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Prediction method of coal seam porosity based on BP neural network

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Model predicts coal seam porosity using BP neural network, indicating improved accuracy over traditional methods.

The BP neural network model predicts coal seam porosity, achieving an average relative error of less than 8%.
Experimental results show strong agreement between predicted and measured porosity values, outperforming traditional methods.
The model uses Spearman correlation to screen input variables, enhancing the optimization of conventional logging curves.
Optimized network weights and thresholds via back propagation establish a reliable prediction technique for porosity.

Zhou et al. (2025) studied this question.

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View Full Paper

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Authors

Model predicts coal seam porosity using BP neural network, indicating improved accuracy over traditional methods.

The BP neural network model predicts coal seam porosity, achieving an average relative error of less than 8%.
Experimental results show strong agreement between predicted and measured porosity values, outperforming traditional methods.
The model uses Spearman correlation to screen input variables, enhancing the optimization of conventional logging curves.
Optimized network weights and thresholds via back propagation establish a reliable prediction technique for porosity.

Zhou et al. (2025) studied this question.

View Full Paper

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