Microseismic events generally contain strong noise-polluting and unobvious P-phase oscillating channel waveforms. The automatic P-phase arrival picking accuracy of these channel waveforms tends to be low, or even are false. Currently, unusable P-picks are not screened out automatically before geophysics inversions in most microseismic data processing software. Therefore, manual interventions are needed to remove or correct the unusable P-picks. However, rapidly increasing monitoring data causes manual handling to be time-consuming and lagging. Supervised machine learning (ML) is applied to distinguish useable and unusable P-picks automatically. Big data analysis revealed that the waveform features, including signal-to-noise ratio, signal-to-noise variance ratio, P-wave starting-up slope, and peak amplitude have impact on P-pick accuracy. In contrast, the effect of the short-time zero-crossing rate on the P-pick accuracy is not as obvious. Five P-pick quality control models were trained based on traditional machine learning approaches, including discriminant analysis, logistic regression, k-nearest neighbor, support vector machine, and Naive Bayes classifier. For these five models, the input data are P-pick labels and waveform features. In addition, another P-pick quality control model was trained based on convolutional neural network. While, the input data are P-pick images and labels. The training sets used in all six machine learning models are uniform. The testing experiments with uniform testing set show that the support vector machine generated best the performance among traditional machine learning approaches, with 82.81% accuracy. However, the convolutional neural network model generated outstanding performance in recognizing P-pick, with 91.71% accuracy. The automatic P-pick quality control method proposed in this study can facilitate the precision and efficiency of the automatic processing of microseismic signals.

微震事件通常包含强噪声污染和不明显的 P 相振荡通道波形。这些通道波形的自动 P 相到达拾取精度往往很低，甚至是错误的。目前，在大多数微地震数据处理软件中，在地球物理反演之前不会自动筛选出不可用的 P-pick。因此，需要手动干预来移除或纠正不可用的 P 形拨片。然而，快速增加的监测数据导致人工处理耗时且滞后。应用监督机器学习 (ML) 来自动区分可用和不可用的 P-pick。大数据分析表明，波形特征，包括信噪比、信噪方差比、P波启动斜率和峰值幅度对P-pick精度有影响。相比之下，短时过零率对 P-pick 精度的影响就不那么明显了。基于传统机器学习方法训练了五个 P-pick 质量控制模型，包括判别分析、逻辑回归、k-最近邻、支持向量机和朴素贝叶斯分类器。对于这五个模型，输入数据是 P-pick 标签和波形特征。此外，基于卷积神经网络训练了另一个P-pick质量控制模型。而输入数据是P-pick图像和标签。所有六个机器学习模型中使用的训练集都是统一的。使用统一测试集的测试实验表明，支持向量机在传统机器学习方法中的性能最好，准确率为 82.81%。然而，卷积神经网络模型在识别 P-pick 方面表现出色，准确率为 91.71%。本研究提出的自动P-pick质量控制方法可以提高微震信号自动处理的精度和效率。