Mapping of rock types using a joint approach by combining the multivariate statistics, self-organizing map and Bayesian neural networks: an example from IODP 323 site

Karmakar, Mampi; Maiti, Saumen; Singh, Amrita; Ojha, Maheswar; Maity, Bhabani Sankar

doi:10.1007/s11001-017-9327-2

Cited by 17 publications

(4 citation statements)

References 30 publications

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“…In the East Sea, there are also similar studies which were proposed by Karmakar et al (2018); Tse et al (2019). The results obtained by these authors show the effectiveness when applying these ML and AI techniques to solve geological structure interpretation missions.…”

Section: _____________________ *supporting

confidence: 57%

Estimation of shale volume from well logging data using Artificial Neural Network

Vietnam¹

2021

JMES

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The existence of shale has a major effect on reservoir quality because it reduces the rock’s both the porosity and permeability. There are several types of shale, and they can be distributed in the sand in four different ways: laminated, structural, dispersed, or any combination of these. Each of them has various features and physical properties. Therefore, shale volume estimation is one of the most important and challengin tasks to be solved information evaluation. There are many equations proposed to calculate shale volume from Gamma - ray log; however, none of them could be considered the best method that can be applied to all case studies. This study aims to propose a new approach to estimate shale volume from well - logging data. Gamma - ray and other logs were used as input data for an artificial neural network (ANN) to predict the shale volume. We apply this technique to the 1143 data set of the ocean drilling program (ODP) in the East Sea. The authors compared the result to core data and recognized that utilization of several logs and ANN gives a better estimation than conventional methods (more accurate and can reflect the trend of actual shale volume).

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Section: _____________________ *supporting

confidence: 57%

Estimation of shale volume from well logging data using Artificial Neural Network

Vietnam¹

2021

JMES

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“…In an alternative formulation, mixture density networks (MDNs) output a probability distribution that is defined by a sum of analytic pdfs called kernels, such as Gaussian distributions, and can be trained to map data to corresponding posterior pdfs (Bishop, 2006). MDNs have been applied to surface wave dispersion inversion (Cao et al., 2020; Earp et al., 2020; Meier et al., 2007a, 2007b), two‐dimensional (2D) travel time tomography (Earp & Curtis, 2020), petrophysical inversion (Shahraeeni & Curtis, 2011; Shahraeeni et al., 2012), earthquake source parameter estimation (Käufl et al., 2014, 2015), Earth's radial seismic structure inversion (de Wit et al., 2013), pore pressure prediction (Karmakar & Maiti, 2019), mapping of lithology (Karmakar et al., 2018), wind prediction (Men et al., 2016), acoustic‐articulatory inversion (Richmond, 2007) and nuclei detection (Koohababni et al., 2018). However MDNs become difficult to train in high dimensionality because of numerical instability, and they suffer from mode collapse which means that some modes (maxima) of the posterior pdf are missing in the results (Cui et al., 2019; Curro & Raquet, 2018; Hjorth & Nabney, 1999; Makansi et al., 2019; Rupprecht et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Geophysical Inversion Using Invertible Neural Networks

Zhang

Curtis

2021

JGR Solid Earth

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Geoscientists build models of the subsurface to understand properties and processes in the Earth's interior. The models are usually parameterized in some way, so to constrain the models, we must solve a parameter estimation problem. Data are recorded which provide constraints, together with prior knowledge. However, since the physical relationships between parameters and data usually predict data given the parameters (known as the forward calculation) but not the reverse, the solution must be found using inverse theory.Geophysical inverse problems usually have nonunique solutions due to noise in the data, to nonlinearity of the physical relationships between model parameters and data, and to fundamentally unconstrained combinations of parameters. Uncertainties in parameter estimates must therefore be quantified to interpret inversion results correctly. Unfortunately, estimating uncertainty in nonlinear inverse problems can be computationally expensive, and the cost increases both with the number of parameters, and with the computational cost of the forward calculation. In this study, we therefore solve two different types of seismic tomography problems which each have fewer than 100 parameters, and have relatively rapid forward functions (each evaluation takes on the order of seconds). This allows us to evaluate solutions sufficiently accurately to thoroughly test a new method of Geophysical inversion.Geophysical inverse problems are traditionally solved by linearizing (approximating) the nonlinear physics, and using optimization methods which seek a model that minimizes the misfit between observed and predicted data (Aki & Lee, 1976;Dziewonski & Woodhouse, 1987;Iyer & Hirahara, 1993). However, despite their wide applications, linearized procedures cannot produce accurate estimates of uncertainty because data uncertainty distributions can deviate substantially from analytic forms of probability such as Gaussians or Uniform distributions that can be propagated through linearized methods efficiently, and since the distribution of possible models post inversion can be strongly affected by nonlinearity in the physics (Bo-

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“…These methods are greatly influenced by noise data, resulting in the rapid reduction of classification accuracy. Some post-processing methods are needed to optimize the classification effect when drawing the preliminary seabed sediment map, such as Bayesian technology [21]. Additionally, the supervised classification techniques of seabed sediments include neural network (NN) [22], multilayer perceptron [23], random forest (RF) [24] and support vector machine (SVM) [25] approaches.…”

Section: Introductionmentioning

confidence: 99%

Sediment Classification of Acoustic Backscatter Image Based on Stacked Denoising Autoencoder and Modified Extreme Learning Machine

et al. 2020

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Acoustic backscatter data are widely applied to study the distribution characteristics of seabed sediments. However, the ghosting and mosaic errors in backscatter images lead to interference information being introduced into the feature extraction process, which is conducted with a convolutional neural network or auto encoder. In addition, the performance of the existing classifiers is limited by such incorrect information, meaning it is difficult to achieve fine classification in survey areas. Therefore, we propose a sediment classification method based on the acoustic backscatter image by combining a stacked denoising auto encoder (SDAE) and a modified extreme learning machine (MELM). The SDAE is used to extract the deep-seated sediment features, so that the training network can automatically learn to remove the residual errors from the original image. The MELM model, which integrates weighted estimation, a Parzen window and particle swarm optimization, is applied to weaken the interference of mislabeled samples on the training network and to optimize the random expression of input layer parameters. The experimental results show that the SDAE-MELM method greatly reduces mutual interference between sediment types, while the sediment boundaries are clear and continuous. The reliability and robustness of the proposed method are better than with other approaches, as assessed by the overall classification effect and comprehensive indexes.

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Mapping of rock types using a joint approach by combining the multivariate statistics, self-organizing map and Bayesian neural networks: an example from IODP 323 site

Cited by 17 publications

References 30 publications

Estimation of shale volume from well logging data using Artificial Neural Network

Estimation of shale volume from well logging data using Artificial Neural Network

Bayesian Geophysical Inversion Using Invertible Neural Networks

Sediment Classification of Acoustic Backscatter Image Based on Stacked Denoising Autoencoder and Modified Extreme Learning Machine

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