Relevant bibliographies by topics / Neural network inversion

Academic literature on the topic 'Neural network inversion'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Neural network inversion"

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Sun, Yuhang, Yang Liu, Mi Zhang, and Haoran Zhang. "Inversion of low- to medium-frequency velocities and densities from AVO data using invertible neural networks." GEOPHYSICS 87, no. 3 (March 3, 2022): A37—A42. http://dx.doi.org/10.1190/geo2021-0450.1.

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Amplitude-variation-with-offset (AVO) inversion and neural networks (NN) are widely used to invert elastic parameters. With more constraints from well-log data, neural network-based inversion may estimate elastic parameters with greater precision and resolution than traditional AVO inversion; however, neural network approaches necessitate a massive number of reliable training samples. Furthermore, because the lack of low-frequency information in seismic gathers leads to multiple solutions of the inverse problem, both inversions rely heavily on proper low-frequency initial models. To mitigate the dependence of inversions on accurate training samples and initial models, we have adopted solving inverse problems with the recently developed invertible neural networks (INNs). Unlike conventional neural networks, which address the ambiguous inverse issues directly, INNs learn definite forward modeling and use additional latent variables to increase the uniqueness of solutions. Motivated by the newly developed neural networks, we adopt an INN-based AVO inversion method, which can reliably invert low- to medium-frequency velocities and densities with randomly generated easy-to-access data sets rather than trustworthy training samples or well-prepared initial models. Tests on synthetic and field data indicate that our method is feasible, antinoise capable, and practicable.
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FEI, DONGYU, JOHN T. KUO, and YU-CHIUNG TENG. "WAVEFORM INVERSION AND MULTI-LAYER NEURAL NETWORK." Journal of Computational Acoustics 03, no. 03 (September 1995): 175–202. http://dx.doi.org/10.1142/s0218396x95000082.

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This paper presents the concept of neural network inversion. The basic mathematical problem is to establish the mapping between two multi-dimensional spaces, addressing the continuous function mapping between two close intervals. It introduces the 3-layer neural network existence theorem and proves that the multi-layer neural network can approximate any continuous function in the sense of supernorm or mean-squares-norm, provided that the activation function is locally Riemann integrable and nonpolynomial. With an initial guess of the target parameter, which, in the present case, is acoustic velocity, in a prescribed sphere, which contains the true parameter, the neural network inversion method ensures the search reaching the global minima. The principle of the neural network inversion on the basis of the least-squares minimization (L2 norm) is developed. As its application, this method is employed to perform seismic waveform inversions — Model 1, for a homogeneous isotropic earth with a 2-D rectangle embedded body, and Model 2, for a layered earth with an elliptically elongated inclusion. A fast computation algorithm of the finite element method is adopted to generate a series of synthetic shot records for training the 3-layer neural network. The trained neural network possesses the capability to find the acoustic velocity of the embedded body in both Model 1 and 2 with a real-time solution within a sufficient accuracy.
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Saad, Emad W., and Donald C. Wunsch. "Neural network explanation using inversion." Neural Networks 20, no. 1 (January 2007): 78–93. http://dx.doi.org/10.1016/j.neunet.2006.07.005.

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Sonehara, Noboru, and Yukio Tokunaga. "Neural Network Models for Image Inversion." Journal of Robotics and Mechatronics 5, no. 2 (April 20, 1993): 88–97. http://dx.doi.org/10.20965/jrm.1993.p0088.

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Das, Vishal, Ahinoam Pollack, Uri Wollner, and Tapan Mukerji. "Convolutional neural network for seismic impedance inversion." GEOPHYSICS 84, no. 6 (November 1, 2019): R869—R880. http://dx.doi.org/10.1190/geo2018-0838.1.

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We have addressed the geophysical problem of obtaining an elastic model of the subsurface from recorded normal-incidence seismic data using convolutional neural networks (CNNs). We train the network on synthetic full-waveform seismograms generated using Kennett’s reflectivity method on earth models that were created under rock-physics modeling constraints. We use an approximate Bayesian computation method to estimate the posterior distribution corresponding to the CNN prediction and to quantify the uncertainty related to the predictions. In addition, we test the robustness of the network in predicting impedances of previously unobserved earth models when the input to the network consisted of seismograms generated using: (1) earth models with different spatial correlations (i.e. variograms), (2) earth models with different facies proportions, (3) earth models with different underlying rock-physics relations, and (4) source-wavelet phase and frequency different than in the training data. Results indicate that the predictions of the trained network are susceptible to facies proportions, the rock-physics model, and source-wavelet parameters used in the training data set. Finally, we apply CNN inversion on the Volve field data set from offshore Norway. P-wave impedance [Formula: see text] inverted for the Volve data set using CNN showed a strong correlation (82%) with the [Formula: see text] log at a well.
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Zhu, Weiqiang, Kailai Xu, Eric Darve, Biondo Biondi, and Gregory C. Beroza. "Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification." GEOPHYSICS 87, no. 1 (December 6, 2021): R93—R109. http://dx.doi.org/10.1190/geo2020-0933.1.

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Full-waveform inversion (FWI) is an accurate imaging approach for modeling the velocity structure by minimizing the misfit between recorded and predicted seismic waveforms. However, the strong nonlinearity of FWI resulting from fitting oscillatory waveforms can trap the optimization in local minima. We have adopted a neural-network-based full-waveform inversion (NNFWI) method that integrates deep neural networks with FWI by representing the velocity model with a generative neural network. Neural networks can naturally introduce spatial correlations as regularization to the generated velocity model, which suppresses noise in the gradients and mitigates local minima. The velocity model generated by neural networks is input to the same partial differential equation (PDE) solvers used in conventional FWI. The gradients of the neural networks and PDEs are calculated using automatic differentiation, which back propagates gradients through the acoustic PDEs and neural network layers to update the weights of the generative neural network. Experiments on 1D velocity models, the Marmousi model, and the 2004 BP model determine that NNFWI can mitigate local minima, especially for imaging high-contrast features such as salt bodies, and it significantly improves the inversion in the presence of noise. Adding dropout layers to the neural network model also allows analyzing the uncertainty of the inversion results through Monte Carlo dropout. NNFWI opens a new pathway to combine deep learning and FWI for exploiting the characteristics of deep neural networks and the high accuracy of PDE solvers. Because NNFWI does not require extra training data and optimization loops, it provides an attractive and straightforward alternative to conventional FWI.
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Yu, Ran Gang, and Yong Tian. "Application of Hybrid Genetic Algorithm in Ground Stress Inversion." Applied Mechanics and Materials 90-93 (September 2011): 337–41. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.337.

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This paper propose genetic algorithm combined with neural networks, greatly improving the convergence rate of neural network aim at the disadvantage of the traditional BP neural network inversion method is easy to fall into local minimum and slow convergence.Finally, verified the feasibility and superiority of the above methods through the successful initial ground stress inversion of actual project.
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Živković, Ivan S., Predrag S. Stanimirović, and Yimin Wei. "Recurrent Neural Network for Computing Outer Inverse." Neural Computation 28, no. 5 (May 2016): 970–98. http://dx.doi.org/10.1162/neco_a_00821.

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Two linear recurrent neural networks for generating outer inverses with prescribed range and null space are defined. Each of the proposed recurrent neural networks is based on the matrix-valued differential equation, a generalization of dynamic equations proposed earlier for the nonsingular matrix inversion, the Moore-Penrose inversion, as well as the Drazin inversion, under the condition of zero initial state. The application of the first approach is conditioned by the properties of the spectrum of a certain matrix; the second approach eliminates this drawback, though at the cost of increasing the number of matrix operations. The cases corresponding to the most common generalized inverses are defined. The conditions that ensure stability of the proposed neural network are presented. Illustrative examples present the results of numerical simulations.
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Milić, I., and R. Gafeira. "Mimicking spectropolarimetric inversions using convolutional neural networks." Astronomy & Astrophysics 644 (December 2020): A129. http://dx.doi.org/10.1051/0004-6361/201936537.

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Context. Interpreting spectropolarimetric observations of the solar atmosphere takes much longer than the acquiring the data. The most important reason for this is that the model fitting, or “inversion”, used to infer physical quantities from the observations is extremely slow, because the underlying models are numerically demanding. Aims. We aim to improve the speed of the inference by using a neural network that relates input polarized spectra to the output physical parameters. Methods. We first select a subset of the data to be interpreted and infer physical quantities from corresponding spectra using a standard minimization-based inversion code. Taking these results as reliable and representative of the whole data set, we train a convolutional neural network to connect the input polarized spectra to the output physical parameters (nodes, in context of spectropolarimetric inversion). We then apply the neural network to the various other data, previously unseen to the network. As a check, we apply the referent inversion code to the unseen data and compare the fit quality and the maps of the inferred parameters between the two inversions. Results. The physical parameters inferred by the neural network show excellent agreement with the results from the inversion, and are obtained in a factor of 105 less time. Additionally, substituting the results of the neural network back in the forward model, shows excellent agreement between inferred and original spectra. Conclusions. The method we present here is very simple for implementation and extremely fast. It only requires a training data set, which can be obtained by inverting a representative subset of the observed data. Applying these (and similar) machine learning techniques will yield orders of magnitude acceleration in the routine interpretation of spectropolarimetric data.
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Mohamed, Islam A., Hamed Z. El-Mowafy, and Mohamed Fathy. "Prediction of elastic properties using seismic prestack inversion and neural network analysis." Interpretation 3, no. 2 (May 1, 2015): T57—T68. http://dx.doi.org/10.1190/int-2014-0139.1.

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The use of artificial intelligence algorithms to solve geophysical problems is a recent development. Neural network analysis is one of these algorithms. It uses the information from multiple wells and seismic data to train a neural network to predict properties away from the well control. Neural network analysis can significantly improve the seismic inversion result when the outputs of the inversion are used as external attributes in addition to regular seismic attributes for training the network. We found that integration of prestack inversion and neural network analysis can improve the characterization of a late Pliocene gas sandstone reservoir. For inversion, the input angle stacks was conditioned to match the theoretical amplitude-variation-with-offset response. The inversion was performed using a deterministic wavelet set. Neural network analysis was then used to enhance the [Formula: see text], [Formula: see text], and density volumes from the inversion. The improvement was confirmed by comparisons with logs from a blind well.
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Dissertations / Theses on the topic "Neural network inversion"

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Jakobsson, Henrik. "Inversion of an Artificial Neural Network Mapping by Evolutionary Algorithms with Sharing." Thesis, University of Skövde, Department of Computer Science, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-165.

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Inversion of the artificial neural network mapping is a relatively unexplored field of science. By inversion we mean that a search is conducted to find what input patterns that corresponds to a specific output pattern according to the analysed network. In this report, an evolutionary algorithm is proposed to conduct the search for input patterns. The hypothesis is that the inversion with the evolutionary search-method will result in multiple, separate and equivalent input patterns and not get stuck in local optima which possibly would cause the inversion to result in erroneous answer. Beside proving the hypothesis, the tests are also aimed at explaining the nature of inversion and how the result of inversion should be interpreted. At the end of the document a long list of proposed future work is suggested. Work, which might result in a deeper understanding of what the inversion means and maybe an automated analysis tool, based on inversion.

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Sopoco, Tara Helene. "A neural network technique for atmospheric inversion of WINDII and OSIRIS data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ57998.pdf.

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Sagiroglu, Serkan. "Adaptive Neural Network Applications On Missile Controller Design." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611106/index.pdf.

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In this thesis, adaptive neural network controllers are designed for a high subsonic cruise missile. Two autopilot designs are included in the study using adaptive neural networks, namely an altitude hold autopilot designed for the longitudinal channel and a directional autopilot designed for heading control. Aerodynamic coefficients are obtained using missile geometry
a 5-Degree of Freedom (5-DOF) simulation model is obtained, and linearized at a single trim condition. An inverted model is used in the controller. Adaptive Neural Network (ANN) controllers namely, model inversion controllers with Sigma-Pi Neural Network, Single Hidden Layer Neural Network and Background Learning implemented Single Hidden Layer Neural Network, are deployed to cancel the modeling error and are applied for the longitudinal and directional channels of the missile. This approach simplifies the autopilot designing process by combining a controller with model inversion designed for a single flight condition with an on-line learning neural network to account for errors that are caused due to the approximate inversion. Simulations are performed both in the longitudinal and directional channels in order to demonstrate the effectiveness of the implemented control algorithms. The advantages and drawbacks of the implemented neural network based controllers are indicated.
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Shahraeeni, Mohammad Sadegh. "Inversion of seismic attributes for petrophysical parameters and rock facies." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/4754.

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Prediction of rock and fluid properties such as porosity, clay content, and water saturation is essential for exploration and development of hydrocarbon reservoirs. Rock and fluid property maps obtained from such predictions can be used for optimal selection of well locations for reservoir development and production enhancement. Seismic data are usually the only source of information available throughout a field that can be used to predict the 3D distribution of properties with appropriate spatial resolution. The main challenge in inferring properties from seismic data is the ambiguous nature of geophysical information. Therefore, any estimate of rock and fluid property maps derived from seismic data must also represent its associated uncertainty. In this study we develop a computationally efficient mathematical technique based on neural networks to integrate measured data and a priori information in order to reduce the uncertainty in rock and fluid properties in a reservoir. The post inversion (a posteriori) information about rock and fluid properties are represented by the joint probability density function (PDF) of porosity, clay content, and water saturation. In this technique the a posteriori PDF is modeled by a weighted sum of Gaussian PDF’s. A so-called mixture density network (MDN) estimates the weights, mean vector, and covariance matrix of the Gaussians given any measured data set. We solve several inverse problems with the MDN and compare results with Monte Carlo (MC) sampling solution and show that the MDN inversion technique provides good estimate of the MC sampling solution. However, the computational cost of training and using the neural network is much lower than solution found by MC sampling (more than a factor of 104 in some cases). We also discuss the design, implementation, and training procedure of the MDN, and its limitations in estimating the solution of an inverse problem. In this thesis we focus on data from a deep offshore field in Africa. Our goal is to apply the MDN inversion technique to obtain maps of petrophysical properties (i.e., porosity, clay content, water saturation), and petrophysical facies from 3D seismic data. Petrophysical facies (i.e., non-reservoir, oil- and brine-saturated reservoir facies) are defined probabilistically based on geological information and values of the petrophysical parameters. First, we investigate the relationship (i.e., petrophysical forward function) between compressional- and shear-wave velocity and petrophysical parameters. The petrophysical forward function depends on different properties of rocks and varies from one rock type to another. Therefore, after acquisition of well logs or seismic data from a geological setting the petrophysical forward function must be calibrated with data and observations. The uncertainty of the petrophysical forward function comes from uncertainty in measurements and uncertainty about the type of facies. We present a method to construct the petrophysical forward function with its associated uncertainty from the both sources above. The results show that introducing uncertainty in facies improves the accuracy of the petrophysical forward function predictions. Then, we apply the MDN inversion method to solve four different petrophysical inverse problems. In particular, we invert P- and S-wave impedance logs for the joint PDF of porosity, clay content, and water saturation using a calibrated petrophysical forward function. Results show that posterior PDF of the model parameters provides reasonable estimates of measured well logs. Errors in the posterior PDF are mainly due to errors in the petrophysical forward function. Finally, we apply the MDN inversion method to predict 3D petrophysical properties from attributes of seismic data. In this application, the inversion objective is to estimate the joint PDF of porosity, clay content, and water saturation at each point in the reservoir, from the compressional- and shear-wave-impedance obtained from the inversion of AVO seismic data. Uncertainty in the a posteriori PDF of the model parameters are due to different sources such as variations in effective pressure, bulk modulus and density of hydrocarbon, uncertainty of the petrophysical forward function, and random noise in recorded data. Results show that the standard deviations of all model parameters are reduced after inversion, which shows that the inversion process provides information about all parameters. We also applied the result of the petrophysical inversion to estimate the 3D probability maps of non-reservoir facies, brine- and oil-saturated reservoir facies. The accuracy of the predicted oil-saturated facies at the well location is good, but due to errors in the petrophysical inversion the predicted non-reservoir and brine-saturated facies are ambiguous. Although the accuracy of results may vary due to different sources of error in different applications, the fast, probabilistic method of solving non-linear inverse problems developed in this study can be applied to invert well logs and large seismic data sets for petrophysical parameters in different applications.
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Shin, Yoonghyun. "Neural Network Based Adaptive Control for Nonlinear Dynamic Regimes." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7577.

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Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named composite model reference adaptive control is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of pseudo-control hedging techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.
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Gosal, Gurpreet Singh. "The use of Inverse Neural Networks in the Fast Design of Printed Lens Antennas." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32249.

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In this thesis the major objective is the implementation of the inverse neural network concept in the design of printed lens (transmitarray) antenna. As it is computationally extensive to perform full-wave simulations for entire transmitarray structure and thereafter perform optimization, the idea is to generate a design database assuming that a unit cell of the transmitarray is situated inside a 2D infinite periodic structure. This way we generate a design database of transmission coefficient by varying the unit cell parameters. Since, for the actual design, we need dimensions for each cell on the transmitarray aperture and to do this we need to invert the design database. The major contribution of this thesis is the proposal and the implementation of database inversion methodology namely inverse neural network modelling. We provide the algorithms for carrying out the inversion process as well as provide check results to demonstrate the reliability of the proposed methodology. Finally, we apply this approach to design a transmitarray antenna, and measure its performance.
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SOUZA, MARCELO GOMES DE. "DETERMINISTIC ACOUSTIC SEISMIC INVERSION USING ARTIFICIAL NEURAL NETWORKS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=34647@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
A inversão sísmica é o processo de transformar dados de Sísmica de Reflexão em valores quantitativos de propriedades petroelásticas das rochas. Esses valores, por sua vez, podem ser correlacionados com outras propriedades ajudando os geocientistas a fazer uma melhor interpretação que resulta numa boa caracterização de um reservatório de petróleo. Existem vários algoritmos tradicionais para Inversão Sísmica. Neste trabalho revisitamos a Inversão Colorida (Impedância Relativa), a Inversão Recursiva, a Inversão Limitada em Banda e a Inversão Baseada em Modelos. Todos esses quatro algoritmos são baseados em processamento digital de sinais e otimização. O presente trabalho busca reproduzir os resultados desses algoritmos através de uma metodologia simples e eficiente baseada em Redes Neurais e na pseudo-impedância. Este trabalho apresenta uma implementação dos algoritmos propostos na metodologia e testa sua validade num dado sísmico público que tem uma inversão feita pelos métodos tradicionais.
Seismic inversion is the process of transforming Reflection Seismic data into quantitative values of petroleum rock properties. These values, in turn, can be correlated with other properties helping geoscientists to make a better interpretation that results in a good characterization of an oil reservoir.There are several traditional algorithms for Seismic Inversion. In this work we revise Color Inversion (Relative Impedance), Recursive Inversion, Bandwidth Inversion and Model-Based Inversion. All four of these algorithms are based on digital signal processing and optimization. The present work seeks to reproduce the results of these algorithms through a simple and efficient methodology based on Neural Networks and pseudo-impedance. This work presents an implementation of the algorithms proposed in the methodology and tests its validity in a public seismic data that has an inversion made by the traditional methods.
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Thompson, Benjamin Berry. "Inversion and fast optimization using computational intelligence with applications to geoacoustics /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/5886.

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Artun, F. Emre. "Reservoir characterization using intelligent seismic inversion." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4185.

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Thesis (M.S.)--West Virginia University, 2005.
Title from document title page. Document formatted into pages; contains xii, 82 p. : ill. (some col.), maps (some col.). Includes abstract. Includes bibliographical references (p. 80-82).
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Hardarson, Gisli. "The Effects of Using Results from Inversion by Evolutionary Algorithms to Retrain Artificial Neural Networks." Thesis, University of Skövde, Department of Computer Science, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-411.

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The aim of inverting artificial neural networks (ANNs) is to find input patterns that are strongly classified as a predefined class. In this project an ANN is inverted by an evolutionary algorithm. The network is retrained by using the patterns extracted by the inversion as counter-examples, i.e. to classify the patterns as belonging to no class, which is the opposite of what the network previously did. The hypothesis is that the counter-examples extracted by the inversion will cause larger updates of the weights of the ANN and create a better mapping than what is caused by retraining using randomly generated counter-examples. This hypothesis is tested on recognition of pictures of handwritten digits. The tests indicate that this hypothesis is correct. However, the test- and training errors are higher when retraining using counter-examples, than for training only on examples of clean digits. It can be concluded that the counter-examples generated by the inversion have a great impact on the network. It is still unclear whether the quality of the network can be improved using this method.

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Book chapters on the topic "Neural network inversion"

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Zhang, Lin, and Mary Poulton. "Neural Network Inversion of EM39 Induction Log Data." In Geophysical Applications of Artificial Neural Networks and Fuzzy Logic, 231–49. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-0271-3_15.

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Wang, Jun. "A Generalized Recurrent Neural Network for Matrix Inversion." In ICANN ’93, 1084. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-2063-6_321.

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Hernández-Espinosa, Carlos, Mercedes Fernández-Redondo, and Mamen Ortiz-Gómez. "Inversion of a Neural Network via Interval Arithmetic for Rule Extraction." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003, 670–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_80.

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Adam, S. P., A. C. Likas, and M. N. Vrahatis. "Interval Analysis Based Neural Network Inversion: A Means for Evaluating Generalization." In Engineering Applications of Neural Networks, 314–26. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65172-9_27.

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Katragadda, G., J. Wallace, J. Lee, and S. Nair. "Neural Network Inversion for Thickness Measurements and Conductivity Profiling." In Review of Progress in Quantitative Nondestructive Evaluation, 781–88. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5947-4_102.

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Hernández-Espinosa, Carlos, Mercedes Fernández-Redondo, and Mamen Ortiz-Gómez. "Rule Extraction from a Multilayer Feedforward Trained Network via Interval Arithmetic Inversion." In Computational Methods in Neural Modeling, 622–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44868-3_79.

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Wang, Hong, Xianzhong Chen, and Jiangyun Li. "A Way to Understand the Features of Deep Neural Networks by Network Inversion." In Communications in Computer and Information Science, 284–95. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1922-2_20.

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Zhang, Yunong, Ke Chen, Weimu Ma, and Xiao-Dong Li. "MATLAB Simulation of Gradient-Based Neural Network for Online Matrix Inversion." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 98–109. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74205-0_12.

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Wang, Peng, and Shurong Li. "Resistivity Inversion Solving Based on a GA Optimized Convolutional Neural Network." In Lecture Notes in Electrical Engineering, 634–45. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8450-3_67.

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Sowmya, G., and P. Thangavel. "Convergence of a Finite-Time Zhang Neural Network for Moore–Penrose Matrix Inversion." In Advances in Intelligent Systems and Computing, 797–808. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8443-5_68.

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Conference papers on the topic "Neural network inversion"

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Alyousuf, Taqi, and Li Yaoguo. "Inversion Using Adaptive Physics-Based Neural Network: Application to Magnetotelluric Inversion." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22504-ea.

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Abstract In order to develop a geophysical earth model that is consistent with the measured geophysical data, two types of inversions are commonly used: a physics-based regularized inversion and a statistical-based machine learning inversion. In nonlinear problems, deterministic regularized inversion usually necessitates a good starting model to prevent possible local minima. The neural networks inversion requires large training data sets, which makes its generalizability limited. To overcome the limitation of physics-based regularized inversion and a statistical-based machine learning inversion and combine the benefits of both one inversion scheme, we developed a new physics-based neural network (PBNN) inversion algorithm. In our PBNN inversion, we include machine learning constraints into the regularized inversion using a coupling model objective function. The coupling objective function aims to minimize the difference between the recovered model through regularized inversion and the network-predicted reference model. We update the reference model using either a fully-trained network or an adaptively-trained network. The fully trained PBNN has the ability to collect all of the connections between data and models through a pseudoinverse operator. However, for geophysical inversion applications, particularly in the exploratory setting, this approach is unlikely to become feasible. Neural networks may struggle to extract complicated correlations from data when given insufficient data observations. The technique is impractical for practical usage due to the quantity of training required. In our novel adaptively PBNN algorithm, there is no need to prepare a training data set. At each iteration, the adaptively-PBNN algorithm retrains using the recovered models from the regularized inversion and their related data. The regularized inversion's recovered resistivity models are sufficient to guide neural network predictions towards the true model. One unique advantage is that the approach’s ability to fully use all intermediate models from the regularized inversion that were commonly discarded and apply them to the network training. When applied to synthetic MT data, we show that our technique is capable of reconstructing high-resolution resistivity models.
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Shukla, Manmohan, and B. K. Tripathi. "Inversion of Complex Neural Network." In 2018 8th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2018. http://dx.doi.org/10.1109/csnt.2018.8820289.

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Mao, Lingtao, Xiang Pan, and Yining Shen. "Geoacoustic Inversion Based on Neural Network." In OCEANS 2021: San Diego – Porto. IEEE, 2021. http://dx.doi.org/10.23919/oceans44145.2021.9705922.

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Priezzhev, Ivan, Dmitry Danko, and Uwe Strecker. "New-Age Kolmogorov Full-Function Neural Network KNN Offers High-Fidelity Reservoir Predictions via Estimation of Core, Well Log, Map and Seismic Properties." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207575-ms.

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Abstract Instead of relying on analytical functions to approximate property relationships, this innovative hybrid neural network technique offers highly adaptive, full-function (!) predictions that can be applied to different subsurface data types ranging from (1.) core-to-log prediction (permeability), (2.) multivariate property maps (oil-saturated thickness maps), and, (3.) petrophysical properties from 3D seismic data (i.e., hydrocarbon pore volume, instantaneous velocity). For each scenario a separate example is shown. In case study 1, core measurements are used as the target array and well log data serve training. To analyze the uncertainty of predicted estimates, a second oilfield case study applies 100 iterations of log data from 350 wells to obtain P10-P50-P90 probabilities by randomly removing 40% (140 wells) for validation purposes. In a third case study elastic logs and a low-frequency model are used to predict seismic properties. KNN generates a high level of freedom operator with only one (or more) hidden layer(s). Iterative parameterization precludes that high correlation coefficients arise from overtraining. Because the key advantage of the Kolmogorov neural network (KNN) is to permit non-linear, full-function approximations of reservoir properties, the KNN approach provides a higher-fidelity solution in comparison to other linear or non-linear neural net regressions. KNN offers a fast-track alternative to classic reservoir property predictions from model-based seismic inversions by combining (a) Kolmogorov's Superposition Theorem and (b) principles of genetic inversion (Darwin's "Survival of the fittest") together with Tikhonov regularization and gradient theory. In practice, this is accomplished by minimizing an objective function on multiple and simultaneous outputs from full-function (via look-up table) Kolmogorov neural network runs. All case studies produce high correlations between actual and predicted properties when compared to other stochastic or deterministic inversions. For instance, in the log to seismic prediction better (simulated) resolution of neural network results can be discerned compared to traditional inversion results. Moreover, all blind tests match the overall shape of prominent log curve deflections with a higher degree of fidelity than from inversion. An important fringe benefit of KNN application is the observed increase in seismic resolution that by comparison falls between the seismic resolution of a model-based inversion and the simulated resolution from seismic stochastic inversion.
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Protas, Eglen, Jose Bratti, Joel Gaya, Paulo Drews, and Silvia Botelho. "Understading Image Restoration Convolutional Neural Networks with Network Inversion." In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2017. http://dx.doi.org/10.1109/icmla.2017.0-156.

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Steriti, R., J. Coleman, and M. A. Fiddy. "A neural network based matrix inversion algorithm." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137607.

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Fabien-Ouellet, G. "Generating Seismic Low Frequencies with a Deep Recurrent Neural Network for Full Waveform Inversion." In First EAGE Conference on Seismic Inversion. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202037023.

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Das, Vishal, Ahinoam Pollack, Uri Wollner, and Tapan Mukerji. "Convolutional neural network for seismic impedance inversion." In SEG Technical Program Expanded Abstracts 2018. Society of Exploration Geophysicists, 2018. http://dx.doi.org/10.1190/segam2018-2994378.1.

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Varkonyi-Koczy, A. R., and A. Rovid. "Observer Based Iterative Neural Network Model Inversion." In Proceedings of the IEEE International Conference on Fuzzy Systems. IEEE, 2005. http://dx.doi.org/10.1109/fuzzy.2005.1452427.

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Lian Yan and D. J. Miller. "Time series prediction via neural network inversion." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.759923.

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