用卷积神经网络加速海底地层速度模型预测，提升油藏勘探效率。
Mapping The Layers of The Ocean Floor With a Convolutional Neural Network
- 采用UNet等卷积神经网络从地震反射数据反演海底速度模型。
- 预测结果的Sørensen-Dice系数超过70%，优于传统方法。
- 适合地质勘探、石油工业中需要快速建模的场景。
海底地层映射是石油行业当前面临的挑战。现有方法依赖地震探测与波反演，过程复杂且计算成本高。本文验证了两种神经网络架构在基于海底地震反射数据反演速度模型中的有效性，并通过损失函数、相似性系数及预测与真实模型差异等稳定性指标进行比较。结果表明，神经网络在该任务中表现出显著优势，预测结果的Sørensen-Dice系数超过70%，为优化地层成像提供了高效可行的新路径。
原文摘要 · Abstract (English)
The mapping of ocean floor layers is a current challenge for the oil industry. Existing solution methods involve mapping through seismic methods and wave inversion, which are complex and computationally expensive. The introduction of artificial neural networks, specifically UNet, to predict velocity models based on seismic shots reflected from the ocean floor shows promise for optimising this process. In this study, two neural network architectures are validated for velocity model inversion and compared in terms of stability metrics such as loss function and similarity coefficient, as well as the differences between predicted and actual models. Indeed, neural networks prove promising as a solution to this challenge, achieving Sørensen-Dice coefficient values above 70%.
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