## 4 Accurate Earthquake Locating  
To ensure precise initial data for the subsequent \(b\)-value cross-section calculations, we need more seismic phase samples with more accurate arrival time data to form reasonable constraints on earthquake locating. We selected all \(M_{L}\geq 1.5\) earthquakes, over 130,000, recorded by 230 stations across Sichuan from 2009 to the end of 2022. To streamline the computation and mitigate large-scale ellipsoid correction errors, we divided Sichuan into \(2^{\circ}\times 2^{\circ}\) sub-areas. An AI-based auto-picker constructed by the CNN network was applied to refine and supplement the phase arrival times [52]. To keep the phase arrival time more accurate, we double-checked the phases whose arrival time differences were equal to or greater than 0.5 s between the AI and manual phase files. This workflow was proven to be valid and effective in the \(M_{S}\) 6.8 Luding earthquake sequence recognition [53]. We compared the results of manual and artificial intelligence recognition in a sub-area, and it can be seen that artificial intelligence corrected the errors of manual recognition and increased the amount of data by about 5% (Figure 3). The earthquake location procedure adopted here is described in the following. Initially, Hypoinverse2000 [54] was employed for initial locating in each sub-area using a unified velocity model [55], yielding absolute location results. Subsequently, tomoDD [56] was used for high-precision relative locating, leveraging a detailed 0.2\({}^{\circ}\)\(\times\) 0.2\({}^{\circ}\) three-dimensional velocity structure of the Sichuan-Yunnan area provided by [PERSON] et al. [57]. We then amalgamated the location results from each sub-area, prioritizing the relative location results from tomoDD (marked as 2, black circle in Figure 4a). Events that were not amenable to relative locating due to factors like \"air quakes\" or connection loss were assigned absolute locating results from Hypoinverse2000 (marked as 1, green circle in Figure 4a). The few remaining earthquakes with fewer than three observations, which could not be numerically calculated for source location, were cataloged using their original parameters (marked as 0, purple circle in Figure 4a). Figure 3: The Wadadi diagram in a sub-area: (a) manual picking; (b) refined and supplemented by the AI model. Red dots: P wave data, blue dots: S wave data. Ultimately, we identified 16,203 earthquakes with \(M_{L}\geq 1.5\) in the study area. The distribution of these events by flags 0, 1, and 2 is 689 (4%), 1959 (12%), and 13555 (84%), respectively. The average horizontal error for events flagged as 2 is less than 1 km, and the vertical error ranges between 1 and 2 km, as determined by the bootstrap test [58]. Flag = 0 events predominantly occur at the boundaries of the seismic network. The average deviation between flag = 1 and flag = 2 events is 0.4 km horizontally and 1.3 km vertically. The focal depth is mostly within 10 km (Figure 4b), but it escalates to nearly 30 km in a small area south of Shimian (SM), a phenomenon previously reported [59] that is yet to be fully explained. Given the occurrence of the Lushan \(M_{S}\) 6.1 earthquake on 1 June 2022 along the southern section of the Longmen Shan fault belt adjacent to our study area, we designated the study period from 2009 to 31 May 2022 to preclude the influence of seismic sequences and mainshock-triggered effects. We computed the minimum magnitude of completeness (\(M_{C}\)) using the Maximum Curvature method [60,61] with a 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) step and a 30 km search radius. The \(M_{C}\) predominantly hovers around \(M_{L}\) = 1.5 across the study area, coinciding with our earthquake locating work's starting magnitude. It increases to about \(M_{L}\) = 1.7 south of Ganzi (GZ) due to fewer stations, and, near Qiaojia (QJ) in the southern study area, the \(Mc\) ascends to \(M_{L}\) 1.8-1.9, being at the seismic network's edge (Figure 5a).