimate boundedness of all signals in the closed-loop DP control system of vessels. Simulation studies with comparisons on a supply vessel are carried out, and the results illustrate the effectiveness of the proposed control scheme. 相似文献
7.
The initial modeling method based on TTI anisotropy can provide an initial model for the fine modeling of tomographic inversion, and the quality of the initial model directly affects the efficiency and accuracy of tomographic inversion. It is necessary to study the accurate initial modeling method of TTI anisotropy. Based on the conventional flow of the initial modeling method of TTI anisotropic, in this paper the anisotropic parameter extraction formula was perfected, a local tomography method to optimize the flow of the initial modeling method of TTI anisotropy was introduced, and the accuracy of the initial model was further improved, which is more consistent with the actual geological conditions and conducive to the fine modeling of the later tomographic inversion. The processing results of model and filed data show that the initial modeling method of TTI anisotropy based on local tomography is effective and practical. 相似文献
8.
Predicting reservoir parameters, such as porosity, lithology, and saturations, from geophysical parameters is a problem with non‐unique solutions. The variance in solutions can be extensive, especially for saturation and lithology. However, the reservoir parameters will typically vary smoothly within certain zones—in vertical and horizontal directions. In this work, we integrate spatial correlations in the predicted parameters to constrain the range of predicted solutions from a particular type of inverse rock physics modelling method. Our analysis is based on well‐log data from the Glitne field, where vertical correlations with depth are expected. It was found that the reservoir parameters with the shortest depth correlation (lithology and saturation) provided the strongest constraint to the set of solutions. In addition, due to the interdependence between the reservoir parameters, constraining the predictions by the spatial correlation of one parameter also reduced the number of predictions of the other two parameters. Moreover, the use of additional constraints such as measured log data at specific depth locations can further narrow the range of solutions. 相似文献
9.
We present a new inversion method to estimate, from prestack seismic data, blocky P‐ and S‐wave velocity and density images and the associated sparse reflectivity levels. The method uses the three‐term Aki and Richards approximation to linearise the seismic inversion problem. To this end, we adopt a weighted mixed l2, 1‐norm that promotes structured forms of sparsity, thus leading to blocky solutions in time. In addition, our algorithm incorporates a covariance or scale matrix to simultaneously constrain P‐ and S‐wave velocities and density. This a priori information is obtained by nearby well‐log data. We also include a term containing a low‐frequency background model. The l2, 1 mixed norm leads to a convex objective function that can be minimised using proximal algorithms. In particular, we use the fast iterative shrinkage‐thresholding algorithm. A key advantage of this algorithm is that it only requires matrix–vector multiplications and no direct matrix inversion. The latter makes our algorithm numerically stable, easy to apply, and economical in terms of computational cost. Tests on synthetic and field data show that the proposed method, contrarily to conventional l2‐ or l1‐norm regularised solutions, is able to provide consistent blocky and/or sparse estimators of P‐ and S‐wave velocities and density from a noisy and limited number of observations. 相似文献
10.
This paper addresses correlations between multiple components in structure‐specific seismic loss estimation. To date, the consideration of such correlations has been limited by methodological tractability, increased computational demand, and a paucity of data for their computation. The effect of component correlations, which arises in various forms, is however a significant factor affecting the results of structure‐specific seismic loss estimation and therefore it is prudent that adequate consideration be given to their effect. This paper provides the details of a tractable and computationally efficient seismic loss estimation methodology in which correlations can be considered. Methods to determine the necessary correlations are discussed, particularly those that can be used in the absence of sufficient empirical data, for which values are suggested based on the judgement. The effects of various assumptions regarding correlations are illustrated via application to a case‐study office structure. It is observed that certain correlation assumptions can lead to errors in excess of 50% in the lognormal standard deviation in the loss given intensity and loss hazard relationships, while full consideration of partial correlations is 50 times more computationally expensive than other assumptions. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献