Research on automatic picking of microseismic first arrival
-
摘要: 微震初至波到时准确拾取是实现震源定位的前提，传统的人工拾取方法效率低、耗时长，而自动拾取方法中常用的长短时窗能量比值（STA/LTA）法对低信噪比信号的拾取准确率较低。针对上述问题，提出了一种基于随机森林的微震初至波到时自动拾取方法。首先，提取微震数据的振幅、能量及相邻时刻振幅比作为特征，并对每个样本进行特征类别标记；然后，构建随机森林模型以识别微震初至波；最后，采用随机森林模型计算每个测试样本属于某一类别的概率，将概率不小于0.5的第1个数据采样点判定为微震初至波到时采样点。采用煤矿井下巷道深孔中的微震监测数据进行实验，结果表明当随机森林算法中决策树的数量和最大深度分别为137，6时，该方法对微震数据样本分类的准确率达98.5％，对微震初至波到时的平均拾取误差为23.1 ms，拾取精度优于STA/LTA方法。Abstract: Accurate picking of the first arrival of microseisms is the prerequisite for the estimation of source location. The traditional manual picking method is inefficient and time-consuming. The short time average long time average (STA/LTA) method, commonly used in automatic picking, has low picking accuracy for low signal-to-noise ratio signals. To address the above problems, a random forest-based automatic picking method of microseismic first arrival is proposed. Firstly, this study extracts the amplitude, energy and amplitude ratio of adjacent moments of microseismic data as features and mark each sample with feature categories. Secondly, a random forest model is constructed to identify microseismic first arrivals. Thirdly, the random forest model is used to calculate the probability of each test sample belonging to a certain category, and the first data sampling point with a probability of no less than 0.5 is defined as the microseismic first arrivals sampling point. In this experiment, microseismic monitoring data in deep boreholes of coal mine roadways is used. The results show that as the number of decision trees reaching 137 and the maximum depth reaching 6 in the random forest algorithm, the accuracy of the method for classifying microseismic data samples could reach 98.5%, and the average picking error for first arrivals of microseismic is 23.1 ms. Therefore, this method is better than the method of STA/LTA in terms of picking accuracy.
-
-
期刊类型引用(11)
1. 李典泽，许华杰，张勃. 基于微震信号深度特征学习的岩石破裂类型识别. 工矿自动化. 2025(03): 156-164 . 
本站查看
2. 何彬，周云耀，吕永清. 结合U-net与FPN的地震初至波拾取算法. 测绘地理信息. 2024(01): 82-87 . 百度学术
3. 陈仲杰，闭水劲，董陇军，杨龙斌. 金川二矿区采场震源精细定位与时空演化规律研究. 矿业研究与开发. 2024(07): 135-141 . 百度学术
4. 宋成林，黄晓冉，邢帅，芦楠楠. 基于隐半马尔可夫模型的微震信号分割方法. 中国科技论文. 2024(08): 868-876 . 百度学术
5. 李铁牛，胡宾鑫，李化坤，耿文成，郝鹏程，纪旭波，孙增荣，朱峰，张华，阳铖权. 基于改进支持向量机的微震初至波到时自动拾取方法. 工矿自动化. 2023(03): 63-69 . 本站查看
6. 阳铖权，胡宾鑫，李化坤，耿文成，郝鹏程，纪旭波，孙增荣，朱峰，张华，李铁牛. 基于改进小波阈值去噪与分形盒维数的矿山微震初至波到时拾取研究. 矿业研究与开发. 2023(04): 125-132 . 百度学术
7. 蒋沛凡，邓飞，严星. 基于Swin Transformer特征提取的微地震初至拾取方法. 地球物理学进展. 2023(03): 1132-1142 . 百度学术
8. 胡慧江，李利平，靳昊，陈彦好，王升，黄瑞哲. 基于高阶统计量偏斜度和赤池信息准则的突涌水微振信号初至拾取方法. 工业建筑. 2023(05): 132-136+195 . 百度学术
9. 刘洋，李冒金. FPGA与北斗的高精度时差信息获取系统设计. 单片机与嵌入式系统应用. 2022(07): 59-61 . 百度学术
10. 曹杰锋，刘海鹏，周嘉琦，李坡，梁昌晶. 基于LMD-STA/LTA模型的油气管道泄漏检测方法. 世界石油工业. 2022(04): 71-76 . 百度学术
11. 邓飞，蒋沛凡，蒋先艺，帅鹏飞，唐云. 应用图像语义分割网络的微地震事件识别和初至拾取方法. 石油地球物理勘探. 2022(05): 1011-1019+999 . 百度学术
其他类型引用(9)
计量
- 文章访问数: 109
- HTML全文浏览量: 12
- PDF下载量: 29
- 被引次数: 20
下载:
