A Meta-Learning-Based Approach for Automatic First-Arrival Picking

Precise first-arrival picking holds pivotal importance in the realms of seismic data processing and microseismic monitoring. Recently, data-driven approaches have shown remarkable performance. However, these approaches rely on high-quality labeled datasets and involve a time-consuming and labor-intensive labeling process. In addition, data-driven picking methods often suffer from generalization problems in the face of varying noise characteristics and geological environments. To tackle the challenges head-on, this study introduces a novel training algorithm grounded in meta-learning. In contrast to traditional training methods, this innovative approach distinguishes itself by reducing the costs associated with dataset creation and requiring only a modest number of high-quality labeled samples to achieve superior performance. Furthermore, the proposed method can be seamlessly implemented with different types of deep neural networks (DNNs). Our extensive experimentation on two field datasets encompassing distinct geological zones demonstrates the method's effectiveness in alleviating the dependence on high-quality training samples, enhancing first-arrival picking accuracy, and bolstering the model's robustness against strong noise interference.