First-Arrival Picking for Out-of-Distribution Noisy Data: A Cost-Effective Transfer Learning Method With Tens of Samples

Data-driven methods for picking the first-arrival of seismic waves can encounter challenges with generalization when they are faced with out-of-distribution data that falls outside their training set. Transfer learning is a promising technique for boosting the method’s generalization. However, transfer learning necessitates a considerable number of labeled samples to fit the target domain data, which restricts its application in tasks characterized by small sample sizes and real-time constraints. In response to these challenges, this article introduces a cost-effective transfer learning approach designed to mitigate generalization issues caused by out-of-distribution noise. The central innovation of our method, which we refer to as prior knowledge-guided transfer learning (PG-TL), lies in the efficient utilization of prior knowledge concerning target domain noise. The PG-TL method adopts a parallel network architecture, comprising a backbone network and a branch network. The backbone network provides a foundation of universal knowledge for first-arrival picking, which is then refined and adapted by the branch network to address the specific noise challenges of the target domain. Through validation with field data from different regions and different noise levels, the PG-TL method exhibits robust applicability, achieving performance levels comparable to traditional transfer learning approaches but with significantly reduced reliance on samples’ only requiring dozens for effective implementation.