ABSTRACT Logistic regression, neural networks and support vector machines are tested for their effectiveness in isolating surface waves in seismic shot records. To distinguish surface waves from other arrivals, we train the algorithms on three distinguishing features of surface‐wave dispersion curves in the k−ω domain: spectrum coherency of the trace's magnitude spectrum, local dip and the frequency range for a fixed wavenumber k in the spectrum. Numerical tests on synthetic data show that the kernel‐based support vector machines algorithm gives the highest accuracy in predicting the surface‐wave window in the k−ω domain compared to neural networks and logistic regression. This window is also used to automatically pick the fundamental dispersion curve. The other two methods correctly pick the low‐frequency part of the dispersion curve but fail at higher frequencies where there is interference with higher‐order modes.

中文翻译：

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通过监督机器学习方法分离多模面波

摘要 测试了逻辑回归、神经网络和支持向量机在分离地震炮记录中的表面波方面的有效性。为了区分表面波和其他到达，我们在 k-ω 域中表面波频散曲线的三个区别特征上训练算法：轨迹幅度谱的谱相干性、局部倾角和固定波数 k 的频率范围光谱。对合成数据的数值测试表明，与神经网络和逻辑回归相比，基于核的支持向量机算法在预测 k-ω 域中的表面波窗口方面具有最高的准确度。该窗口还用于自动选取基本色散曲线。