Quantitative thickness prediction of tectonically deformed coal using Extreme Learning Machine and Principal Component Analysis: a case study

Thickness Distribution Prediction for Tectonically Deformed Coal with a Deep Belief Network: A Case Study

“…In practical applications, the number of seismic attributes is huge, and the information on seismic attributes is redundant [11]. Consequently, it is compulsory to reduce the dimensionality of seismic attributes before training and prediction, i.e., finding meaningful low-dimensional structures hidden in the high-dimensional attributes.…”

“…In the literature, principal component analysis (PCA) has been used to explore high-dimensional data and to reduce information redundancy for seismic attributes, geophysical logging, and biotechnology [10,[22][23][24]. The main target of PCA is to represent the most variations of input data with fewer linearly independent components, where the most used method may be the singular value decomposition algorithm [11,25]. By calculating the eigenvalues of the input matrix and sorting them as λ 1 ≥ λ 2 ≥ .…”

“…≥ λ n , the corresponding eigenvectors are the principal components (PCs). Conventionally, the first three to five PCs account for over 85% variance and can represent the variations of the input seismic attributes [11].…”

“…Seismic attributes are useful input for reservoir prediction. Among them, the curvature is related to structural development, the amplitude is related to impedance contrast and bed thickness, frequency and bandwidth are related to bed thickness and burial depth, and sweetness and spectral decomposition are related to bed lithology [8][9][10][11]. In the literature, most studies have been focusing on the forecasting of TDC distribution with seismic attribute input [12][13][14].…”

“…Machine learning technologies, including a fuzzy neural network (FNN), support vector machines (SVM), and extreme learning machine (ELM), have been used to predict TDC thickness and have achieved valuable results [11,16,17]. However, they have not found wide applications in the coal mining industry due to the limitations in learning speed and modeling capacity.…”