scholarly journals Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements

2019 ◽  
Vol 9 (6) ◽  
pp. 1144 ◽  
Author(s):  
Ailing Qi ◽  
Wenhui Kang ◽  
Guangming Zhang ◽  
Haijun Lei
Keyword(s):  
Coal Seam ◽  
Gaussian Process ◽  
Prediction Accuracy ◽  
Transition Probability ◽  
Seam Thickness ◽  
Superior Performance ◽  
Support Vector ◽  
Structural Elements ◽  
Thickness Prediction ◽  
Coal Seam Thickness

Coal seam thickness prediction is crucial in coal mine design and coal mining. In order to improve the prediction accuracy, an improved Kriging interpolation method on the basis of efficient data and Radial Basis Function (RBF-Kriging) is firstly proposed to interpolate the cutting data that is obtained in pre-mining, especially at the edge of the geological surface of coal seam by taking into account the spatial structure and the efficient spatial range, ensuring the integrity of the edge data during the movement of structural elements. Subsequently, a structural element transition probability based Gaussian process progression (STTP-GPR) method is proposed to predict the coal seam thickness from the interpolated coal seam data. The experimental results demonstrated that the proposed STTP-GPR method has superior performance in coal seam thickness prediction. The average absolute error of thickness prediction for thin coal seams is 0.025 m, which significantly improves the prediction accuracy in comparison to the existing back propagation (BP) neural networks, support vector machine, and Gaussian process regression methods.

Study and Application of Seismic Attributes on Coal Seam Thickness Prediction

2011 ◽  
Vol 138-139 ◽  
pp. 492-497
Author(s):  
Chong Hui Suo ◽  
Suo Liang Chang ◽  
Shi Mi Peng ◽  
Ru Tai Duan
Keyword(s):  
Coal Seam ◽  
Seismic Attributes ◽  
Regression Method ◽  
Seam Thickness ◽  
Thickness Prediction ◽  
Single Attribute ◽  
Proportional Relationship ◽  
Nonlinear Regression Method ◽  
Coal Seam Thickness ◽  
The Relationship

Seismic wave contain more information about the sediments. The seismic attributes has some relationship with the coal seam thickness. Through analysis of the wedge-shaped coal seam’s seismic attributes, when the seam thickness is less than the tuning thickness, there is proportional relationship between the amplitude value and seam thickness; while the apparent time difference between the wave peak and trough is not sensitive. When the coal seam thickness is above the tuning thickness, the conclusion is opposite. As a result, it’s difficult to predict the coal seam thickness only by using one single attribute. Therefore, we use the nonlinear-regression method to analyze the relationship between the seam thickness and both attributes, and get a method to predict the coal seam thickness. With the method we predict the coal seam thickness in a coal mine and get a perfect result.

Study on seismic spectral attribute for coal seam thickness prediction

2017 ◽  
Author(s):  
Zhongbin Tian ◽  
Jianqing Wang ◽  
Xiaodong Yang ◽  
Youyi Shen
Keyword(s):  
Coal Seam ◽  
Seam Thickness ◽  
Thickness Prediction ◽  
Coal Seam Thickness

Estimation of burnt coal seam thickness in central Borneo using JERS-1 SAR data

2003 ◽  
Vol 24 (4) ◽  
pp. 879-884 ◽  
Author(s):  
J. Tetuko S. S. ◽  
R. Tateishi ◽  
N. Takeuchi
Keyword(s):  
Coal Seam ◽  
Seam Thickness ◽  
Sar Data ◽  
Coal Seam Thickness

scholarly journals Performance Analysis of Natural γ-Ray Coal Seam Thickness Sensor and Its Application in Automatic Adjustment of Shearer’s Arms

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zengfu Yang ◽  
Zengcai Wang ◽  
Ming Yan
Keyword(s):  
Mathematical Model ◽  
Coal Seam ◽  
Seam Thickness ◽  
Mining Machine ◽  
Practical Advantage ◽  
Rock Interface ◽  
Coal Rock ◽  
Coal Seam Thickness ◽  
The Mathematical Model ◽  
The Relationship

The technology of coal-rock interface recognition is the core of realizing the automatic heightening technology of shearer’s rocker. Only by accurately and quickly identifying the interface of coal and rock can we realize the fully automatic control of shearer. As the only one used in the actual detection of coal mining machine drum cutting coal seam after the thickness of the remaining coal seam detection method, natural γ-ray has a very practical advantage. Based on the relationship between the attenuation of the natural γ-ray passing through the coal seam and the thickness of the coal seam, the mathematical model of the attenuation of the natural γ-ray penetrating coal seam is established. By comparing the attenuation intensity of γ-ray with or without brackets, it is verified that the hydraulic girders will absorb some natural γ-rays. Finally, this paper uses the ground simulation experiment and the field experiment to verify the correctness of the mathematical model and finally develop the natural γ-ray seam thickness sensor. The sensor has the function of indicating the thickness of the coal seam, measuring the natural γ-ray intensity, and storing and processing the data.

Estimation of municipal waste generation of Turkey using socio-economic indicators by Bayesian optimization tuned Gaussian process regression

2020 ◽  
Vol 38 (8) ◽  
pp. 840-850 ◽  
Author(s):  
Zeynep Ceylan
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Kernel Functions ◽  
Machine Learning Algorithms ◽  
Economic Indicators ◽  
Bayesian Optimization ◽  
Superior Performance ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector

Accurate estimation of municipal solid waste (MSW) generation has become a crucial task in decision-making processes for the MSW planning and management systems. In this study, the Gaussian process regression (GPR) model tuned by Bayesian optimization was used to forecast the MSW generation of Turkey. The Bayesian optimization method, which can efficiently optimize the hyperparameters of kernel functions in the machine learning algorithms, was applied to reduce the computation redundancy and enhance the estimation performance of the models. Four socio-economic indicators such as population, gross domestic product per capita, inflation rate, and the unemployment rate were used as input variables. The performance of the Bayesian GPR (BGPR) model was compared with the multiple linear regression (MLR) and Bayesian support vector regression (BSVR) models. Different performance measures such as mean absolute deviation (MAD), root mean square error (RMSE), and coefficient of determination (R2) values were used to evaluate the performance of the models. The exponential-GPR model tuned by Bayesian optimization showed superior performance with minimum MAD (0.0182), RMSE (0.0203), and high R2 (0.9914) values in the training phase and minimum MAD (0.0342), RMSE (0.0463), and high R2 (0.9841) values in the testing phase. The results of this study can help decision-makers to be aware of social-economic factors associated with waste management and ensure optimal usage of their resources in future planning.

Comprehensive prediction of coal seam thickness by using in-seam seismic surveys and Bayesian kriging

2019 ◽  
Vol 67 (3) ◽  
pp. 825-836 ◽  
Author(s):  
Mengbo Zhu ◽  
Jianyuan Cheng ◽  
Weixiong Cui ◽  
Hui Yue
Keyword(s):  
Coal Seam ◽  
Seam Thickness ◽  
Seismic Surveys ◽  
Bayesian Kriging ◽  
Coal Seam Thickness

scholarly journals Retraction Notice: Composite Advanced Detection for Coal Seam Thickness in Coal Roadway

2016 ◽  
Vol 9 (1) ◽  
pp. 324-324
Author(s):  
Wang Bo ◽  
Zhang Xiayang ◽  
Liu Shengdong ◽  
Lu Tuo ◽  
Chen Mulan
Keyword(s):  
Coal Seam ◽  
Seam Thickness ◽  
Coal Roadway ◽  
Retraction Notice ◽  
Coal Seam Thickness

CBM Exploration Prospects Analysis in Northeastern Chian – In Jixi Basin as an Example

2013 ◽  
Vol 734-737 ◽  
pp. 1157-1160
Author(s):  
Shi Hui Wang ◽  
Cheng Wu Xu ◽  
Bao De Tan ◽  
You Zhi Wang ◽  
Jia Li
Keyword(s):  
Spatial Distribution ◽  
Data Analysis ◽  
Coal Seam ◽  
Comprehensive Analysis ◽  
Seam Thickness ◽  
Gas Content ◽  
Coal Resources ◽  
Drilling Data ◽  
Exploration Area ◽  
Coal Seam Thickness

The northeast part of China has rich coal resources and many coal basins. It has good prospects for CBM exploration. By drilling data analysis, this article evaluates coal seam characteristics and predicts seam thickness and spatial distribution of Chengzhihe formation. Jixi Basin has wide coal distribution, high seam gas content, good capping conditions, through a comprehensive analysis of factors such as coal seam thickness, depth and degree of metamorphism. the south of Jixi Basin is a key exploration area for CBM exploration and development in the northeast part of China.

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