Coal Seam Roof and Floor Lithology Prediction for Underground Coal Gasification Based on Deep Residual Shrinkage Network

ABSTRACT Lithology identification is a crucial task in coal underground gasification projects, serving as a prerequisite for ensuring the safe operation of these endeavors. The inherent complexity in the relationship between logging parameters and lithological compositions creates ambiguity, leading to biases in traditional logging interpretation methodologies. We introduce a lithological prediction model, the deep residual shrinkage network (DRSN), which integrates residual networks, attention mechanisms, and soft‐thresholding strategies. This network mitigates the gradient vanishing issue common in traditional neural networks and enhances the model's focus on essential features, thereby improving its ability to capture critical information. Acoustic, bulk density, neutron, gamma, and deep resistivity logs are used as inputs, with lithology as the output. A comparative analysis between the DRSN and other newer lithological prediction models is conducted. Blind well testing results demonstrate the superior performance of the DSRN, with higher Accuracy, Precision, Recall, and F1 Scores of 0.8221, 0.7198, 0.8004, and 0.7465, respectively. This study provides a novel and rapid method for lithology evaluation of strata in the early stages of underground coal gasification

PERFORMANCE EVALUATION OF THE INFERENCE STRUCTURE IN EXPERT SYSTEM ABSTRACT

There are a variety of factors that affect the performance of an expert system. This paper presents an evaluating method focusing on the complexity, costs and efficiency of inference structure in a rule / Knowledge-source based expert system. The performance measuring factors proposed here, when fedback to Knowledge engineers, can help them gain a quantitative understanding of reasoning performance of Knowledge acquired at the (early) stages of the development life cycle, and enable them to make a numeric comparison and a choice among several structures, or have some refinements. This paper also presents some application examples of this evaluating method and describes an evaluating facility based on it.

Study on the Performances of an Approximating Spline Filter Based on the ADRF Function in Surface Roughness Evaluation

Isotropy is an important feature of an area filter in the three-dimensional surface roughness evaluation. First, the transmission characteristic deviation between the approximating spline filter and the Gaussian filter is reduced by cascading approximating. Second, the approximating spline filter is superimposed on the orthogonal direction to obtain an isotropic areal filter. Then, four direct methods for the solving approximating spline matrix are applied. Based on the profile filtering and areal filtering, the computational efficiency and accuracy are compared. The experimental results show that the improved square root method (LDLT decomposition) combines both computational efficiency and filtering precision, and is a good choice for solving the approximating spline matrix. Finally, six kinds of robust approximating spline filters are constructed. Taking the output value of robust Gaussian regression filter (RGRF) as reference, and the honing profile and step surface with deep valley characteristics were used as test surfaces to compare their robustness and iteration time. The experimental results show that the approximating spline filter based on the ADRF function has the shortest iteration times, while its roughness is close to the robust Gaussian regression filter.</jats:p

Study on the Performances of an Approximating Spline Filter Based on the ADRF Function in Surface Roughness Evaluation

Isotropy is an important feature of an area filter in the three-dimensional surface roughness evaluation. First, the transmission characteristic deviation between the approximating spline filter and the Gaussian filter is reduced by cascading approximating. Second, the approximating spline filter is superimposed on the orthogonal direction to obtain an isotropic areal filter. Then, four direct methods for the solving approximating spline matrix are applied. Based on the profile filtering and areal filtering, the computational efficiency and accuracy are compared. The experimental results show that the improved square root method (LDLT decomposition) combines both computational efficiency and filtering precision, and is a good choice for solving the approximating spline matrix. Finally, six kinds of robust approximating spline filters are constructed. Taking the output value of robust Gaussian regression filter (RGRF) as reference, and the honing profile and step surface with deep valley characteristics were used as test surfaces to compare their robustness and iteration time. The experimental results show that the approximating spline filter based on the ADRF function has the shortest iteration times, while its roughness is close to the robust Gaussian regression filter