Visualization Analysis of Seismic Facies Based on Deep Embedded SOM

Liu, Zhege; Cao, Junxing; Chen, Shuna; Lu, Yujia; Tan, Feng

doi:10.1109/lgrs.2020.3003585

Cited by 9 publications

(2 citation statements)

References 10 publications

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“…In contrast, data-driven methods are aimed at automatically learning local patterns in seismic data without prior knowledge or assumptions, which builds upon the learning capability of the autoencoder [21]- [26], or recurrent network [27]- [29]. Thereafter, isolated learning-based SFA achieves SFA via multifarious feature clustering algorithms such as centroid-based clustering [30]- [35], [36], probabilistic model clustering [37]- [39], and spectral clustering [11], [40], [41]. However, due to the isolation between feature extraction and clustering, the guidance of clustering loss is ineffective for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Seismic Facies Deep Clustering Via Lognormal Mixture-Based Variational Autoencoder

Hua,

Qian,

Zhang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Introductionmentioning

confidence: 99%

Unsupervised Seismic Facies Deep Clustering Via Lognormal Mixture-Based Variational Autoencoder

Hua,

Qian,

Zhang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Ahmed et al [15] analyzed the problems of the K-means algorithm, such as the issues of initialization, inability to handle data with mixed types of features, and introduced the relevant research to solve the problems. SOM is an unsupervised network proposed by Kohonen [16], which has been widely used in many fields of data clustering [17][18][19]. SOM has a strong learning ability but cannot provide precise clustering results, and the convergence speed is slow [14,20].…”

Section: Introductionmentioning

confidence: 99%

Seismic Facies Analysis Using the Multiattribute SOM-K-Means Clustering

Zhu

Chen

Ren

et al. 2022

Computational Intelligence and Neuroscience

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An accurate seismic facies analysis (SFA) can provide insight into the subsurface sedimentary facies and has guiding significance for geological exploration. Many machine learning algorithms, including unsupervised, supervised, and deep learning algorithms, have been developed successfully for SFA over the past decades. However, SFA and facies classification are still challenging tasks due to the complex characteristics of geological and seismic data. A multiattribute SOM-K-means clustering algorithm, which implements a two-stage clustering by using multiple geological attributes, is proposed and applied for SFA. The proposed algorithm can effectively extract complementary features from the multiple attribute volumes and comprehensively use the different attributes to improve the recognition ability of seismic facies. Experimental results show that the proposed algorithm improves clustering accuracy and can be used as an effective and powerful tool for SFA.

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Application of Manifold Recognition Target Identification Method in Seismic Exploration

Zhao,

Lei,

et al. 2024

Lecture Notes in Computer Science

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Visualization Analysis of Seismic Facies Based on Deep Embedded SOM

Cited by 9 publications

References 10 publications

Unsupervised Seismic Facies Deep Clustering Via Lognormal Mixture-Based Variational Autoencoder

Unsupervised Seismic Facies Deep Clustering Via Lognormal Mixture-Based Variational Autoencoder

Seismic Facies Analysis Using the Multiattribute SOM-K-Means Clustering

Application of Manifold Recognition Target Identification Method in Seismic Exploration

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