基于深度学习的井间地震CT初至自动拾取方法  

Automatic Picking Method for First Arrival of Cross-well Seismic CT Based on Deep Learning

作　　者：杨洋 王用鑫[1,2] 李虎 王庆林 范涛[6] 门燕青 刘锐 孙怀凤 YANG Yang;WANG Yongxin;LI Hu;WANG Qinglin;FAN Tao;MEN Yanqing;LIU Rui;SUN Huaifeng(Geotechnical and Structural Engineering Research Center,Shandong University,Jinan 250061,China;Laboratory of Earth Electromagnetic Exploration,Shandong University,Jinan 250061,China;Advanced Exploration and Transparent City Innovation Center,Shandong Research Institute of Industrial Technology,Jinan 250101,China;Jinan Rail Transit Group Co.,Ltd,Jinan 250101,China;Earthquake Administration of Shandong Province,Jinan 250014,China;Xi’an Research Institute of China Coal Technology&Engineering Group Corp.,Xi'an 710077,China)

机构地区：[1]山东大学岩土与结构工程研究中心,山东济南250061 [2]山东大学地球电磁探测研究所,山东济南250061 [3]山东省工业技术研究院先进勘探与透明城市协同创新中心,山东济南250101 [4]济南轨道交通集团有限公司,山东济南250101 [5]山东省地震局,山东济南250014 [6]中煤科工集团西安研究院有限公司,陕西西安710077

出　　处：《应用基础与工程科学学报》2022年第1期183-195,共13页Journal of Basic Science and Engineering

基　　金：国家自然科学基金项目(42004056);山东省自然科学基金项目(ZR2020QD052,ZR2019MD019)。

摘　　要：井间地震CT方法能够有效探测岩溶等不良地质,而拾取地震波的初至对其反演结果至关重要.以往井间地震CT初至拾取通过手动标定,效率低,无法满足模型训练要求.本文基于人工源地震初至的时距特征,提出了一种基于深度学习的地震初至训练样本库快速标定方法.利用人工合成数据训练初始网络模型,采用少量人工标定实测数据进一步训练,基于该网络模型对未标定的井间地震实测数据剖面进行初至拾取,采用长短时窗比法修正曲线拟合结果,对仍不理想的拾取结果进行人工判别和改正,并将准确标定数据加入训练集,形成新的网络模型;通过迭代实现数据集再生成并获得后续阶段的网络模型.基于上述方法完成了80000道数据的初至拾取,在充分训练后神经网络具备较高的拾取精度,采用3840组数据对该模型进行测试,发现预测结果与人工拾取结果对比的绝对误差值小于0.2ms的数据占91.8%,说明该方法是一种切实有效且高效的井间地震CT初至拾取方法.The cross-well seismic computed tomography(CT)method can effectively detect bad geology,such as karst.The first arrival of the picked up seismic wave is very important to its inversion result.In the past,manual calibration in the first arrival of cross-well seismic CT was inefficient and could not meet actual engineering requirements.Therefore,based on the timedistance characteristics of the first arrival of an artificial source,this paper proposes a fast calibration method for the training sample database of the first arrival of an earthquake based on deep learning.First,use artificial synthetic data to train the initial network model,and use a small amount of manually calibrated measured data for further training.Based on this network model,the uncalibrated cross-well seismic data section is first picked.The long-short time window ratio method is used to modify the curve fitting results to manually distinguish and correct the still unsatisfactory picking results.Then add accurate calibration data to the training set to form a new network model.Through iteration,the data set is regenerated,and the network model of the subsequent stage is obtained.Based on the above method,the first arrival of 80000 channels of data has been picked up.After full training,the neural network has a high picking accuracy,and 3840 sets of data are used to test the model.Comparing the prediction results obtained by deep learning with the manual picking results,the data with an absolute error value of less than 0.2 milliseconds accounted for 91.8%.The result shows that the method proposed is a practical and efficient method for picking up cross-well seismic CT first arrivals.

关 键 词：井间地震CT 初至拾取 深度学习 神经网络 训练样本集 人工智能 

分 类 号：U452[建筑科学—桥梁与隧道工程]