Separation of overlapping signals is an important task in signal processing, with application in music, speech, and seismic signal processing. We show that separation is possible also for seismic recordings, using techniques from machine learning (and even those recorded with a single sensor). This may have an impact on seismic applications such as ambient noise tomography, induced seismicity, earthquake analysis, aftershock analysis, nuclear verification, and seismoacoustics/infrasound. The machine learning technique that we use for seismic signal separation is based on a dual-path recurrent neural network which is applied directly to the time domain data. We train the network on seismic data produced by trains, and recorded with a Raspberry Shake sensor at the University of Vienna. We demonstrate that the network predicts the signals from a synthetic mixture very well. We then use a transfer learning approach to fine-tune this pre-trained network for earthquake signals and denoise them. We also perform a task outside of its initial training domain - a P- and S- wave arrival picking, demonstrating the wide potential for applications of such a network. Furthermore, we argue that a network built this way can serve as a Bidirectional Encoder Representation (BERT) pre-training step in waveform Machine Learning applications, thus reducing necessary training time for potential applications. This work proves the concept and steers the direction for further research of earthquake-induced source separation. We have therefore aimed to describe the technicalities in detail. We provide a reproducible research repository with the algorithms and datasets.