Identification of subsurface structures using electromagnetic data and shape priors

Tveit, Svenn; Bakr, Shaaban A.; Lien, Martha; Mannseth, Trond

doi:10.1016/j.jcp.2014.12.041

Cited by 11 publications

(7 citation statements)

References 53 publications

(81 reference statements)

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“…This mapping technique can increase the amount of information in the data set, particularly if the number of data is small [46]. The KPCA has already proven to be powerful as a preprocessing step for identification algorithms [47][48][49]. In this section, a brief description of KPCA for feature extraction is provided.…”

Section: Extracting Principal Components Based On Kpcamentioning

confidence: 99%

Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine

Yang

Bai

et al. 2018

Water

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Abstract:The stable operation of sewage treatment is of great significance to controlling regional water environment pollution. It is also important to forecast the inlet water quality accurately, which may ensure the purification efficiency of sewage treatment at a low cost. In this paper, a combined kernel principal component analysis (KPCA) and extreme learning machine (ELM) model is established to forecast the inlet water quality of sewage treatment. Specifically, KPCA is employed for feature extraction and dimensionality reduction of the inlet wastewater quality and ELM is utilized for the future inlet water quality forecasting. The experimental results indicated that the KPCA-ELM model has a higher accuracy than the other comparison PCA-ELM model, ELM model, and back propagation neural network (BPNN) model for forecasting COD and BOD concentration of the inlet wastewater, with mean absolute error (MAE) values of 2.322 mg/L and 1.125 mg/L, mean absolute percentage error (MAPE) values of 1.223% and 1.321%, and root mean square error (RMSE) values of 3.108 and 1.340, respectively. It is recommended from this research that the method may provide a reliable and effective reference for forecasting the water quality of sewage treatment.

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Section: Extracting Principal Components Based On Kpcamentioning

confidence: 99%

Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine

Yang

Bai

et al. 2018

Water

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show abstract

“…KPCA, as a novel nonlinear extension of PCA, extracts the principal exponents by using the kernel method. Although the actual meaning of the extracted principal components is not clear, KPCA has been already proven powerful as a preprocessing step for identification algorithms (Chen et al 2008;Mercer et al 2011;Tveit et al 2015). In this section, we provide a brief description of KPCA for feature extraction.…”

Section: Feature Selectionmentioning

confidence: 99%

A novel hybrid KPCA and SVM with PSO model for identifying debris flow hazard degree: a case study in Southwest China

Qian

Chen

Xiang³

et al. 2016

Environ Earth Sci

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A novel support vector machine (SVM) model that combines kernel principal component analysis (KPCA) with particle swarm optimization (PSO) is proposed for identifying debris flow hazard degree. The proposed model consists of three stages. First, KPCA is used to extract nonlinear feature information and solve the linear correlation of input data. Second, PSO is implemented to optimize the selection of the penalty factor and the kernel function parameter of the SVM classifier in the solution space. This optimization can prevent blindness in parameter selection and improve the identification accuracy of the model. Finally, the extracted new features and the optimal parameters for the SVM classifier are employed to predict the test data. Experimental results show that the proposed model can more effectively select discriminating input features and achieve higher identification accuracy than other hybrid models. Therefore, the proposed model exhibits high potential to become a useful tool for identifying debris flow hazard degree in limited samples.

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“…While the joint-inversion techniques described above aim to utilize complementary data types in a single inversion process, so-called cooperative inversion techniques [55] aim to invert the data types in separate steps, with the resulting model from inversion of one data type acting as starting model or constraint for the subsequent inversion of another data type. Examples of cooperative inversion techniques can be found in [75,76], where interpreted seismic inversion results are used as structural prior information for CSEM inversion, and in [17,40,70], where inversion of each data type is done in sequence and, in some cases, iterated. Exchanging information between geophysical models in the disparate inversion sequences can be challenging, especially if the spatial resolutions of the data types are different.…”

Section: Introductionmentioning

confidence: 99%

“…We will apply a model-based parametrization to represent the unknown functions in the inversions. The particular model-based parameterization applied here (see, e.g., [7,76]) is based on the level-set framework, facilitating representation of region boundaries without a priori restrictions on their shapes. It is therefore well suited to represent the boundaries of the images of a large-scale CO 2 plume in the respective geophysical domains, that is, in the electric conductivity, density, and seismic velocity.…”

Section: Introductionmentioning

confidence: 99%

Combining CSEM or gravity inversion with seismic AVO inversion, with application to monitoring of large-scale CO2 injection

et al. 2020

Self Cite

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A sequential inversion methodology for combining geophysical data types of different resolutions is developed and applied to monitoring of large-scale CO 2 injection. The methodology is a two-step approach within the Bayesian framework where lower resolution data are inverted first, and subsequently used in the generation of the prior model for inversion of the higher resolution data. For the application of CO 2 monitoring, the first step is done with either controlled source electromagnetic (CSEM) or gravimetric data, while the second step is done with seismic amplitude-versus-offset (AVO) data. The Bayesian inverse problems are solved by sampling the posterior probability distributions using either the ensemble Kalman filter or ensemble smoother with multiple data assimilation. A model-based parameterization is used to represent the unknown geophysical parameters: electric conductivity, density, and seismic velocity. The parameterization is well suited for identification of CO 2 plume location and variation of geophysical parameters within the regions corresponding to inside and outside of the plume. The inversion methodology is applied to a synthetic monitoring test case where geophysical data are made from fluid-flow simulation of large-scale CO 2 sequestration in the Skade formation. The numerical experiments show that seismic AVO inversion results are improved with the sequential inversion methodology using prior information from either CSEM or gravimetric inversion.

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Identification of subsurface structures using electromagnetic data and shape priors

Cited by 11 publications

References 53 publications

Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine

Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine

A novel hybrid KPCA and SVM with PSO model for identifying debris flow hazard degree: a case study in Southwest China

Combining CSEM or gravity inversion with seismic AVO inversion, with application to monitoring of large-scale CO2 injection

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