The present toolkit is developed to address the significant challenge of limited labelled data in seismological studies, a preliminary drawback in the application of deep learning and machine learning. Data augmentation has emerged as a simple but effective solution to this problem to enhance model performance. It significantly mitigates overfitting by increasing the volume of training data and introducing variability, thereby improving the model's performance on unseen data. Additionally, data augmentation helps to balance the imbalanced classes of a dataset by creating more examples of under-represented classes. Given the rapid advancements in deep learning for seismology, ‘**SeisAug**’ assists in extensibility by building on standardized frameworks and offering open access.
**Full Changelog**: https://github.com/ISR-AIML/SeisAug/commits/SeisAug(V_0.1)