Automated input attribute weighting for unsupervised seismic facies analysis

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.

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Towards understanding common features between natural and seismic images

Shafiq

Prabhushankar

et al. 2018

SEG Technical Program Expanded Abstracts 2018

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In this paper, we propose an unsupervised learning framework that aims at evaluating the applicability of the broad domain knowledge from natural images and videos in assisting seismic interpretation, such as seismic attributes, structural automation, and seismic image processing. Specifically, we propose a novel approach based on a data-driven sparse autoencoder architecture that can automatically recognize and extract salient geologic features from unlabeled 3D seismic volumes. It is superior in learning sparse features from natural images, which is not limited by the lack of labeled seismic images. By developing models based on prevalent features in both domains, we can not only automate the process of seismic interpretation but also develop new seismic attributes that highlight areas of interest in seismic sections and convey the most useful information in a compact manner. We show that the proposed approach can effectively detect salient areas within real and synthetic seismic datasets. The experimental results demonstrate the potential of the proposed method in highlighting important geological structures such as horizons, faults, salt domes, and seismic reflections at different orientations and can be effectively used for computer-aided extraction of other geologic features as well.

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Automated input attribute weighting for unsupervised seismic facies analysis

Cited by 4 publications

References 13 publications

Seismic facies analysis using machine learning techniques: a review and case study

Seismic facies analysis using machine learning techniques: a review and case study

Seismic facies analysis based on deep convolutional embedded clustering

Towards understanding common features between natural and seismic images

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