## 1 Introduction
Compared to traditional methods, they all showed improvements in detection accuracy, completeness, and computational efficiency. However, they shared a common issue: significant variation in generalizability across different tectonic regions. In the field of AI earthquake datasets, [PERSON] et al. [23] published the Stanford Earthquake Dataset (STEAD), the first publicly released benchmark dataset for training AI seismic models, which contains approximately 3 million global seismic and noise records. However, most seismic data in STEAD originated from Europe and the USA, with only a small portion originating from mainland China. Later, [PERSON] et al. [24] launched the DiTing dataset, which primarily features seismic benchmark AI data from mainland China, including about 780,000 earthquake events. Additionally, phase association technology was a crucial aspect following phase detection that impacted earthquake localization. [PERSON] et al. [20] developed a Bayesian-based hybrid model for this purpose, enhancing the accuracy and efficiency of seismic phase association. Furthermore, to enhance the efficiency of the earthquake location workflow, [PERSON] et al. [21] and [PERSON] et al. [22] developed comprehensive workflow frameworks called LOC-FLOW and PLAM (Phase picking, Association, Location and Matched Filter), respectively. Both frameworks offered flexible interfaces to overcome format barriers between multiple processing steps and employed traditional methods for earthquake association. The main difference between these frameworks was that LOC-FLOW used a grid search for association and localization, which had lower computational efficiency, while PALM achieved association based on temporal and spatial differences between different stations, resulting in improved computational efficiency. Despite significant progress over the past years, data-driven deep learning technology still encounters big challenges when confronted with complex regional structures and observation conditions in the real world. [PERSON] et al. highlighted that, when the EQtransformer and PhaseNet were simultaneously utilized for detecting the aftershock sequences of the 2021 Yangbi earthquake and the 2021 Maduo earthquake, the catalogs produced by the two AI models exhibited significant differences. This inconsistency be tween the two catalogs indicated that the regional tectonic structures exerted a significant influence on the generalization of AI models. In this study, we designed a novel training strategy by retraining the AI model on a small sample set to improve its generalization in specific regional tectonic structural scenarios. This retraining strategy combined the advantages of AI algorithms and the traditional short-long window algorithm (STA/LTA) [13]. We utilized the PALM method to obtain seismic/noise sample sets specific to the local region, which were then used to train a CNN model. Simultaneously, a manually picked P and S phases dataset was employed to train an RNN model. The CNN model was employed for detecting seismic events, while the RNN model was dedicated to picking seismic phases. Finally, the retrained AI model was applied to the 2014 Ludian aftershocks, resulting in a high-resolution catalog. Our results not only offer essential foundational data for the study of small seismic activities in the Ludian region but also provide a viable solution to the current challenges of generalization in AI phase detection.