Building realistic and reliable models of the subsurface is the primary goal of seismic imaging. We have constructed an ensemble of convolutional neural networks (CNNs) to build velocity models directly from the data. Most other approaches attempt to map full data into 2D labels. We exploit the regularity of seismic acquisition and train CNNs to map gathers of neighboring common midpoints (CMPs) to vertical 1D velocity logs. This allows us to integrate well-log data into the inversion, simplify the mapping by using the 1D labels, and accommodate larger dips relative to using single CMP inputs. We dynamically generate the training data in parallel with training the CNNs, which reduces overfitting. Data generation and training of CNNs is more computationally expensive than conventional full-waveform inversion (FWI). However, once the network is trained, data sets with similar acquisition parameters can be inverted much faster than with FWI. The multiCMP CNN ensemble is tested on multiple realistic synthetic models, performs well, and was combined with FWI for even better performance.

中文翻译：

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通过深度学习将完整的地震波形映射到垂直速度剖面

建立真实可靠的地下模型是地震成像的主要目标。我们构建了一个卷积神经网络 (CNN) 的集合，以直接从数据构建速度模型。大多数其他方法试图将完整数据映射到 2D 标签。我们利用地震采集的规律性并训练 CNN 将相邻公共中点 (CMP) 的道集映射到垂直一维速度测井。这使我们能够将测井数据集成到反演中，通过使用一维标签简化映射，并适应相对于使用单个 CMP 输入更大的倾角。我们在训练 CNN 的同时动态生成训练数据，从而减少过拟合。CNN 的数据生成和训练比传统的全波形反演 (FWI) 的计算成本更高。然而，一旦网络经过训练，具有相似采集参数的数据集可以比 FWI 更快地反转。multiCMP CNN 集成在多个真实的合成模型上进行了测试，性能良好，并与 FWI 结合以获得更好的性能。