An analytical methodology of rock burst with fully mechanized top-coal caving mining in steeply inclined thick coal seam

Rock burst disaster is still one of the most serious dynamic disasters in coal mining, seriously restricting the safety of coal mining. The b value is the main parameter for monitoring rock burst, and by analyzing its changing characteristics, it can effectively predict the dangerous period of rock burst. This article proposes a method based on deep learning that can predict rock burst using data generated from microseismic monitoring in underground mining. The method first calculates the b value from microseismic monitoring data and constructs a time series dataset, and uses the dynamic time warping algorithm (DTW) to reconstruct the established b value time series. A bidirectional short-term and short-term memory network (BiLSTM) loaded with differential evolution algorithm and attention mechanism was used for training, and a prediction model for the dangerous period of rock burst based on differential algorithm optimization was constructed. The study used microseismic monitoring data from the B1+2 fully mechanized mining face and B3+6 working face in the southern mining area of Wudong Coal Mine for engineering case analysis. The commonly used residual sum of squares, mean square error, root mean square error, and correlation coefficient R2 for time series prediction were introduced, which have significant advantages compared to basic LSTM algorithms. This verifies that the prediction method proposed in this article has good prediction results and certain feasibility, and can provide technical support for the prediction and prevention of rock burst in steeply inclined thick coal seams in strong earthquake areas

Rockburst and gas outburst forecasting using a probabilistic risk assessment framework in longwall top coal caving faces

A probabilistic risk assessment framework was developed to mathematically represent the complex engineering phenomena of rock bursts and gas outbursts for a heterogeneous coal seam. An innovative object-based non-conditional simulation approach was used to distribute lithological heterogeneity present in the coal seam to respect their geological origin. The changing mining conditions during longwall top coal caving mining (LTCC) were extracted from a coupled numerical model to provide statistically sufficient data for probabilistic analysis. The complex interdependencies among abutment stress, pore pressure, the volume of total gas emission and incremental energy release rate, their stochastic variations and uncertainty were realistically implemented in the GoldSim software, and 100,000 equally likely scenarios were simulated using the Monte Carlo method to determine the probability of rock bursts and gas outbursts. The results obtained from the analysis incorporate the variability in mechanical, elastic and reservoir properties of coal due to lithological heterogeneity and result in the probability of the occurrence of rock bursts, coal and gas outbursts, and safe mining conditions. The framework realistically represents the complex mining environment, is resilient and results are reliable. The framework is generic and can be suitably modified to be used in different underground mining scenarios, overcoming the limitations of earlier empirical indices used

Experimental and numerical modelling investigations into coal mine rockbursts and gas outbursts

Rockbursts and gas outbursts are a longstanding hazard in underground coal mining due to their sudden occurrences and high consequences. These hazards are becoming prominent due to the increase in mining depth, difficult mining conditions, and adverse gas pressure conditions. Several researchers have proposed different theories, mechanisms, and indices to determine the rockbursts and gas outbursts liability but most of them focus on only some aspects of the complex engineering system for the ease to represent them using partial differential equations. They have often ignored the dynamics of changing mining environment, coal seam heterogeneity and stochastic variations in the rock properties. Most of the indices proposed were empirical and their suitability to different mining conditions is largely debated. To overcome the limitations of previous theories, mechanisms and indices, a probabilistic risk assessment framework was developed in this research to mathematically represent the complex engineering phenomena of rockbursts and gas outbursts for a heterogeneous coal seam. An innovative object-based non-conditional simulation approach was used to distribute lithological heterogeneity occurring in the coal seam to respect their geological origin. The dynamically changing mining conditions during a longwall top coal caving mining (LTCC) was extracted from a coupled numerical model to provide statistically sufficient data for probabilistic analysis. The complex interdependencies among several parameters, their stochastic variations and uncertainty were realistically implemented in the GoldSim software, and 100,000 equally likely scenarios were simulated using the Monte Carlo method to determine the probability of rockbursts and gas outbursts. The results obtained from the probabilistic risk assessment analysis incorporate the variations occurring due to lithological heterogeneity and give a probability for the occurrence of rockbursts, coal and gas outbursts, and safe mining conditions. The framework realistically represents the complex mining environment, is resilient and results are reliable. The framework is generic and can be suitably modified to be used in different underground mining scenarios, overcoming the limitations of earlier empirical indices used.Open Acces