Application of Microseismic Monitoring Technology in Water Hazard Prevention for Coal Mine Roofs: An Optimization Approach Based on Adaptive Boosting Genetic Algorithm (ABGA)

Efficient data alignment is crucial for distributed data acquisition systems, where latency variations and inconsistent sampling rates often lead to synchronization issues. To address this challenge, we propose an Adaptive Boosting Genetic Algorithm (ABGA) to enhance data alignment accuracy and system performance. ABGA optimizes synchronization parameters through adaptive selection, crossover, and mutation strategies, dynamically adjusting data collection timing across distributed nodes. Experimental results demonstrate that ABGA reduces the average timestamp discrepancy to 5.2ms, a 67.1% improvement over traditional synchronization methods. It also achieves a 97.1% data alignment accuracy, surpassing standard genetic algorithms by 2.4<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>, and enhances fault tolerance by 4.5%, ensuring stable performance under fluctuating network conditions. Additionally, ABGA improves computational efficiency, reducing processing time to 0.91s, making it 7.1% faster than conventional genetic algorithms. These findings confirm that ABGA provides a highly accurate, stable, and efficient solution for distributed data synchronization. Its superior performance makes it particularly beneficial for real-time applications such as microseismic monitoring and industrial sensor networks, where precise data fusion is essential.