Developing seismo-volcanic monitoring tools is crucial for volcanic observatories. Our study reviews current methods using transfer learning techniques and finds that while these systems identify nearly 90 % of seismic events, they miss other important volcanic data due to the catalog-learning bias. We propose a weakly supervised technique to reduce bias and uncover new volcanic information. This method can improve existing databases and efficiently create new ones using machine learning.