###### Abstract
This led scholars to focus on studies related to earthquake stress triggering. In Southern California, [PERSON] et al. found that large earthquakes on the northern San Jacinto Fault could cause cascading ruptures in the Sierra Madre-Cucamonga system by mimicking dynamic shear stress variations between strike-slip and reverse-strike faults and static Coulomb stress changes [17]. [PERSON] et al. assessed the stress changes related to both co-seismic and post-seismic deformation following two major earthquakes of 9.2 \(M\)w and 8.7 \(M\)w on the Great Sumatra Retroflex Fault based on the Coulomb failure stress theory [18]. [PERSON] et al. examined the effects of the Wenchuan earthquake (7.9 \(M\)w) by simulating Coulomb stress changes along the Xianshuihe-Xiaojiang Fault System [19]. A considerable body of research has shown that both the Coulomb failure stress theory and the stress trigger theory serve as effective indicators for evaluating seismic hazards [20; 21; 22; 23; 24]. The EAFZ has not recorded a significant number of large earthquakes exceeding 7 \(M\)w in the past century. The 7.7 \(M\)w and 7.6 \(M\)w earthquakes of 6 February 2023, were bound to release much energy into the surrounding area. This energy will likely disrupt the stress balance on the surrounding faults, triggering new earthquakes. Previous studies on major earthquakes near the EAFZ have focused solely on Coulomb stress changes induced by individual large seismic events [1; 21; 25; 26]. Current research in the EAFZ region remains limited regarding the long-term spatiotemporal evolution of Coulomb stresses. To address this gap, we investigate Coulomb stress variations generated by earthquakes exceeding magnitude 6 near the EAFZ from 1986 to 2023. Our analysis examines the spatiotemporal evolution of Coulomb stress in this region and evaluates potential triggering relationships between successive earthquakes. While there are many studies that use machine learning techniques to assess seismic risk, such as [PERSON] et al. 's study of machine learning-based seismic vulnerability and susceptibility assessment of reinforced concrete buildings, the study in this study differs from such studies in both scope and methodology [27]. While [PERSON] et al. [27] utilized machine learning to predict structural responses to seismic events, our work employs geophysical modeling and stress tensor inversion to analyze the spatial and temporal evolution of Coulomb stress changes induced by historical earthquakes. This allows us to assess how stress changes influence the likelihood of future seismic events, providing a complementary perspective to structural-based risk assessments. By integrating stress tensor inversion and Coulomb stress calculations, we offer a more comprehensive understanding of the seismic hazard in the EAFZ region, which is critical for both structural engineering and geophysical earthquake forecasting. To investigate the Coulomb stress development in the vicinity of the EAFZ, we selected earthquakes with magnitudes exceeding 6 \(M\)w that occurred between 1986 and 2023 for our analysis. The \(\Delta CFS\) around the EAFZ is analyzed from both spatial and temporal aspects. In addition, we calculate the specific values of \(\Delta CFS\) induced by these significant earthquakes (6 \(M\)w) for subsequent seismic events and discuss their impact on ensuing seismicity.