Since 2018, deep learning inversion methods of seismic data have made great progress. Currently, deep learning inversion methods are mostly data-driven and based on supervised learning. They rely on labels, i.e., actual velocity models, which are difficult to obtain in real sampling. To solve this problem, the acoustic wave equation is introduced into SeisInvNet, our previously proposed inversion network, which could perform forward modeling of the network prediction results to obtain the corresponding seismic data. The predicted seismic data and the acquired real data can be used to obtain a new loss function, called data loss, and the gradients of the network parameters can be computed, enabling the training of a DNN that does not depend on the actual velocity model. In this way, physical information is also introduced into the network to improve generalizability. The proposed network is trained by sampling 1200 sets of 2D models with 64×200 girds from the overthrust model. To provide stable initial parameters of the network, we used a linear initial model as bootstrap and gradually increased the weight of data loss to finally achieve a good unsupervised learning inversion effect. It is interesting to note that it is difficult to obtain accurate inversion results with a linear model as the initial model in traditional full-waveform inversion. We believe that it is very meaningful to use a large amount of data as a priori for the network to effectively improve the local optimum problem in traditional inversion.