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摘要:
有效事件自动拾取是微地震监测的重要环节，拾取的准确性直接影响后续震源定位和震源机制反演的精度与可靠性。构建了10层U-Net神经网络模型框架，将三维有限差分模拟的原始微地震数据与实测储气库微地震原始数据制作为标签图像，将其切割为128×128大小切片并输入U-Net神经网络学习，输出预测后的切片并将其合并，再对预测后的图像进行二值化，最后提取微地震有效事件的P波初至，使得背景噪声与有效信号图像的边缘分割更加精细，提高了微地震有效事件的自动拾取效率与准确性。定量分析对比了U-Net与STA/LTA法的拾取率、错拾率和拾取误差，测试结果表明，U-Net的拾取效果优于STA/LTA法，而且U-Net也具有较强的抗干扰能力与泛化能力；评价不同标签宽度对初至拾取结果的影响，结果表明依据事件的主周期制作的标签拾取效果最佳。本研究建立的U-Net神经网络初至自动拾取算法是高效、高精度储气库完整性微地震智能监测系统的重要组成部分，对提高我国微地震监测技术水平具有重要意义。
Abstract:Objective Automatic pickup of effective events is an important part of microseismic monitoring, and the accuracy of pickup directly affects the accuracy and reliability of subsequent seismic source localization and seismic source mechanism inversion.
Methods In this paper, a 10-layer U-Net neural network model framework is constructed, the original microseismic data from 3D finite-difference simulation and the raw microseismic data from the measured gas storage reservoirs are made into labeled images, which are cut into 128*128 sized slices and input into the U-Net neural network for learning, and then the output of predicted slices is outputted and merged, and then the predicted images are binarized, and the microseismic effective events are extracted in the end of the P-wave first arrivals. This makes the edge segmentation of background noise and effective signal image more fine, and improves the efficiency and accuracy of automatic picking up of effective microseismic events.
Results Quantitatively analyze and compare the pickup rate, wrong pickup rate and pickup error of U-Net method and STA/LTA method, the test results show that the pickup effect of U-Net is better than that of STA/LTA method, and U-Net also has a strong anti-jamming ability and generalization ability; Evaluate the effect of different label widths on the first-to-pickup results, the results show that the label pickup effect based on the event's primary cycle is The results show that the label pickup effect based on the main cycle of the event is the best.
Conclusion The U-Net neural network first-to-automatic pickup algorithm established in this paper is an important part of the highly efficient and high-precision reservoir integrity microseismic intelligent monitoring system, which is of great significance to improve the level of microseismic monitoring technology in China.
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图 2 U-Net微地震拾取有效事件基本流程（a）以及基本原理（b）
Figure 2. Basic process and principles (a) of U-Net microseismic pickup effective events (b)
图 3 模型训练的模拟数据Vz分量与其训练标签（同相轴上制作标签宽度为200（时间为0.1 s）的有效事件标记为1（白色），其余部分为0（黑色）；SNR. 信噪比，下同）
Figure 3. The Vz component of simulated data for model training and its training label
图 4 模型训练的站点2实测数据与其训练标签
Figure 4. The measured data from the second site of model training and its training label
图 6 模拟数据的训练标签（a）与预测结果（b）
Figure 6. Training labels and prediction results for simulated data
图 7 实测数据的训练标签（a）与预测结果（b）
Figure 7. Training labels and prediction results based on measured data
表 1 不同信噪比模拟数据拾取结果评价
Table 1. Evaluation of data pickup results for simulations with different signal to noise ratios
方法 指标 不含
噪声SNR=10 dB SNR=5 dB SNR=0 dB SNR=−5 dB U-Net法 拾取率/% 91.2 86.4 85.2 78.4 58 错拾率/% 7.6 10.8 12 11.6 29.6 平均相对
误差/%6.12 5.68 7.27 7.9 9.79 STA/LTA法 拾取率/% 99 69.65 49.25 38.81 34.33 错拾率/% 0 30.35 50.75 61.19 65.67 平均相对
误差/%0.125 1.44 1.35 1.62 1.33 
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表 2 实测数据拾取结果评价
Table 2. Evaluation of different signal to noise ratio simulation data pickup results
方法 指标 站点1 站点2 站点3 平均值 U-Net法 拾取率/% 85 82.35 100 89.17 错拾率/% 20 29.41 9.52 19.64 平均相对误差/% 7.56 5.39 1.5 4.82 STA/LTA法 拾取率/% 55 64.71 66.67 62.13 错拾率/% 45 35.29 14.29 31.53 平均相对误差/% 7.72 3.26 3.05 4.68
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表 3 模拟数据有效事件结果评价对比
Table 3. Comparison of Results Evaluation for Simulated Data with Effective Event Tags Width of 100 Sampling Points
标签
宽度指标 不含
噪声SNR=10 dB SNR=5 dB SNR=0 dB SNR=−5 dB 100 拾取率/% 80.4 65.2 61.6 62 55.6 错拾率/% 10 23.6 31.6 29.6 34 平均相对误差/% 7.98 7.62 9.58 9.67 11.49 200 拾取率/% 91.2 86.4 85.2 78.4 58 错拾率/% 7.6 10.8 12 11.6 29.6 平均相对误差/% 6.12 5.68 7.27 7.9 9.79 300 拾取率/% 86 68.8 66.4 60 58.8 错拾率/% 13.6 25.2 28.8 36.8 39.6 平均相对误差/% 6.11 5.97 6.06 6.64 7.57
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