## 3 Data and Methods
### Earthquake Detection, Phase Picking, Association and Location  
Utilizing the retrained CNN and RNN models on continuous waveform data, we conducted earthquake detection over a 30 s window with a 15 s sliding step, applying a 1-20 Hz bandpass filter to 24 h three-component waveforms. P- and S-wave phases were concurrently picked within this framework, and surface wave amplitudes were quantified within an amplitude window extending from 1 s pre P-wave to 5 s post S-wave. After obtaining the P- and S-wave phases for all stations, clustering of phase arrival times is achieved using a threshold of 2.0 s for grid search travel time residual, and a requirement  
Figure 4: Detection accuracy and picking uncertainty over the course of CNN and RNN training iterations. (**a**,**c**) The training accuracy and validation accuracy, respectively, for event samples and noise samples during the iteration process of the CNN model. Here, the red curve denotes the training accuracy, while the blue curve represents the validation accuracy. (**b**,**d**) The time step accuracy and picking uncertainty, respectively, during the iteration process of the RNN model, where the red curve indicates the accuracy on the training set, and the blue curve signifies the accuracy on the validation set. of at least 4 stations simultaneously recording the same seismic signal. This process culminated in initial detections comprising 3624 seismic events and 25,125 P- and S-wave phase arrivals. A juxtaposition of the AI-determined arrival times with those manually picked revealed an average temporal discrepancy for P-waves of 0.02 s (standard deviation of 0.32 s), and for S-waves, an average discrepancy of 0.11 s (standard deviation of 0.44 s). For absolute earthquake localization, the Hypoinverse software (Version 1.40) was harnessed. We adopted an average strategy for the initial velocity model, utilizing an averaged three-layer model of [PERSON] et al. (Figure 5) [25]. Throughout the iterative inversion process, station weights were modulated based on the root mean square of the travel time residuals and epicentral distance. A residual cutoff threshold was established, affording full weight to stations with residuals under 0.3 s, nullifying weights for residuals exceeding three times the cutoff residual (0.9 s), and implementing weighted interpolation for intermediate values according to a cosine function curve. A distance cutoff was set at 40 km, with a cutoff range spanning 40-120 km. Subsequent to the absolute localization, relative localization was performed employing the HypoDD algorithm (Version 1.3). The double-difference method incorporates an initial relative location derived from travel time measurements, further refined by cross-correlation to correct temporal disparities, thereby augmenting the precision of the relative locations. The parameters included a maximum station-event distance of 150 km, an event-pair distance constraint of 6 km, and a minimum of 8 phases per event pair. After two cycles and eight iterations, the inversion parameters, inclusive of travel time residuals and horizontal and vertical discrepancies, were stabilized. During the cross-correlation for travel time difference calculations (cc), the maximum distance between events pairs was set to 4 km, and the maximum epicentral distance for stations was 120 km. Template windows are defined as a P-wave before 0.5 s and after 3.5 s, and S-wave before 0.3 s and after 4.5 s, using a 2-15 Hz bandpass filter. The velocity model is the same as the one used by [PERSON] et al. (Figure 5). Finally, we obtained high-precision location results for 3286 events. The Frequency-Magnitude Distribution (FMD) between the AI catalog of this study and those compiled by [PERSON] et al. and the China Earthquake Network Center (CENC) was contrasted (Figure 6). This comparative analysis indicated that the AI-generated catalog exhibits superior detection capabilities relative to the catalogs by [PERSON] et al. [25] and CENC. Figure 5: The velocity model used in the Ludian region.