School of Computer and Information Technology, Northeast Petroleum University
School of Physics and Electronic Engineering, Northeast Petroleum University
Artificial Intelligence Energy Research Institute, Northeast Petroleum University
Key Laboratory of Networking and Intelligent Control of Heilongjiang Province

在煤层气勘探开发中，通过微地震监测技术掌握裂缝走向，调整采煤巷道位置、方位对安全生产具有重要意义。因此，提出一种基于多尺度卷积神经网络的微地震检测方法，较好地解决了在强干扰环境下的弱信号识别的问题。研究表明：在不同强度的噪声干扰下，本文方法的检测精确率和召回率均优于小波分析法、BP网络和卷积神经网络等方法，且具有较好的抗噪性。当信噪比大于6 dB时，模型检测召回率可达到90%以上，精确率可达到92.1%以上。通过黑龙江某地区实际煤层气微地震监测数据的验证，模型具有良好表现。

In the exploration of coalbed methane, it is of great significance for safe production to master the direction of artificial fractures and adjust the position and orientation of coal mining roadway through micro-seismic monitoring technology. In this paper, a micro-seismic detection method based on a multi-scale convolutional neural network is proposed, which better solves the problem of weak signal recognition in a strong interference environment. The results show that the detection precision and recall rate of the proposed method is better than those of wavelet analysis, BP network, and convolutional neural network under different intensities of noise interference, and have good noise immunity. When the signal-to-noise ratio is greater than 6 dB, the model detection recall rate can reach more than 90%, the accuracy rate can reach more than 92.1%, and the model has good performance through the verification of the actual coal-bed methane micro-seismic monitoring data in a certain area of Heilongjiang Province.

国家自然科学基金区域联合基金资助项目(U21A2019)；国家自然科学基金面上资助项目(61873058)

张岩，刘小秋，宋利伟，等. 多尺度神经网络煤层气微地震检测研究[J]. 煤矿安全，2023，54（9）：1−7

ZHANG Yan, LIU Xiaoqiu, SONG Liwei, et al. Study on micro-seismic detection in coalbed methane based on multiscale neural network[J]. Safety in Coal Mines, 2023, 54（9）: 1−7