Seismic Inversion for Fracture Properties Using Model-Data Dual-Driven Network

Deep learning techniques have seen widespread application in seismic inversion, yet they face significant challenges when applied to fracture property inversion. The limited availability of labeled data and the lack of robust geophysical constraints can severely impact the accuracy of predictions. To address these challenges, we propose a model-data dual-driven network system. To mitigate data scarcity, we introduce a semisupervised learning framework enhanced by data augmentation techniques. This framework leverages unlabeled data to enhance the diversity of the training dataset, thereby improving the robustness of the learning process. In addition, we embed initial model constraints into our inversion network, ensuring that the inversion process is guided by geophysically plausible starting points. Drawing on the principles of a horizontally transversely isotropic (HTI) medium, we develop a forward model that establishes a clear relationship between fracture properties and azimuth-dependent seismic data. This model is implemented through convolutional operations between the azimuthal PP-wave reflection coefficient equation and seismic wavelets, effectively linking fracture attributes to observable seismic responses. The integration of these components results in a model-data dual-driven network capable of performing fracture property inversion with high accuracy. To validate the effectiveness and superiority of our proposed method, we apply it to both a synthetic overthrust model and real datasets. The results demonstrate that the predicted fracture weaknesses values achieve high correlation coefficients of 0.97 and 0.84 with the ground truth, respectively. Furthermore, the training time is significantly reduced compared to conventional data-driven methods, effectively improving both inversion accuracy and computational efficiency.