Elastic impedance based facies classification using support vector machine and deep learning

Nishitsuji, Yohei; Exley, Russell

doi:10.1111/1365-2478.12682

Cited by 19 publications

(11 citation statements)

References 22 publications

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“…1(b)], but it could also be included in other machine learning packages. The proposed implementation uses the Flipout gradient estimator [31] to minimize the loss function in (5), in which a stochastic forward pass is applied by sampling the posterior distribution of neural parameters. The stochastic gradient descent method is used to update the neural parameters, and its performance depends on the variance of the gradient [27].…”

Section: Theorymentioning

confidence: 99%

“…Inversion methods for these categorical/discrete variables can then be adopted to solve this classification problem based on seismic data. Several solutions have been proposed, including statistical learning methods, such as the support vector machine [5], the K -nearest neighbor [6], the self-organizing maps [7], the random forest [8], the independent component analysis [9], and the generative topographic mapping [10].…”

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confidence: 99%

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Bayesian Convolutional Neural Networks for Seismic Facies Classification

Feng

Balling

Grana

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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The seismic response of geological reservoirs is a function of the elastic properties of porous rocks, which depends on rock types, petrophysical features, and geological environments. Such rock characteristics are generally classified into geological facies. We propose to use the convolutional neural networks in a Bayesian framework to predict facies based on seismic data and quantify the uncertainty in the classification. A variational approach is adopted to approximate the posterior distribution of neural parameters that is mathematically intractable. The network is trained on labeled examples. The mean and the standard deviation of the distribution of neural parameters are randomly drawn from predefined Gaussian functions for the initialization, and are updated by minimizing the negative evidence lower bound. The facies classification is applied to seismic sections not included in the training data set. We draw multiple random samples from the trained variational posterior distribution to simulate an ensemble predictor and classify the most probable seismic facies. We implement the proposed network in the open-source library of TensorFlow Probability, for its convenience and flexibility. The applications show that the internal regions of the seismic sections are generally classified with higher confidence than their boundaries, as measured by the predictive entropy that is calculated based on a multiclass probability across the possible facies. A plain neural network is also applied for comparison, by assigning fixed values to the neural parameters using a classical backpropagation technique. The comparison shows consistent results; however, the proposed approach is able to assess the uncertainty in the predictions.

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Section: Theorymentioning

confidence: 99%

mentioning

confidence: 99%

Bayesian Convolutional Neural Networks for Seismic Facies Classification

Feng

Balling

Grana

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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“…Current approaches for HPO in geophysics mainly rely on either similar past experiences, a trial and error approach (Aleardi et al., 2022) or a grid search of one or two dimensions (Nishitsuji & Exley, 2018; Moghadas, 2020) of this hyperparameter space. Even when one attempts to automate the grid search for optimal hyperparameters, a significant amount of manual intervention and interpretation is usually involved.…”

Section: Introductionmentioning

confidence: 99%

Automating hyperparameter optimization in geophysics with Optuna: A comparative study

Almarzooq,

bin Waheed

2024

Geophysical Prospecting

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Deep learning has gained attraction amongst geophysicists for solving complex longstanding problems. Nevertheless, proper hyperparameter optimization methodologies remain critically underexplored in geophysical deep learning research. This paper attempts to first highlight the importance of hyperparameter optimization and then showcase two geophysics‐related deep learning examples where a grid search and Optuna framework (an automated optimization approach) were used for hyperparameter optimization. We consider two geophysical problems related to denoising seismic traces and the inversion of seismic traces for velocity information. In both cases, models created based on Optuna hyperparameter optimization were able to perform better than those created through grid search. The most significant advantage of Optuna, however, is having quantifiable results to justify the choice of a neural network architecture, depth and other hyperparameters rather than relying on inefficient methods of exploring the hyperparameter space such as a trial‐and‐error or grid search. This study aims to stimulate further exploration and adoption of these frameworks, pushing the boundaries of current deep learning based geophysical problem‐solving methodologies towards full automation.

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“…Zang (2006) normalized the petrofabric type, rock mass structure, joint development degree and other rock mass quality information by introducing the BP neural network principle to qualitatively forecast the rock mass quality. Nishitsuji and Exley (2019) compared the performance of support vector machine, deep learning and linear classifier, Bayesian classifier and other models in the classification of lithofacies types. They believed the deep learning method is more likely to become the dominant method for lithology classification in the future.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneity identification method for surrounding rock of large-section rock tunnel faces based on support vector machine

Wang

Tong

et al. 2023

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PurposeThe purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways.Design/methodology/approachRelying on the support vector machine (SVM)-based classification model, the nominal classification of blastholes and nominal zoning and classification terms were used to demonstrate the heterogeneity identification method for the surrounding rock of tunnel face, and the identification calculation was carried out for the five test tunnels. Then, the suggestions for local optimization of the support structures of large-section rock tunnels were put forward.FindingsThe results show that compared with the two classification models based on neural networks, the SVM-based classification model has a higher classification accuracy when the sample size is small, and the average accuracy can reach 87.9%. After the samples are replaced, the SVM-based classification model can still reach the same accuracy, whose generalization ability is stronger.Originality/valueBy applying the identification method described in this paper, the significant heterogeneity characteristics of the surrounding rock in the process of two times of blasting were identified, and the identification results are basically consistent with the actual situation of the tunnel face at the end of blasting, and can provide a basis for local optimization of support parameters.

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Elastic impedance based facies classification using support vector machine and deep learning

Cited by 19 publications

References 22 publications

Bayesian Convolutional Neural Networks for Seismic Facies Classification

Bayesian Convolutional Neural Networks for Seismic Facies Classification

Automating hyperparameter optimization in geophysics with Optuna: A comparative study

Heterogeneity identification method for surrounding rock of large-section rock tunnel faces based on support vector machine

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