Following the advancement of machine learning-based seismic feature classification techniques for complex reservoirs, the acquisition and analysis of reliable seismic samples involved in seismic facies analysis and network-based inversion have emerged as a current research hotspot in the field of intelligent seismic processing. Many investigations focus on the improvement of model classification algorithms and neural networks. However, creating and collecting labels for massive seismic data is highly time-consuming and laborious, and suffers from sample unreliability and category imbalance in the case of small-sample labels. To address such problems, a multi-scale and multi-label consistent PCA-LDA algorithm to learn a robust feature discriminative dictionary for classification is presented. In addition to the automatic use of multi-label from Well logs and core analysis, we associate multi-scale with Well trajectory locations to enrich sample information and enhance the reliability of the samples during 3D sample acquisition. More specifically, this article begins by proposing an approach for the automatic collection of multi-scale multi-label 3D post-stack seismic samples along the well track. Next, the multi-label sequence in the scan window is fed into the Boyer-Moore majority vote algorithm for sample segmentation (BMMV-SS), which constructs multi-label hierarchies for each sample. Then to enhance the model training bias due to small-sample label imbalance, we propose a novel label shuffling balanced (NLSB) strategy, which obtains a complete database through filling random unduplicated augmented training samples (spatial and frequency domain augmentation operations). Finally, the linear robustness decision-making space of PCA-LDA is obtained using the feature mapping space of PCA, as well as its visual representation. Experimental results on synthetic and field seismic data demonstrate that the robust feature extraction with a trustworthy and complete multi-scale multi-label sample database increases classification accuracy.

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The accurate prediction of coal structure is important to guide the exploration and development of coal reservoirs. Most prediction models are interpreted for a single sensitive coal seam, and the selection of sensitive parameters is correlated with the coal structure, but they ignore the interactions between different attributes. Part of it introduces the concept of the geological strength index (GSI) of coal rocks in order to achieve a multi-element macroscopic description and quantitative characterization of coal structure; however, the determination of coal structure involves some uncertainties among the properties of coal, such as lithology, gas content and tectonic fracture, due to their complex nature. Fuzzy inference systems provide a knowledge discovery process to handle uncertainty. The study shows that a type-2 fuzzy inference system (T2-FIS) with multi-attribute fusion is used to effectively fuse pre-stack and post-stack seismic inversion reservoir parameters and azimuthal seismic attribute parameters in order to produce more accurate prediction results for the Hengling block in the Shanxi area. The fuzzy set rules generated in this paper can provide a more reliable prediction of coal structure in the GSI system. The proposed system has been tested on various datasets and the results show that it is capable of providing reliable and high-quality coal structure predictions.