Current data-driven inversion methods based on deep learning (DL) use an end-to-end learning to obtain a mapping relationship from seismic data to the velocity model. These methods require truncating the feature maps in the output layer of the network to build the velocity model with a specified size. Therefore, they lack the flexibility of handling output of different sizes, as the network needs to be retrained to achieve reliable results when changing the desired size of the velocity model. Here, to solve the problem of data-driven velocity inversion with variable size of output, a novel sampling matrix is proposed to compress the seismic data to the same size as the model to avoid clipping the feature maps. The compressed seismic data are fed into the designed deep convolutional neural network (CNN) model for training. Here, the network has a multiscale encoder–decoder structure and is composed of several residual blocks. Also, the multiobjective loss functions balance strategy is used to optimize the training process. Utilizing the trained network to test compressed synthetic seismic data of various sizes, the effectiveness of the proposed method for the inversion of variable-size elastic wave velocity models is verified.

中文翻译：

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基于深度学习的地震变尺寸速度模型构建


当前基于深度学习（DL）的数据驱动反演方法使用端到端学习来获得从地震数据到速度模型的映射关系。这些方法需要截断网络输出层中的特征图，以构建指定大小的速度模型。因此，它们缺乏处理不同大小输出的灵活性，因为在改变速度模型的所需大小时需要重新训练网络才能获得可靠的结果。在这里，为了解决输出大小可变的数据驱动速度反演问题，提出了一种新颖的采样矩阵，将地震数据压缩到与模型相同的大小，以避免剪切特征图。压缩的地震数据被输入到设计的深度卷积神经网络（CNN）模型中进行训练。这里，网络具有多尺度编码器-解码器结构，由多个残差块组成。此外，多目标损失函数平衡策略用于优化训练过程。利用训练好的网络测试不同尺寸的压缩合成地震数据，验证了该方法反演变尺寸弹性波速模型的有效性。