基于时间卷积网络的地震波阻抗反演

近些年来，深度学习网络的兴起极大地推动了人工智能技术在地震数据处理、反演以及解译等领域的应用.地震波阻抗反演是石油地震勘探领域的一项关键技术，其反演精度在圈定油气储层构造中起到非常重要的作用.提出了一种基于数据驱动时间卷积网络（temporal convolution network，TCN）模型的地震波阻抗反演方法，旨在无需建立初始反演模型，直接利用工区的少量测井标签数据，以地震振幅数据为输入，将波阻抗反演转化为时间序列建模任务，最终输出地下模型的阻抗信息.采用Marmousi2数据集对基于TCN的波阻抗反演模型进行训练、验证和测试，结果显示，在测试集上该模型预测结果的皮尔逊系数和决定系数分别达到97.92%和95.95%，并对远离训练区域的波阻抗信息预测有着良好的泛化性，且在预测时间和预测精度等方面都要明显优于前人的相关研究工作.上述结果表明，TCN时间序列深度学习模型在复杂地层波阻抗反演中具有一定优越性和应用前景，为地震波阻抗反演提供了新思路. 

Seismic Wave Impedance Inversion Based on Temporal Convolutional Network

In recent years, the rising of deep learning has significantly boosted the application of artificial intelligence techniques in fields such as seismic data processing, inversion, and interpretation. As a key technology for seismic exploration in the petroleum industry, the precision of seismic wave impedance inversion is essential to characterize hydrocarbon reservoir. A new algorithm is proposed for derivations of wave impedance model from seismic record data using data-driven temporal convolution network (TCN). The proposed algorithm takes the seismic amplitude data as input without relying on the initial inversion model and outputs the impedance information of the subsurface model by utilizing a few well log tag data from the work area and transforming the wave impedance inversion into a time series modeling task. In this paper, the TCN wave impedance inversion model is trained, validated, and tested using the Marmousi2 dataset. The results show high Pearson correlation coefficient (97.92%) and coefficient of determination (95.95%), respectively, on the test set, and also suggest well generalization for predicting wave impedance information far from the training area, and the proposed model significantly out performs previous related work in terms of prediction time and precision. The above results show case the excellent performance of TCN time series model in wave impedance inversion of complex stratigraphic and provide a new idea for seismic wave impedance inversion.