Exploration seismology uses reflected and refracted seismic waves, emitted from a controlled (active) source into the ground, and recorded by an array of seismic sensors (receivers) to image the subsurface geologic structures. These seismic images are the main resources for energy and resource exploration and scientific investigation of the crust and upper mantle. We survey recent advances in applications of machine-learning methods, more specifically deep neural networks (DNNs), in exploration seismology. We provide a technically oriented review of DNN applications for seismic data acquisition; data preprocessing tasks such as interpolation/extrapolation, denoising, first-break picking, velocity picking, and seismic migration; data processing tasks such as geologic and structural interpretations; and data modeling tasks such as the inference of subsurface structures and lithologic and petrophysical properties. DNNs have entered almost every sector of exploration seismology. They have outperformed many traditional algorithms for the automation of seismic data acquisition, data preprocessing, data processing, interpretations, and data modeling tasks. However, despite the impressive performances of DNN-based approaches, the out-of-distribution generalization and interpretability of these models remain challenging. To overcome these challenges, incorporating domain knowledge into the DNNs is a promising path and a focus of current deep-learning research in seismology.