A seismic facies classification method based on the convolutional neural network and the probabilistic framework for seismic attributes and spatial classification

Abstract

In the early stage of oil and gas exploration, due to the lack of available drilling data, the automatic seismic facies classification technology mainly relies on the unsupervised clustering method combined with the seismic multiattribute. However, the clustering results are unstable and have no clear geologic significance. The supervised classification method based on manual interpretation can provide corresponding geologic significance, but there are still some problems such as the discrete classification results and low accuracy. To solve these problems, inspired by hyperspectral and spatial probability distribution technology, we have developed a classification framework called the probabilistic framework for seismic attributes and spatial classification (PFSSC). It can improve the continuity of the classification results by combining the classification probability and the spatial partial probability of the classifier output. In addition, the convolutional neural network (CNN) is a typical classification algorithm in deep learning. By convolution and pooling, we could use it to extract features of complex nonlinear objects for classification. Taking advantage of the combination of PFSSC and CNN, we could effectively solve the existing problems mentioned above in seismic facies classification. It is worth mentioning that we select seismic the multiattribute by maximal information coefficient (MIC) before the seismic facies classification. Finally, using the CNN-PFSSC and MIC methods, we can obtain high accuracy in the test set, reasonable continuity within the same seismic facies, clear boundaries between different seismic facies, and seismic facies classification results consistent with sedimentological laws.

Epistemic Uncertainty and Model Transparency in Rock Facies Classification Using Monte Carlo Dropout Deep Learning

“…However, the majority of these available methods operate on the foundation of mapping one input to one output and hence they do not output the uncertainty consequent to the model parameters (epistemic uncertainty) [11][14] [20]. Retraining such a Neural Network (NN) model results in a different prediction due to differing starting values of the parameters, indicating that the model's uncertainty is not acknowledged or taken into consideration [21].…”

Uncertainty Quantification in Classifying Complex Geological Facies Using Bayesian Deep Learning

“…Nevertheless, the aforementioned strategies are not accounted for the uncertainty in DL parameters (epistemic uncertainty), and basic activation functions are used to train just a fixed set of weights and biases [11][16] [22]. Due to various initialization values of neural parameters, retraining such a network yields a different prediction which signifies the fact that the neural network's uncertainty isn't explicitly stated or taken into account [23].…”

3-D seismic interpretation with deep learning: a brief introduction

“…In recent years, machine learning has thus been applied to numerous problems in seismic interpretation, such as: (1) salt detection (e.g., Guillen et al, 2015;Waldeland et al, 2018) , (2) fault detection (e.g., Zhang et al, 2014;Araya-Polo et al, 2017;Wu and Fomel, 2018) , (3) horizon mapping (e.g., Peters et al, 2019;Tschannen et al, 2020) , and (4) seismic facies classification (e.g., Qian et al, 2018;Wrona et al, 2018;Liu et al, 2019) . Most of these studies are built on recent advances in machine learning of multi-layered neural networks (i.e.…”