Over the last decade, the evolution of survey acquisition design composed of numerous receivers and shot points has exponentially increased the seismic data volume. As a result, the demand for efficient algorithms for detecting seismic first arrivals has been growing. Machine learning methods, especially convolutional neural networks, have shown great potential in previous studies. However, most existing methods lack generalization ability or be sensitive to noise. This paper proposes a new picking strategy based on the single-to multi-domain selftrained network to achieve automated picking for the first arrivals. Our self-trained network learns from a single domain to multiple domains while rejecting the wrong predicted picking and assigning the pseudo-first-arrivals-times to the unlabeled dataset. The model inherits the advantage from both single-and multi-domain picking strategies; it has high generalizability and noise robustness. In addition, the model is designed for a limited amount of manually annotated data to reduce human labor and total seismic processing time. In experiments on an open hard rock seismic multi-survey dataset, our picking method outperforms the benchmark on most evaluation metrics. Remarkably, our approach achieves approximately 10 % accuracy improvement against Lalor site data with a sampling rate that differs from training data. The experimental results demonstrate our method’s high generalizability and robustness for unseen datasets.