## 3 Data and Methods
### Seismic Data
To ensure the quality of the data, the signal-to-noise ratio (SNR) for all samples was calculated, and samples with exceptionally high or low SNR were excluded. Ultimately, this meticulous process yielded a dataset comprising 121,507 event samples annotated with manual phase labels, providing a rich and reliable foundation for the subsequent deep learning training and aftershock cataloging. For the generation of positive (seismic event) and negative (noise) samples, we employed a multifaceted approach integrating the STA/LTA, Kurtosis [29], and seismic association algorithms embedded within the PALM framework. This approach was applied to waveform data from the Ludian region to extract both event and noise samples efficiently. The Kurtosis algorithm was an automatic S-wave onset-picker, which used kurtosis-derived characteristic functions and eigenvalue decompositions on three-component seismic data. The Kurtosis algorithm outperformed the STA/LTA (short-term average/long-term average) algorithm in terms of accuracy and the number of picks, especially for S-wave. The detection of P and S arrival pairs for seismic events was accomplished using both the STA/LTA picker and the Kurtosis picker. Once all P and S arrival pairs were detected, the seismic association algorithm was employed to associate phase arrivals across all stations, thus forming distinct seismic events. These detected events constituted our event sample dataset. Noise samples were randomly extracted from 24 h continuous waveform recordings. A 30 s window was deemed a noise sample if it lacked any P or S phase arrivals. This method ensures the inclusion of diverse noise characteristics in the training dataset. In the end, we obtained 15,833 event samples. To augment the event samples, we added noise and randomly shifted time windows, resulting in a fivefold increase. A total of 75,327 noise samples were obtained through random selection. Since noise data were abundant and diverse, we did not perform data augmentation on the noise data to maintain its original complexity and diversity. After assembling all sample sets, we conducted a thorough analysis of various attributes within the dataset. This included assessing the distribution of signal-to-noise ratios, epicentral distances, azimuths, and the relationship between travel time and epicentral distance (see Figure 2). Such an analysis is essential to understand the dataset's diversity and to ensure that the deep learning models are trained on a representative and comprehensive sample of seismic data. For a visual understanding of these distributions, refer to Figure 2 in the paper. This figure illustrates the range and variability of the aforementioned attributes within the dataset, offering insights into the data's complexity and the challenges it poses for AI-based seismic analysis. This comprehensive preparation and analysis of the seismic AI training dataset lay the groundwork for the effective training of deep learning models, enabling them to accurately discern between seismic events and noise and to proficiently identify seismic phases, thereby contributing to the advancement of seismic research and aftershock analysis.