Geoscientists mainly identify subsurface geologic features using exploration-derived seismic data. Classification or segmentation of 2D/3D seismic images commonly relies on conventional deep learning methods for image recognition. However, complex reflections of seismic waves tend to form high-dimensional and multi-scale signals, making traditional convolutional neural networks (CNNs) computationally costly. Here we propose a highly efficient and resource-saving CNN architecture (SeismicPatchNet) with topological modules and multi-scale-feature fusion units for classifying seismic data, which was discovered by an automated data-driven search strategy. The storage volume of the architecture parameters (0.73 M) is only ~2.7 MB, ~0.5% of the well-known VGG-16 architecture. SeismicPatchNet predicts nearly 18 times faster than ResNet-50 and shows an overwhelming advantage in identifying Bottom Simulating Reflection (BSR), an indicator of marine gas-hydrate resources. Saliency mapping demonstrated that our architecture captured key features well. These results suggest the prospect of end-to-end interpretation of multiple seismic datasets at extremely low computational cost.

地球科学家主要利用勘探地震数据来识别地下地质特征。2D/3D 地震图像的分类或分割通常依赖于传统的深度学习方法进行图像识别。然而，地震波的复杂反射往往会形成高维和多尺度信号，使得传统的卷积神经网络（CNN）计算成本高昂。在这里，我们提出了一种高效且节省资源的 CNN 架构（SeismicPatchNet），具有拓扑模块和多尺度特征融合单元，用于对地震数据进行分类，这是通过自动数据驱动搜索策略发现的。架构参数的存储量（0.73 M）仅为~2.7 MB，约为众所周知的VGG-16架构的0.5%。SeismicPatchNet 的预测速度比 ResNet-50 快近 18 倍，在识别海底模拟反射 (BSR)（海洋天然气水合物资源指标）方面显示出压倒性优势。显着性映射证明我们的架构很好地捕捉了关键特征。这些结果表明了以极低的计算成本对多个地震数据集进行端到端解释的前景。