Seismic facies analysis based on deep convolutional embedded clustering

Abstract

Seismic facies classification takes a two-step approach: attribute extraction and seismic facies analysis by using clustering algorithms, sequentially. In general, it is clear that the choice of feature extraction is critical for successful seismic facies analysis. However, the choice of features is customarily determined by the seismic interpreters, and so the clustering result is affected by the difference in the seismic interpreters’ experience levels. It becomes challenging to extract features and identify seismic facies simultaneously. We have introduced deep convolutional embedded clustering (DCEC), which aims to simultaneously learn feature representations and cluster assignments by using deep neural networks. Our method learns mapping from the data space to a lower dimensional feature space in which it iteratively optimizes a clustering objective by building a specific loss function. We apply the method to the Modified National Institute of Standards and Technology (MNIST) data, geophysical model data, and field seismic data. In the MNIST data, the DCEC method shows better latent space of clustering results than traditional clustering methods. In the geophysical model data, the accuracy of waveform classification based on DCEC method is higher than traditional clustering methods. The results from the seismic data demonstrate that selection of input data and method has an important effect on the clustering result. In addition, our method is helpful for improving the resolution of seismic facies edges and offers the richer depositional information than the traditional clustering methods.

Dynamic subwindow matching: A new similarity measure for seismic facies analysis

“…However, the proposed method is based on a small number of labelled samples for seismic facies analysis, while the existing deep learning‐based methods are either unsupervised methods without labels or supervised methods relying on a large number of labels. To demonstrate the validity of analysing seismic facies based on such a small number of interpreted samples (only eight labelled data in the application of real data), we use deep learning methods (Duan et al ., 2019) to perform unsupervised seismic facies analysis. The result is shown in Figure 14.…”

“…Further, Duan et al . (2019) introduced deep convolutional embedded clustering to learn feature and cluster assignments simultaneously.…”

Deep Learning for Geophysics: Current and Future Trends

“…All rights reserved. extraction, and then a clustering method, such as K-means, is used for unsupervised clustering (Duan et al 2019, He et al 2018, Qian et al 2018. Clustering refers to grouping similar attributes in an unsupervised manner.…”

“…If the attributes cannot be directly computed from the seismic data, a DNN can work in a cascaded way (Das & Mukerji, 2020). If labels are not available, CAE is used for feature extraction, and then a clustering method, such as K-means, is used for unsupervised clustering (Duan et al, 2019;He et al, 2018;Qian et al, 2018). Clustering refers to grouping similar attributes in an unsupervised manner.…”

Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning

“…where the x i (j) is the distance indicator of the n dataset points and c j is the representation of K centroids. Usually, the distance is measured with the Euclidean distance [38]. The K-means algorithm composed with the following four steps [39]:…”