基于模态分解和深度学习的煤矿微震时序预测方法

method for predicting the time series of microseismic events in coal mines based on modal decomposition and deep learning

微震监测数据对煤矿冲击地压等动力灾害预测和预警具有重要作用,利用历史微震监测数据来预测未来微震事件的演化特征是提高冲击灾害预测和预警时效性与准确性的有效方法。然而,由于微震数据是典型的非平稳时间序列,一般的时序预测方法很难进行准确的预测。为此,提出了一种基于模态分解技术和深度学习方法相结合的微震时序预测方法。该方法首先使用自适应噪声完备集合经验模态分解(CEEMDAN)将微震数据分解为多个本征模态序列。然后,通过样本熵将分解后的序列重构为高频、低频和趋势序列。接着,利用变分模态分解(VMD)将高频和低频序列再次分解为多个新的本征模态序列,用作微震数据的多元特征时间序列。最后,将一定时窗内的微震多元特征时间序列和历史微震数据作为输入,下一时刻的微震监测数据作为输出,建立了卷积神经网络(CNN)和长短期记忆网络(LSTM)相结合的微震时序预测模型,并采用贝叶斯优化方法对模型的超参数进行寻优,提高微震预测的准确性。依托新巨龙煤矿多个工作面微震监测数据,使用该方法对微震日最大能量、日平均能量和日频次进行了预测,并针对微震日最大能量数据进行了系列对比试验研究。结果表明,提出的方法能够较好地预测微震事件演化趋势,预测结果和实际监测值误差较小,具有良好的预测性能和泛化性能。

Microseismic monitoring data plays a crucial role in predicting and warning dynamic hazards such as coal mine rockburst.Utilizing historical microseismic monitoring data to forecast the evolution characteristics of future microseismic events is an effective approach to enhance the timeliness and accuracy of rockburst prediction and early warning.However,due to the non-stationary nature of microseismic data,conventional time series prediction methods often struggle to achieve accurate forecasts.To address this issue,this paper proposes a microseismic time series prediction method based on the combination of modal decomposition technique and deep learning methods.Firstly,the adaptive noise complete ensemble empirical mode decomposition(CEEMDAN)is employed to decompose the microseismic data into multiple intrinsic mode sequences.Subsequently,the decomposed sequences are reconstructed into high-frequency,low-frequency,and trend sequences using sample entropy.Furthermore,the variable mode decomposition(VMD)method is utilized to further decompose the high-frequency and low-frequency sequences into multiple new intrinsic mode sequences,which serve as the multivariate feature time series of microseismic data.Finally,a convolutional neural network(CNN)and long short-term memory network(LSTM)combined microseismic time series prediction model is established,with the microseismic multivariate feature time series within a certain time window and historical microseismic data as inputs,and the next moment microseismic monitoring data as output.The Bayesian optimization method is employed to optimize the hyperparameters of the model,aiming to improve the accuracy of microseismic prediction.Based on the microseismic monitoring data from the multiple working faces of the Xinjulong coal mine,the proposed method is applied to predict the daily maximum energy,daily average energy,and daily frequency of microseismic events,and a series of comparative experiments are conducted on the prediction of daily maximum energy data.The results show that the proposed method can effectively predict the evolution trend of microseismic events,with small errors between the predicted results and actual monitoring values.It demonstrates a good predictive performance and generalization ability.