The conventional surface wave inversion methodology relying upon dispersion curve analyses is frequently confounded by the phenomenon known as “mode kissing” as well as the absorptive properties of seismic waves traversing near-surface geologic strata. We propose a deep learning approach to overcome challenges in surface wave inversion due to complex near-surface geology. A convolutional neural network regression model is developed to directly map surface wave dispersion spectra to shear wave velocity profiles. The network takes entire spectrograms as input, avoiding issues from manual picking of individual dispersion curves. This circumvents problems of mode misidentification and crossover that hamper traditional inversion in layered media. Furthermore, we incorporate the quality factor Q into calculating the theoretical frequency dispersion spectrum to account for the absorptive influence of geologic formations. Through studying theoretical spectra, we found that when the Q-factor is less than 10, the absorption of the formation will have a greater impact on the dispersion imaging. Compared to existing workflows requiring manual parameter tuning and computationally expensive inversion calculations, the trained network directly infers velocities without user intervention or iterative optimization. The end-to-end relation learned from synthetic training data enables rapid interpretation of field records complicated by low-velocity zones and high-velocity inclusions, which inhibit conventional inversion algorithms.

中文翻译：

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用于面波探索的基于深度学习的色散谱反演


依赖于频散曲线分析的传统面波反演方法经常被称为“模式接吻”的现象以及穿过近地表地质地层的地震波的吸收特性所混淆。我们提出了一种深度学习方法来克服复杂的近地表地质带来的表面波反演挑战。开发了卷积神经网络回归模型，将表面波色散谱直接映射到剪切波速度剖面。该网络将整个频谱图作为输入，避免了手动选取单个色散曲线带来的问题。这避免了阻碍分层介质中传统反演的模式错误识别和交叉问题。此外，我们将品质因数 Q 纳入理论频率色散谱的计算中，以考虑地质构造的吸收影响。通过研究理论光谱，我们发现当Q因子小于10时，地层的吸收会对色散成像产生较大的影响。与需要手动参数调整和计算成本高昂的反演计算的现有工作流程相比，经过训练的网络可以直接推断速度，无需用户干预或迭代优化。从合成训练数据中学习到的端到端关系能够快速解释由低速带和高速夹杂物复杂化的现场记录，这抑制了传统的反演算法。