Seismic forward prospecting is essential because it can identify the velocity distribution in front of the tunnel face and provide guidance for safe excavation activities. We have developed a convolutional neural network (CNN)-based method to invert forward-prospecting data recorded in tunnels for accurate and rapid estimation of seismic velocity distribution. Targeting the unusual seismic acquisition setup in tunnels, we design two separate encoders to extract features from observation data recorded on both tunnel sidewalls. Subsequently, these features are concatenated to a decoder for velocity prediction. Considering the various acquisition setups used in different tunneling projects, the deep-learning inversion network must be flexible in terms of the seismic source/receiver positions for practical application. We have generated two auxiliary feature maps that can be used to feed acquisition information to our network. Our network, acquisition adaptive CNN (-CNN), can be trained by defining the loss function based on the -norm and multiscale structural similarity. Compared with traditional CNNs, our method has superior performance on data sets with fixed and random acquisition setups and also demonstrates certain robustness when handling synthetic data with field noise. Finally, we test how the network performs when feeding the modified acquisition setup information. It turns out that the inversion result will demonstrate a shift when the provided acquisition setup information shift, which verified the validity of the network and its use of acquisition information.