## 3 Data and Methods  
### Seismic Data  
In this research, we gathered comprehensive seismic data to construct a high-resolution aftershock catalog of the 2014 Ludian earthquake. The data collection involved continuous waveform and manually picked phase data from the Sichuan Seismic Network, the Yunnan Seismic Network, and the Qiaojia-Ludian seismic station network. The Qiaojia-Ludian network consisted of 12 permanent stations and 12 campaign stations, which were deployed near the Xiaojiang-Zhaotong-Ludian Fault Zone, allowing for a robust coverage of the 2014 Ludian earthquake. The campaign stations used Guralp CMG-3T seismometers (Guralp Systems Ltd., Aldermaston, UK) and Reftek 130S data loggers (Trimble Navigation Ltd., Sunnyvale, CA, USA), with a frequency range of 120 s-50 Hz. The permanent stations used Guralp CMG-3 ESPC seismometers (Guralp Systems Ltd.) and EDAS-24I data loggers (Beijing Gangzhen Technology Development Co., Ltd., Beijing, China), with a frequency range of 60 s-50 Hz, and the seismometers are mostly installed on bedrock. The initial step in data handling was a rigorous assessment of data quality from all seismic stations. This involved evaluating the continuity of waveform data and excluding stations with consistently poor data quality. Then, we meticulously selected 12 campaign stations and 12 regional permanent stations to locate Ludian aftershocks, considering data quality and geographical significance. The selection criteria aimed to achieve an optimal balance between station quality and location to enhance aftershock location effectiveness. The observation period for these 24 seismic stations spanned from 1 August 2014 to 31 December  
Figure 1: The tectonic background, seismic station distribution and historical large earthquakes in the study area. Blue triangles and green triangles represent the permanent stations and campaign stations, respectively. The red circles represent the historical earthquakes greater than 7.0. The red star represents the 2014 Ms 6.5 Ludian earthquake. The black solid curves represent the primary active faults, and the blue solid curves represent the block boundary in China’s mainland. 2015, with data continuity exceeding 90%. To verify the consistency of instrument responses, especially in campaign stations, we conducted comparative analyses using instrument responses to surface waves from global earthquakes greater than _M_s 7.0. This comparison, which involved examining recordings from different stations for the same global seismic events, focused particularly on the response to long-distance seismic surface waves. This procedure confirmed the consistency of instrument responses across stations, which was crucial for the accurate determination of earthquake magnitudes. The construction of the seismic AI training dataset was a crucial step in the development of a reliable and effective deep learning model for seismic event detection and phase picking. Our dataset was structured into three distinct sets: seismic event samples (positive), noise samples (negative), and seismic phase-picking samples. The CNN model was tailored to learn from the event and noise samples, while the RNN model was specifically trained using the seismic phase-picking samples. For the generation of phase-picking samples, 55 regional permanent stations from the Sichuan Seismic Network were selected to create sample datasets for training the RNN model. These seismic records, encompassing continuous waveforms and P- and S-wave phase data, were collected from 1 January 2008 to 31 December 2012. The accuracy of the P- and S-wave phase arrivals was ascertained using the theoretical travel time versus epicentral distance relationship. We extracted earthquake events from 24 h continuous waveforms based on the arrival times of P- and S-wave phases. These events were then represented as 30 s seismic waveform segments, with the P- and S-wave arrival times annotated on each segment.