Distribution and failure mechanism of landslides associated with seismic response of the slope during the 2022 Lushan Ms6.1 earthquake

Distribution and failure mechanism of landslides associated with seismic response of the slope during the 2022 Lushan Ms6.1 earthquake | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distribution and failure mechanism of landslides associated with seismic response of the slope during the 2022 Lushan Ms6.1 earthquake Haochen Wu, Yunsheng Wang, Jianxian He, Yonghong Luo, Gang Jin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4717589/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, the frequent occurrence of intense seismic events in the mountainous regions of western China has led to numerous geological disasters, resulting in significant human casualties and extensive property damage. Understanding the seismic response of slopes is crucial for elucidating the failure mechanism of earthquake-induced landslides. The distribution of geological landslides and the seismic response of slopes in Lushan are examined through post-earthquake field investigations, landslides inventories, and comprehensive field monitoring. The landslides triggered by the earthquake were primarily concentrated along both banks of the Donghe River in Baoxing County, predominantly manifesting as rockfalls. Geological disasters are predominantly occurred along fault zones and water systems, where vulnerabilities are heightened near 1 km of these faults. The topographic features, lithological composition, and rock mass structure significantly influences the Peak Ground Acceleration (PGA). Notably, PGA experience a sudden increase in areas with slope breaks and loose soil layers, leading to initiation location of the landslide. In the monitoring profile, the PGA amplification factors increases significantly along the slope surface: PGA at the upper slope is 1 to 2 times greater than that of the hilltop reference point, and within the loose soil layer, it ranges from 1.5 to 3.0. Seismic waves in the 1–5 Hz frequency range are notably amplified in this profile, as evidenced by analysis of the Fourier spectrum and Horizontal to Vertical Spectral Ratio (HVSR) curve. The monitoring profile data reveals that site conditions have a pronounced influence on the amplitude of the acceleration, surpassing the magnification effects of terrain and elevation. In disaster investigations, deviations in the development of disasters from the epicentral area are observed, especially in regions with complex geological structures like nappe tectonics. In such cases, it is crucial to emphasize the impact of both the macroscopic and microscopic epicentershaode. Additionally, more attentions should be paid to understanding the seismic response of slopes, particularly concerning earthquake-triggered landslides. Lushan earthquake Filed observation Macroscopic epicenter Seismic response of the slope Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1 Introduction Over the past few decades, earthquake-induced geological disasters have inflicted substantial human casualties and property damage. For instance, the Wenchuan earthquake resulted in the loss of approximately 87,000 lives and an economic impact totaling 845.1 billion RMB. Geological disasters directly contributed to more than 30% of the casualties during this event (Wang and Jin 2022 ; Wang et al. 2008 ; Dai et al. 2011 ; Yin et al. 2009 ; Li 2008 ). The Wangjiayan landslide tragically resulted in the loss of more than 1,600 lives, with over 500 individuals buried in Beichuan County (Wang and Jin 2022 ; Wang et al. 2009 ). The landslides triggered by the Ms7.0 earthquake in Lushan, Sichuan province, in 2013 led to the tragic loss of 196 lives and injuries to 11,470 individuals (Meng et al. 2014 ; Wang et al. 2014b ). Ninety-three people lost their lives due to massive landslides triggered by the Luding Ms6.8 earthquake in Sichuan province in 2022 (Tie et al. 2022 ). The loess landslides and rockfalls induced by the Ms6.2 earthquake led to more than 900 injuries and 151 fatalities in Jishishan, Gansu province (Li et al. ; Liu et al. ; Pang et al. ; Xv et al.). To reveal the failure mechanisms and predict earthquake-triggered landslides, numerous studies have focused on understanding the development of geological disasters influenced by the dynamic response of slopes. Seismic geological disasters are primarily triggered in strip-shaped or narrow ridges, as well as on convex mountaintops (Kargel et al. 2016 ; Korup et al. 2007 ; Wang and Jin 2022 ; Zhao et al. 2022b ; Huang and Li 2014 ). Numerous theoretical and observational studies have consistently demonstrated that topographic features, such as ridges and cliff faces, can induce significant increases in ground acceleration (Hartzell et al. 1994 ; Spudich et al. 1996 ; Assimaki et al. 2005 ). Hough et al. ( 2010 ) discovered that many sturdy, relatively well-built buildings along the ridge in the study area suffered considerable damage during the examination of the 2010 Ms7.0 earthquake in Haiti (Hough et al. 2010 ). By employing monitoring instruments in the valley, Hough determined that the PGA amplification effect caused by the overburden was approximately 1.8 times greater than that observed at the hard rock reference point (Hough et al. 2010 ). Luo's study identified higher magnification factors near the hilltop and smaller factors near the foot of the slope. Amplification effects are most pronounced at slope breaks, peaks, or steep inclines, where geological disasters triggered by earthquakes often occurred. As altitude increases, the PGA amplification factors derived from HVSR curves range from 2.4 to 6.0. Notably, the PGA amplification factor at the lower elevation monitoring site (Q5, 5.2) exceeds that at the higher elevation monitoring site (Q6, 3.8). Meanwhile, the PGA amplification coefficient at the Q5 monitoring point (5.2) closely aligns with that of the Q11 monitoring point (5.3) (Luo et al. 2014 ; Luo et al. 2020 ). The aftershock data collected from the low hilly area indicate that the PGA amplification factors in three directions (E-W, N-S, U-D) at lower elevations are 2.32, 4.28, and 2.22 respectively, while at higher elevations they are 1.42, 2.24, and 0.95 respectively. The difference between the two monitoring sites lies in their geological conditions: the low elevation site has a loose cover layer, whereas the high elevation site is bedrock (Wang and Jin 2022 ). The geological disasters triggered by the Ms7.0 earthquake in Jiuzhaigou in 2017 were predominantly concentrated on hilltops and prominent ridges, potentially linked to terrain amplification. Monitoring data indicates that the peak ground acceleration (PGA) on prominent ridges is 4–11 times greater than that at the foot of the mountain (Zhao et al. 2018 ). The geological disasters triggered by earthquakes are attributed to the amplification of PGA. This amplification effect is influenced by topographical features, lithology, and site conditions (He et al. 2021 ; Luo et al. 2021 ; Panzera et al. 2017 ; Zhao et al. 2021 ). Therefore, exploring the seismic response of the slope during the earthquake is crucial for understanding disaster triggering mechanisms. This paper focuses on utilizing the Lushan Ms6.1 earthquake as a case study to investigate the development and distribution of geological hazards triggered by earthquakes in deep canyon landforms. By integrating multiple recorded seismic data, we explore the seismic response of slopes in deep canyon areas. Furthermore, we discuss the impact of slope seismic response on the evolution of seismic-triggered geological hazards. Exploring the seismic response characteristics of slopes can offer valuable insights into seismic response parameters for the construction of numerous engineering facilities and the enhancement of human production and life in the mountainous gorge geomorphic areas of western Sichuan, China. 2 Study area 2.1 Geological setting On June 1, 2022, an Ms6.1 earthquake struck Lushan County, Sichuan Province, occurring in the Longmenshan fault zone. The earthquake is classified as a thrust event. The nodal parameters of the focal mechanism are as follows: Nodal I - strike/dip/slip angles of 221.5°/44.4°/103.3°; Nodal II - strike/dip/slip angles of 23.2°/47.1°/77.3°, with a centroid depth of 15.5 km. The seismogenic fault dips southeastward and is situated at the southern end of the Longmenshan fault zone (Chen et al. 2023 ; Lu et al. 2022 ; Ma et al. 2023 ). Since around 50 million years ago, the collision between the Indian Plate and the Eurasian Plate has uplifted the Tibetan Plateau. The plateau continues to move eastward. The Longmenshan fault zone formed along the boundary between the Tibetan Plateau and the Sichuan Basin due to the obstruction caused by the rigid nature of the basin (Zhao and Su 2024 ; Chen et al. 2000 ; Densmore et al. 2007 ; Gan et al. 2007 ; Jia et al. 2006 ; Tapponnier et al. 2001 ; Wang et al. 2014a ; Wang et al. 2001 ; Xu et al. 2015 ; Yin and Harrison 2000 ). The Longmenshan thrust zone consists of three primary fault systems: the Maoxian-Wenchuan fault (also known as the back-range fault), the Yingxiu-Beichuan fault (also known as the central fault), and the Guanxian-Jiangyou fault (also known as the front-range fault) (Xu et al. 2015 ). Due to its geological structure, the junction area between the Longmenshan fault zone and the foreland basin is characterized by northeast-oriented structures. This region includes the Longmenshan thrust nappe tectonic belt, the western Sichuan foreland basin, the nappe-detachment superimposed subbelt, and the foreland fold belt. Following the collision, the Indian plate continued its northward movement, resulting in the accumulation of substantial strain energy. The displacement rate of the Bayan Har block decreased from 10 mm/yr to 6 mm/yr between 1984 and 2014. This period saw significant strain energy accumulation near the Longmenshan fault zone. As a result of the continuous release of this accumulated strain energy, the Longmenshan fault has become one of the most seismically active regions in China (Parsons et al. 2008 ; Shen et al. 2009 ; Wang et al. 2014a ; Xu et al. 2015 ; Zhao et al. 2015 ; Zhao et al. 2022a ; Zhao et al. 2018 ). The Wenchuan Ms8.0 earthquake in 2008, the Lushan Ms7.0 earthquake in 2013, and the Jiuzhaigou Ms7.0 earthquake in 2017 occurred along the Longmenshan fault in recent decades. The epicenter area is situated in Lushan County and Baoxing County, Ya'an City, Sichuan Province, China (102.41°E-103.18°E, 30.01°N-30.93°N). The epicenter of the Lushan Ms6.1 earthquake was located within this region (102.94°E, 30.37°N). The central seismic zone is situated at the convergence of the Northwest Sichuan Inverted Triangle Block, Bayan Har Block, Sichuan-Yunnan Rhombohedral Block, and Sichuan Block. The stress within the target area continuously accumulates due to the compression between multiple tectonic plates. The study area, predominantly characterized by undulated mountainous topography, spans elevations from a low of 557 m to a high of 5290 m above sea level. The northwestern region features relatively high terrain, whereas the southeastern part is characterized by low-lying topography (Fig. 1 ). The terrain is characterized by deep canyons. The strata in the area are completely exposed, ranging from the Upper Proterozoic Sinian to loose Quaternary strata, with the absence of Cambrian and Carboniferous strata. The lithology in the study area consists primarily of sandstone, mudstone, shale, slate, and phyllite. Figure 1 Regional geological map of 2022 Lushan earthquake (F1: Minjiang fault, F2: Longmenshan fault zone, F3: Xianshuihe fault, F4: Anninghe fault, F5: Zemuhe fault, F6: Xiaojiang fault, F7: Huayingshan fault, F8: Jinshajiang fault, F9: Ganzi-Litang fault, F10: Litang-Dewu fault, F11: Yulongxi fault) 2.2 Distribution of the earthquake-induced landslides After the earthquake, the investigation group immediately entered Baoxing County, where the disasters were concentrated. Based on the aftershock of the Lushan Ms6.1 earthquake recorder by the National Earthquake Data Center ( https://data.earthquake.cn/ ), the distribution of aftershocks has an elliptical shape with a short axis. The aftershock primarily concentrated within the zone bounded by the Guanxian-Jiangyou fault (Longmenshan front-range fault) and the Yingxiu-Beichuan fault (Longmenshan central fault). Figure 2 Mainshock and aftershock distribution map; disasters distribution map Based on the seismic intensity map (Fig. 2 ), the contour map shows an elliptical shape oriented in the northeast direction. The seismic intensity reached a maximum of VIII degree near the epicenter. More than 238 geological disasters were triggered by the earthquake, comprising 171 small-scale, 52 medium-scale, 12 large-scale, and 3 giant geological disasters. Near the epicenter in Lushan County, there were no significant geological disasters observed. Only 5 disasters were triggered in Lushan County, with the remaining geological disasters occurring in Baoxing County. Lushan County is located in the VIII degree seismic intensity zone, whereas Baoxing County is in the VI degree zone. Surprisingly, the distribution of disasters is predominantly concentrated in the low-intensity region, which contradicts conventional cognitive patterns (Huang and Li 2009b ). Geological disasters primarily concentrate on near faults, showing a linear pattern along seismogenic faults. The linear density of disasters in Baoxing is 3.09 per kilometer. The density of disaster development and distribution decreases as the distance from the fault increases. Geological disasters demonstrate a spatial distribution closely aligned with river networks, particularly evident in their proximity to the Baoxing river system and seismogenic structures. Following an earthquake event, all triggered geological disasters were found in close proximity to river systems, constituting 100% of the total occurrences. Specifically, within the Baoxing river area, 58 geological disasters occurred, with 77.58% (45 disasters) observed on the left bank. This asymmetric distribution highlights a greater incidence and scale of disasters on the left bank compared to the right. The occurrence of disasters is predominantly localized within a radius of 1 km from fault lines and water systems (Huang 2009 ). 2.3 Earthquake-induced landslides types The Lushan Ms6.1 earthquake induced landslides characterized by diverse failure mechanisms. As shown in Fig. 3 A, the failure mechanism of the landslide on the right bank was identified as shattering-sliding type. Initially, this involves the presence of bedding or weak structural planes within the inner slope. Subsequently, seismic activity loosens or penetrates these structural planes. Finally, the slope rapidly slides along these weakened structures or bedding planes due to the sustained impact of a powerful earthquake force. As illustrated in Fig. 3 A, the landslide occurred on the right bank of the river, where the rock strata outside the inclined slope created favorable conditions for earthquake-induced landslides. Despite these conducive conditions, the scale of the landslide was relatively small, affecting only a portion of the hazardous rock mass near the base of the slope. The sliding occurred along a planar surface, resulting in debris accumulation at the foot of the slope (Li et al. 2015 ). As illustrated in Fig. 3 B (Huang 2009 ), the rear edge of the slope exhibited multiple sets of cracks oriented in various directions, forming distinct groups of wedges of different sizes. Under the influence of gravity, these cracks continued to propagate. During an earthquake, the strained rear edge underwent catastrophic failure, leading to the detachment and descent of large-scale wedges. These massive stone blocks were forcefully ejected and impacted the slope surface, fragmenting into smaller gravel pieces that accumulated within the gullies. The type of landslide observed in Fig. 3 C is a shallow surface landslide. The bedrock surface beneath the slope is overlain by residual slope deposits. When subjected to seismic loads, seismic waves within the overlying layer undergo multiple reflections and refractions, resulting in the accumulation of energy. The accumulation of energy in this process triggers the sliding of the overlying layer along the basement interface, initiating from the break in the slope. The material resulting from these shallow landslides is subsequently deposited on the surface of the slope. Figure 3 Earthquake-induced landslides The wedge illustrated in Fig. 4 A are formed by multiple sets of internal cracks within the slope, which develop on the right bank of the river. These cracks undergo slip under seismic loads and rainfall conditions. Over time, these internal cracks gradually evolve into sliding surfaces, leading to slope instability. The soil landslide triggered by the M6.1 Lushan earthquake is depicted in Fig. 4 B. This high-slope landslide originated on the left bank of the river. The landslide material moved at high speed, scraping the flow area, quickly crossing the riverbed, accumulating on the valley floor, and forming a barrier lake. Generally, the inner side of the highway is the source excavation slope, while the outer side is the source accumulation slope. The material is deposited on the bedrock outside the highway. During the earthquake, the low natural vibration frequency of the material leads to the development of ground cracks. (Fig. 4 C). Figure 4 Geological disasters triggered by the Lushan Ms6.1 earthquake 3 Seismic response of the slope 3.1 Data acquisition and monitoring profile The monitoring profiles along the Xianhuihe, Tuosuohu, and Huayingshan faults have been established by the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (SKLGP) since 2008. The stations are equipped with the G01NET-3 seismic dynamic data acquisition instrument, developed by the Institute of Engineering Mechanics, China Earthquake Administration, featuring a fundamental sensitivity parameter of 1.1V/G. The earthquake monitoring profile is situated in Shimian County, Ya'an City, Sichuan Province, China, approximately 137 km from the epicenter. This region exhibits high seismic activity, establishing it as one of most seismically active areas China (He et al. 2020 ; Allen et al. 1991 ). The monitoring profile extends northeastward along the Nanya River, featuring a vertical elevation difference of approximately 750 m. The profile features an average slope angle of approximately 40°. The lithology comprises early Sinian granite, with residual slope deposits covering the slopes on both sides. Monitoring site 1# is located on the relatively flat bedrock of an adit, situated at an elevation of 1160 m within the Jigongshan Mountains, approximately 410 m above the riverbed. The lithology predominantly comprises granite. Monitoring site 2# is positioned on the ridge in the upper half of Jigongshan Mountain, at an elevation of 1060 m, approximately 310 m higher than the riverbed. Site 3#, situated at an elevation of 900 m above sea level, is about 150 m distant from the riverbed. The overburden thickness ranges from 10 to 15 m and consists of strongly weathered granite layers. A fault known as the Shimian fault has been identified in close proximity to site 3#. (Fig. 5 ). Several earthquakes had been recorded at the monitoring profile, notable including Lushan Ms6.1 earthquake in 2022, Lushan Ms4.5 earthquake in 2022, Changning Ms6.0 earthquake in 2019 and Changning Ms5.4 earthquake in 2019 (Table 1 ). Figure 5 Monitoring profile of seismic station Table 1 Seismic data Date Time Lat (°N) Lon (°E) Depth (km) Ms 1 01/06/2022 17:00:08 30.37 102.94 17 6.1 2 01/06/2022 17:03:09 30.37 102.92 18 4.5 3 17/06/2019 22:55:43 28.34 104.90 16 6.0 4 22/06/2019 22:29:56 28.43 104.77 10 5.4 3.2 Data analysis and results The time history of acceleration was baseline corrected and filtered using a Butterworth bandpass filter set to a frequency range of 0.5–30 Hz in MATLAB. Subsequently, parameters such as Fourier spectrum and Arias intensity, along with other relevant metrics, were analyzed to investigate the seismic response characteristics of the slope. PGA and Arias intensity analyses The acceleration time history curve is derived from processed seismic data (Fig. 6 ), capturing the peak ground acceleration (PGA) and Fourier spectrum of the earthquake (Fig. 7 , Fig. 8 ). Arias intensity serves as a fundamental metric for quantifying vibrational energy, reflecting the continuous transformation of kinetic and potential energies of objects during vibration, consistent with the principles of energy conservation. The mass of the vibrating object remains constant throughout the seismic process. Velocity is utilized to characterize its kinetic energy. The Arias intensity formula entails integrating the square of the acceleration of the object over time during vibration, as exemplified by Formula (1): $${\text{I}}a=\frac{\pi }{{2g}}\int\limits_{0}^{{td}} {\mathop {\left( {at} \right)}\nolimits^{2} } dt$$ 1 where \(Ia\) is the Arias intensity, \(td\) is the vibration duration, is the gravitational acceleration. Figure 6 Lushan Ms6.1 mainshock time history curve The comparison of multiple datasets in Fig. 6 highlights the performance enhancement of horizontal (E-W, N-S) PGA at monitoring points. Horizontal amplitudes generally exceed the vertical (U-D) amplitude across all points, except for the 1# monitoring site during the Ms6.1 mainshock. Specifically, the amplitudes in each direction at the 1# monitoring point are lower compared to other sites, whereas the 3# monitoring point has the highest amplitudes among all. The peak of the time history curve corresponds to the PGA. Horizontal PGA (E-W, N-S) values also consistently exceed vertical PGA (U-D) values across all sites during the Lushan Ms4.5 earthquake. The PGA recorded at the 1# monitoring site is the smallest, while the PGA at the 3# monitoring site is the largest. Arias intensity is primarily influenced by earthquake acceleration and vibration duration. The Arias intensity values at each monitoring point exhibit a pattern similar to that of PGA. Specifically, Arias intensity values are higher in the horizontal directions (E-W, N-S) compared to the vertical direction (U-D). The Arias intensity at monitoring point 1# is relatively lower than at other sites, while monitoring point 3# records the highest Arias intensity among all stations. At the same monitoring profile, the seismic response parameters of slope during the Lushan Ms6.1 and Ms4.5 earthquakes show a nonlinear increasing trend in magnitude. Moreover, Arias intensity increases with higher earthquake magnitudes. The PGA and Arias intensity of the Changning Ms6.0 and Ms5.4 earthquakes follow a similar pattern to those observed during the Lushan earthquakes. Specifically, the PGA and Arias intensity of the Lushan Ms6.1 earthquake exceed those of the Lushan Ms4.5 earthquake, while the PGA and Arias intensity of the Changning Ms6.0 earthquake also surpass those of the Changning Ms5.4 earthquake (Table 2 ). Table 2 Seismic response parameters of slope Earthquake Site PGA/(gal) Arias Intensity/(dm·s − 1 ) Predominant Frequency/Hz E-W N-S U-D E-W N-S U-D E-W N-S U-D Lushan Ms6.1 1 # (1160m) 3.287 3.709 4.323 0.30 0.31 0.47 6.055 5.489 6.139 2 # (1060m) 4.790 7.099 3.858 0.73 0.79 1.14 6.377 8.475 6.341 3 # (900m) 10.738 8.904 7.383 1.95 1.62 1.55 5.865 7.826 5.871 Lushan Ms4.5 1 # (1160m) 2.429 1.977 2.128 0.08 0.09 0.12 5.912 5.537 6.586 2 # (1060m) 2.656 4.325 3.678 0.18 0.26 0.27 6.335 8.559 6.335 3 # (900m) 5.182 6.111 4.777 0.53 0.48 0.41 8.004 6.74 6.74 Changning Ms6.0 1 # (1160m) 1.303 2.455 1.848 0.04 0.12 0.09 0.960 2.000 1.020 2 # (1060m) 2.457 3.864 2.421 0.06 0.19 0.08 3.167 3.198 3.162 3 # (900m) 8.643 7.732 2.908 1.56 1.36 0.25 1.270 1.019 2.612 Changning Ms5.4 1 # (1160m) 1.006 1.576 0.953 0.03 0.08 0.03 1.21 2.95 0.94 2 # (1060m) 1.514 1.578 1.485 0.05 0.05 0.06 3.90 2.33 3.99 3 # (900m) 2.851 4.613 1.960 0.47 0.18 0.04 1.21 1.18 2.83 Fourier spectrum analysis As illustrated in Fig. 7 and Fig. 8 , the frequency contents of the three directions (E-W, N-S, U-D) at the monitoring points during the same earthquake do not show significant differences in obtaining peak values. At the monitoring point, significant amplitudes are observed in the frequency range of 5–10 Hz, particularly during the Ms6.1 earthquake, which shows larger amplitudes. Amplitudes in the high-frequency band are generally lower than those in the low-frequency band. Among the monitored points, the 1# monitoring point records the smallest amplitude peak, while the 3# monitoring point registers the largest, augmented by the overlay site. The predominant frequency range of the Changning earthquake is primarily distributed within the 1–3 Hz band. The Fourier spectrum amplitude of the Lushan Ms6.1 earthquake exceeds that of the Lushan Ms4.5 aftershock, displaying non-linear growth with increasing magnitude in the low-frequency range. The predominant frequencies of the Lushan and Changning earthquakes vary within the same monitoring profile. Figure 7 Fourier spectrum of Lushan Ms6.1 mainshock Figure 8 Fourier spectrum of Lushan Ms4.5 aftershock The horizontal-vertical spectral ratio (HVSR) method is a non-referenced field technique that assesses local ground motion amplification by comparing Fourier spectra from two directional components (EW/UD, NS/UD). This approach effectively mitigates site-specific amplification effects, terrain influences, and other variables. The HVSR calculation Formula (2) is presented as follows: $$Rij(f)=\frac{{\sqrt {H1ij\mathop {\left( f \right)}\nolimits^{2} +H2ij\mathop {\left( f \right)}\nolimits^{2} } }}{{Vij\mathop {\left( f \right)}\nolimits^{2} }}=\frac{{Hij\left( f \right)}}{{Vij\left( f \right)}}$$ 2 Where \(H1ij(f)\) , \(H2ij(f)\) , and \(Vij(f)\) represent the Fourier spectrum values of horizontal EW component, NS component and vertical UD component, respectively. The monitoring station gathers data on seismic source characteristics, propagation paths, epicentral distances, and medium properties through analytical methods. In calculating the HVSR curve, MATLAB is employed for fast Fourier spectrum analysis to obtain directional components for each monitoring point. Subsequently, a simple full-period average method is applied to smooth the obtained spectra. As depicted in Fig. 9 , Particularly noteworthy is the consistency in peak HVSR values between the Lushan Ms4.5 and Ms6.1 earthquakes, both falling within similar frequency ranges. This trend is also observed in the HVSR results of the Changning earthquake. Additionally, the HVSR amplitude of the Lushan Ms6.1 earthquake exceeds that of the Lushan Ms4.5 earthquake. Comparing the HVSR between the Lushan and Changning earthquakes can reveal significant characteristic differences. The HVSR of the Lushan earthquake exhibits multiple peaks, indicating significant energy concentration due to multiple reflections and refractions as seismic waves propagate through the monitoring profile. In contrast, the HVSR of the Changning earthquake shows a single prominent peak, primarily concentrated within the frequency range of 1–5 Hz. This difference results in the Changning earthquake displaying higher peak amplitudes compared to the Lushan earthquake. In the context of slope dynamics, seismic waves with lower amplitudes and frequencies, such as those between 1 and 5 Hz, provide more precise analysis results than waves with higher amplitudes and frequencies. Therefore, selecting seismic waves within this optimal frequency range is crucial for accurately studying the seismic responses of slopes and the development of disasters in this region. Figure 9 HVSR response curve of monitoring point Acceleration response spectrum analysis The reaction spectrum is defined as the correlation between the maximum absolute reaction value and the period of a series of single-degree-of-freedom systems with identical damping ratios. The acceleration response spectrum with different damping ratio is analyzed, the maximum response state of acceleration under seismic load can be displayed (Fig. 10 ). The amplitude of acceleration response spectrum gradually diminishes to zero as the period increases. The shape of the acceleration response spectrum remains consistent for each monitoring point in both horizontal and vertical directions across varying damping conditions, with peak values reached simultaneously. As damping increases, the damping ratio decreases in each direction of every monitoring point. At a 5% damping ratio, acceleration amplitudes peak, while they minimize at 20%. Field medium damping characteristics impact ground motion amplitude but do not alter seismic process characteristics. Monitoring points 1# and 2# show similar acceleration response spectrum patterns, with no significant directional differences in horizontal acceleration under identical damping ratios. Conversely, the horizontal acceleration response spectrum at 3# shows greater amplitude in the E-W direction under the same damping ratio. At 1# and 2#, vertical acceleration amplitudes exceed those in the horizontal direction under the same damping ratio, while at 3#, vertical amplitudes are lower compared to horizontal ones. Monitoring point 3# displays the highest response spectrum amplitude under the same damping conditions, while monitoring point 1# exhibits the lowest. The acceleration response spectrum amplitude at each monitoring point aligns with the acceleration time history curve. These findings indicate that monitoring point 3# experiences the strongest seismic response, whereas monitoring point 1# is the weakest. Figure 10 Lushan Ms6.1 earthquake acceleration response spectrum According to the Fig. 10 , the amplitude reaches its maximum value and the response characteristics are most pronounced under a 5% damping condition, which is commonly used in design presets. The seismic influence coefficient, denoted by α , is determined by seismic intensity, site category, characteristic period, and natural vibration period of the site. Formula (3) for calculating α is as follows: $$\alpha =\mathop {\left( {\frac{{Tg}}{T}} \right)}\nolimits^{\gamma } =\eta \alpha \hbox{max}$$ 3 where \(\alpha\) is seismic influence coefficient, \(\alpha \hbox{max}\) is the maximum value of seismic influence coefficient, is site natural vibration period, \(Tg\) is characteristic period, \(\gamma\) -curve is the attenuation index of the descending section, \(\eta\) is damping adjustment coefficient. According to the Code for Seismic Design of Buildings (GB50011-2011), Shimian County is classified with a seismic fortification intensity of VIII degree. This classification entails a design basic seismic acceleration value of 0.20g, placing it within Group III for design category (Fig. 11 ). Figure 11 Seismic influence coefficient curves As shown in the Fig. 12 , the seismic intensity values vary among monitoring points. Monitoring point 3# registers the highest intensity value, while monitoring point 1# records the lowest. The intensity value at monitoring point 3# exceeds fortification standard VI but remains within the range of fortification standard VII. None of the intensity values at any point exceed fortification standard VII. Subsequent seismic design efforts should prioritize monitoring at point 3#, situated above the overburden. Figure 12 Seismic intensity spectra of the Lushan earthquake 4 Discussion 4.1 Amplification effect of topography According to previous studies (Luo et al. 2014 ; Luo et al. 2020 ; Wang and Jin 2022 ; Zhao et al. 2018 ), the amplification coefficient of PGA exhibits a nonlinear increase with altitude, yet the data deviate from expected norms. As shown in Table 3 , the PGA value measured at 2# monitoring point, located at lower elevation, exceeds that at 1#monitoring point, situated at higher elevation. This discrepancy is attributed to the 1# monitoring site located in a flat tunnel, whereas the 2# site is situated on a narrow ridge. The amplitude and Fourier spectrum of ground motion indicate amplification of seismic waves due to the presence of this narrow, steep ridge. (Hough et al. 2010 ). The topographic amplification effect exceeds that of elevation alone. Table 3 Amplification factor of slope seismic response Site Amplification coefficient PGA Amplification coefficient Arias intensity EW NS UD EW NS UD Lushan Ms6.1 1 # (1160m) 1.00 1.00 1.00 1.00 1.00 1.00 2 # (1060m) 1.46 1.91 0.89 2.43 2.55 2.43 3 # (900m) 3.27 2.40 1.71 6.50 5.23 3.30 Lushan Ms4.5 1 # (1160m) 1.00 1.00 1.00 1.00 1.00 1.00 2 # (1060m) 1.09 2.19 1.73 2.25 2.89 2.25 3 # (900m) 2.13 3.09 2.24 6.63 5.33 3.42 Changning Ms6.0 1 # (1160m) 1.00 1.00 1.00 1.00 1.00 1.00 2 # (1060m) 1.89 1.57 1.31 1.50 1.58 0.89 3 # (900m) 6.63 3.15 1.57 39.00 11.33 2.78 Changning Ms5.4 1 # (1160m) 1.00 1.00 1.00 1.00 1.00 1.00 2 # (1060m) 1.50 1.01 1.56 1.67 0.625 2.00 3 # (900m) 2.83 2.93 2.06 15.7 2.25 1.33 4.2 Amplification effect of site conditions According to the Table 3 , the earthquake monitoring data has provided compelling evidence of site-specific amplification effects: PGA and Arias intensity at monitoring point 3# above the overburden layer exhibited the highest values among all seismic measurements within the same monitoring group. The dominant frequency of the overburden typically ranges from 1–5 Hz, which is lower compared to that observed in the underlying bedrock. This lower frequency range facilitates resonance with low-frequency seismic waves, resulting in amplified PGA amplitudes. Furthermore, the overburden layer experiences multiple reflections and refractions of seismic wave energy, leading to energy accumulation and qualitative changes. These dynamics contribute to the development of cracks at the trailing edge of the slope, thereby reducing its structural constraint capacity while enhancing its vibrational resilience (Wang et al. 2008 ). As illustrated in Table 3 , the PGA amplification at the 2# monitoring point during the Lushan Ms6.1 earthquake ranged from 1.0 to 2.0 in all three directions (E-W, N-S, U-D). Similarly, the PGA amplification coefficients at the 2# monitoring site for the Lushan Ms4.5 earthquake, Changning Ms6.0 earthquake, and Changning Ms5.4 earthquake also fell within the range of 1.0 to 2.0. At station 3#, the PGA amplification ranged from 1.5 to 3.0 across all four earthquakes. These findings underscore that the PGA amplification at each monitoring point is predominantly influenced by site-specific conditions, showing minimal correlation with earthquake magnitude. To comprehensively study the seismic response of slopes in this region, it is essential to account for both the overburden effect of the site and the influence of slope structure. It is noteworthy that site-specific conditions exert a stronger amplification effect compared to terrain elevation. Specifically, sites situated along ridges are expected to experience less significant sediment amplification than those at lower elevations. This distinction highlights the critical role of site characteristics and topographical features in seismic response analysis. (Hough et al. 2010 ). 4.3 Directional effect The variation in PGA serves as a critical indicator of disaster development. During the Lushan earthquake, the Donghe River area in Baoxing County experienced a concentration of disasters. Specifically, there were a total of 54 geological disasters in Donghe County, with 41 occurring on the left bank and 13 on the right bank. Significantly, the scale of disasters on the left bank of the Donghe River was notably larger than on the right bank, indicating directional characteristics in the distribution of these events. As depicted in the Fig. 2 , the seismic waves propagate along a direction of 300°, parallel to the orientation of the left bank slope of the Donghe River, which extends between 280° and 300°. As illustrated in Fig. 13 , when the slope orientation coincides with the direction of seismic wave propagation, it assumes the role of a back-wave slope. Conversely, if the orientation of the slope opposes the direction of seismic wave propagation, it becomes a face-wave slope. For instance, along the left bank of the Donghe River, where the slope direction matches the propagation direction of seismic waves (300°), it qualifies as a back-wave slope. When seismic waves propagate to the back wave surface, the energy of the seismic waves on the posterior thin plane is greater than that on the face wave surface. Slope A develops numerous large-scale rock collapses, even though Slope B is an anti-dip slope. In contrast, Slope B develops relatively small-scale landslides. The cover layer of Slope B undergoes shallow sliding along the interface between the bedrock and the cover layer, with some landslide material accumulating on the slope surface. Consequently, Slope B shows a relatively limited number of small-scale shallow surface landslides. This alignment can significantly influence the nature and extent of landslide occurrences in such areas. The interaction between the back-wave slope and local terrain amplifies disaster development more significantly on the left bank compared to the right bank. This phenomenon is exemplified by landslides triggered by seismic events such as the Mw7.6 Chichi earthquake in Taiwan in 1999, the Mw7.6 Kashmir earthquake in Pakistan in 2005, and the Ms8.0 Wenchuan earthquake in 2008, all of which exhibited a pronounced back-wave effect. (Xu and Li 2010 ). Figure 13 Direction effect diagram 4.4 Earthquake-triggered landslide failure mechanism The development of geological disasters is shaped by a complex interplay of human engineering activities and natural factors, such as topography, lithology, geological structure, and seismic events. The topography of Lushan County is characterized by gentle slopes and relatively flat terrain, providing better rock stability compared to Baoxing County. As a result, Lushan County experiences fewer and smaller-scale geological disasters. In contrast, the terrain of Baoxing features high elevations and steep slopes, particularly within the 25° to 40° range, where most disasters occurred. The landscape of Lushan includes developed tributaries and valleys eroded over time, resulting in significant elevation differences and cutting depths of around 1000 m. Its undulating topography and steep slopes, often V-shaped valleys, create conditions favorable for geological disasters such as slope breaks, mountain bulges, narrow ridges, isolated peaks, and areas prone to instability. These geological features are exacerbated during seismic events, leading to abrupt changes in PGA parameters and increased risk of slope instability. The occurrence of disasters highlights the critical influence of geological and hydrological factors on the spatial distribution of such events (Huang and Li 2009a ). Fault lines, as zones of tectonic activity, often serve as epicenters for earthquakes and related ground failures. Similarly, proximity to water systems can exacerbate the severity of disasters through mechanisms such as soil liquefaction during seismic events or increased erosion and landslide susceptibility in saturated terrains. Understanding this localized pattern is essential for effective risk assessment and the implementation of targeted mitigation strategies. In the study area, Mesozoic sandstone dominates the Lushan side, covered by a layer of residual and weathered soil varying in thickness from 10 to 20 meters. Seismic waves interacting with this weathered overburden undergo reflection and refraction, resulting in energy accumulation. This interaction leads to a sudden increase in PGA within the overburden compared to values observed at bedrock stations. Therefore, the overburden not only acts as a source of geological disasters but also amplifies their impact. 4.5 Seismogenic structure and Epicenter As shown in Fig. 14 , it is a schematic diagram of the nappe structure in Lushan and Baoxing, and the Longmenshan fault zone passes through the Baoxing and Lushan. the 2013 Lushan earthquake originated from the Longmenshan front-range fault (Guanxian-Jiangyou fault), whereas the 2022 Lushan earthquake was associated with the Longmenshan central fault (Yingxiu-Beichuan fault). Both faults are nappe tectonics, where older rock strata uplifted to the surface result in fragmented displacement. Despite distance of Baoxing from the epicenter, disaster development was pronounced, influenced by the 'epicenter offset' caused by the nappe structure. During an earthquake, the seismic source is not a singular point but a fault plane, which is often inclined rather than strictly vertical. If the inclination angle is relatively small, even though the initial rupture occurs at the location with the maximum slip, the projection of the focal point on the ground (instrument epicenter) will significantly deviate from the fault line, which is the intersection between the fault plane and the ground surface. Consequently, the epicenter cannot coincide with the surface line, leading to a noticeable disparity between the macro epicenter and the micro epicenter. Macroscopic epicenters, which closely correlate with densely affected disaster areas, differ in distance from microseismic epicenters. Speculating on the macroscopic epicenter can enhance earthquake disaster prediction by pinpointing actual epicenter locations. Including additional reference factors in earthquake disaster assessments can expedite relief efforts and improve disaster prevention efficacy. Epicenter determination should consider seismic intensity alongside monitoring data, especially in nappe structure areas. Engineering facility parameters should also be based on macro epicenters to account for regional tectonic factors in future construction planning. Figure 14 Nappe tectonics 5 Conclusion The primary objective of this study is to investigate the seismic response characteristics and disaster development laws of slopes in deep canyon landforms. Based on the field surveys of disaster distribution and analysis of seismic monitoring data, the following findings have been obtained: (1) The Lushan Ms6.1 earthquake resulted in a concentration of geological disasters predominantly in Baoxing County. These disasters are primarily clustered along seismic faults and show linear alignment with water systems. Significantly, the left bank of the river shows a higher incidence and larger scale of geological disasters compared to the right bank. (2) Based on seismic data, the PGA amplification at the 2# monitoring point within the monitoring profile ranges from 1.0 to 2.0, while at the 3# monitoring point it ranges from 1.5 to 3.0. PGA values increase with the earthquake magnitude. In addition to topographic features, geological structure, and lithology significantly influence site amplification. Thus, the PGA amplification effect is not solely dependent on seismic magnitude but also on site-specific conditions. Comparing the Lushan and Changning earthquakes, the amplification effect in the monitoring profile area prioritizes site conditions over topography and elevation effects, with the order being: site condition amplification effect > topography amplification effect > elevation amplification effect. Analysis of HVSR curves from multiple seismic events reveals significant seismic response in this region, particularly within the frequency range of 1Hz to 5Hz. Understanding the impact of low-frequency seismic waves on site behavior is crucial for assessing seismic response and designing slopes resilient to seismic activity. (3) The development of disasters in the Baoxing area can be attributed to a combination of factors, including steep terrain, loose soil layers, and slope orientation. These conditions collectively amplify the PGA on slopes, thereby increasing the susceptibility to disaster occurrences. (4) The post-earthquake disaster pattern following the Lushan earthquake shows a distinct deviation from the epicenter. Specifically, the disaster concentration is observed primarily in areas characterized by lower intensity levels, specifically Level VII. This phenomenon can be attributed to the distance difference between the macroscopic epicenter and microscopic epicenter within nappe tectonic regions. Declarations Acknowledgment We sincerely appreciate the National Nature Science Foundation of China (Grant NO. 41877235) and the National Key Research and Development Program of China (Grant No. 2017YFC1501000) support for the preliminary data collection for this paper. Author contributions All authors contributed to the completion of this article. Professor Yunsheng Wang provided guidance on the core direction of the article. Haochen Wu and the Researcher Jianxian He analyzed the data presented in this paper. Professor Yonghong Luo led the investigation team into the disaster area. Gang Jin contributed data collected in 2019, and Huaying Song participated in data processing. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding This research was partially supported by the National Natural Science Foundation of China (Grant No. 42207194) and State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (SKLGP2023Z028). Ethical statement: The submitted work is original and it not published elsewhere in any form or language. It is not submitted to any other journal for simultaneous consideration. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. References Allen CR, Luo Z, Qian H, Wen X, Zhou H, Huang W (1991) Field study of a highly active fault zone: the Xianshuihe Fault of southwestern China. Geological Society of America Bulletin 103 (9):1178-1199. doi:10.1130/0016-7606(1991)1032.3.CO;2 Assimaki D, Gazetas G, Kausel E (2005) Effects of local soil conditions on the topographic aggravation of seismic motion: Parametric investigation and recorded field evidence from the 1999 Athens earthquake. 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Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYPACGx5+ZubDD0jRkiYn2c6WZkCKlkPGBud5FCSIUmtw/Ozh1zx/DiRuPszDYMBQYxNNWMuZvDRr3rY7idsO8x54wHAsLbeBkBazAzlmxrwNz4Ba+BIMGBsOE6Hl/BszY54/hxM3N/MYSBCn5UaO8WMetsPGBszEarG/8caMcW5bmpzEYWAgJxDjF8n+HOMPb/4Ao7L/8OEHH2psCGsBAjYpHhgzgQjlIMD88QeRKkfBKBgFo2CEAgCZtEI0fqni/AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1774-9494","institution":"Chengdu University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Yunsheng","middleName":"","lastName":"Wang","suffix":""},{"id":340951487,"identity":"d901836b-20d5-444f-9b07-2f4b6baf510b","order_by":2,"name":"Jianxian He","email":"","orcid":"","institution":"Chengdu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jianxian","middleName":"","lastName":"He","suffix":""},{"id":340951488,"identity":"44e8b3cb-090b-4697-811b-81909533256a","order_by":3,"name":"Yonghong Luo","email":"","orcid":"","institution":"Chengdu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yonghong","middleName":"","lastName":"Luo","suffix":""},{"id":340951489,"identity":"7776f93a-2e18-4ba7-b637-5d1a8084fb7b","order_by":4,"name":"Gang Jin","email":"","orcid":"","institution":"China Railway Engineering Design and Consulting Group CO, LTD, Zhengzhou Institute","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Jin","suffix":""},{"id":340951490,"identity":"5fc5b27a-1024-4e3a-a507-518edacab5ed","order_by":5,"name":"Huaying Song","email":"","orcid":"","institution":"Chengdu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Huaying","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2024-07-10 11:09:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4717589/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4717589/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66536100,"identity":"c69a0335-a120-4bb0-84f6-88150e752cdc","added_by":"auto","created_at":"2024-10-14 07:03:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1310462,"visible":true,"origin":"","legend":"\u003cp\u003eRegional geological map of 2022 Lushan earthquake\u003c/p\u003e\n\u003cp\u003e(F1: Minjiang fault, F2: Longmenshan fault zone, F3: Xianshuihe fault, F4: Anninghe fault, F5: Zemuhe fault, F6: Xiaojiang fault, F7: Huayingshan fault, F8: Jinshajiang fault, F9: Ganzi-Litang fault, F10: Litang-Dewu fault, F11: Yulongxi fault)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/62b6b4da2efeab5029c71e5f.png"},{"id":66537271,"identity":"54854dc8-daca-4449-874a-e05d034f747d","added_by":"auto","created_at":"2024-10-14 07:11:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":707303,"visible":true,"origin":"","legend":"\u003cp\u003eMainshock and aftershock distribution map; disasters distribution map\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/c92bde8b0f7ab38b7499fd43.png"},{"id":66537704,"identity":"079dfa38-0997-4532-81be-73a011452124","added_by":"auto","created_at":"2024-10-14 07:19:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":502359,"visible":true,"origin":"","legend":"\u003cp\u003eEarthquake-induced landslides\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/0b1a74a023ff661fe23d840a.png"},{"id":66536102,"identity":"018e9e4e-0f7b-49cd-a470-a41f127a5bb7","added_by":"auto","created_at":"2024-10-14 07:03:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":325626,"visible":true,"origin":"","legend":"\u003cp\u003eGeological disasters triggered by the Lushan Ms6.1 earthquake\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/c865f1e16f72bf88dfb05900.png"},{"id":66537273,"identity":"c2fcbe39-a733-458a-977c-0a81709e92d4","added_by":"auto","created_at":"2024-10-14 07:11:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":324803,"visible":true,"origin":"","legend":"\u003cp\u003eMonitoring profile of seismic station\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/82c21d91dc741b844e1afb09.png"},{"id":66536106,"identity":"6ec050a3-2897-4a1b-82c0-d8f9a1f500cd","added_by":"auto","created_at":"2024-10-14 07:03:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":149869,"visible":true,"origin":"","legend":"\u003cp\u003eLushan Ms6.1 mainshock time history curve\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/675d8502bacf0b88125a97d6.png"},{"id":66536114,"identity":"7f477a2a-0e63-4d1c-b375-dfeac2607589","added_by":"auto","created_at":"2024-10-14 07:03:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":136807,"visible":true,"origin":"","legend":"\u003cp\u003eFourier spectrum of Lushan Ms6.1 mainshock\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/61be895030a2c4e70e54d5d0.png"},{"id":66536101,"identity":"9f81941a-8109-42be-8071-6aecbb0049d9","added_by":"auto","created_at":"2024-10-14 07:03:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":131199,"visible":true,"origin":"","legend":"\u003cp\u003eFourier spectrum of Lushan Ms4.5 aftershock\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/2a0c8af6dce8fb0638a1bc51.png"},{"id":66537705,"identity":"e062fe6e-f6f5-45d2-acd1-694998e46096","added_by":"auto","created_at":"2024-10-14 07:19:46","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":220967,"visible":true,"origin":"","legend":"\u003cp\u003eHVSR response curve of monitoring point\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/c3eb325667cbf49391f4d30f.png"},{"id":66537270,"identity":"65346e24-2309-4263-b393-0b8208d0056c","added_by":"auto","created_at":"2024-10-14 07:11:46","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":120491,"visible":true,"origin":"","legend":"\u003cp\u003eLushan Ms6.1 earthquake acceleration response spectrum\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/43e62b7fa9d9b6d820f45362.png"},{"id":66536109,"identity":"1e9d1a2c-4c19-4de6-9260-bd3e9211aef3","added_by":"auto","created_at":"2024-10-14 07:03:46","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":86563,"visible":true,"origin":"","legend":"\u003cp\u003eSeismic influence coefficient curves\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/b8988827faf98848cc4b3b5f.png"},{"id":66536111,"identity":"4e0b9df6-bbd6-4881-861c-cc23ad467de8","added_by":"auto","created_at":"2024-10-14 07:03:46","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":194627,"visible":true,"origin":"","legend":"\u003cp\u003eSeismic intensity spectra of the Lushan earthquake\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/a90c685298fa68548774d34f.png"},{"id":66537268,"identity":"137b7796-324d-4a8b-8c26-6686a3a34b0c","added_by":"auto","created_at":"2024-10-14 07:11:46","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":184977,"visible":true,"origin":"","legend":"\u003cp\u003eDirection effect diagram\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/239e6cb14b90e686adfb578f.png"},{"id":66536108,"identity":"3df12d14-5cf2-4826-a845-1e97e89ad8e9","added_by":"auto","created_at":"2024-10-14 07:03:46","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":120146,"visible":true,"origin":"","legend":"\u003cp\u003eNappe tectonics\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/48396e716d4f616d84ac7092.png"},{"id":71503486,"identity":"2a6a824c-e6ed-4933-b92d-f12e45580446","added_by":"auto","created_at":"2024-12-16 09:28:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5410606,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4717589/v1/181e85f7-1ec4-41c6-ae46-2fdb622c406d.pdf"}],"financialInterests":"","formattedTitle":"Distribution and failure mechanism of landslides associated with seismic response of the slope during the 2022 Lushan Ms6.1 earthquake","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eOver the past few decades, earthquake-induced geological disasters have inflicted substantial human casualties and property damage. For instance, the Wenchuan earthquake resulted in the loss of approximately 87,000 lives and an economic impact totaling 845.1\u0026nbsp;billion RMB. Geological disasters directly contributed to more than 30% of the casualties during this event (Wang and Jin \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Dai et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yin et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Li \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The Wangjiayan landslide tragically resulted in the loss of more than 1,600 lives, with over 500 individuals buried in Beichuan County (Wang and Jin \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The landslides triggered by the Ms7.0 earthquake in Lushan, Sichuan province, in 2013 led to the tragic loss of 196 lives and injuries to 11,470 individuals (Meng et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e). Ninety-three people lost their lives due to massive landslides triggered by the Luding Ms6.8 earthquake in Sichuan province in 2022 (Tie et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The loess landslides and rockfalls induced by the Ms6.2 earthquake led to more than 900 injuries and 151 fatalities in Jishishan, Gansu province (Li et al. ; Liu et al. ; Pang et al. ; Xv et al.). To reveal the failure mechanisms and predict earthquake-triggered landslides, numerous studies have focused on understanding the development of geological disasters influenced by the dynamic response of slopes.\u003c/p\u003e \u003cp\u003eSeismic geological disasters are primarily triggered in strip-shaped or narrow ridges, as well as on convex mountaintops (Kargel et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Korup et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wang and Jin \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Huang and Li \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Numerous theoretical and observational studies have consistently demonstrated that topographic features, such as ridges and cliff faces, can induce significant increases in ground acceleration (Hartzell et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Spudich et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Assimaki et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Hough et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) discovered that many sturdy, relatively well-built buildings along the ridge in the study area suffered considerable damage during the examination of the 2010 Ms7.0 earthquake in Haiti (Hough et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). By employing monitoring instruments in the valley, Hough determined that the PGA amplification effect caused by the overburden was approximately 1.8 times greater than that observed at the hard rock reference point (Hough et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Luo's study identified higher magnification factors near the hilltop and smaller factors near the foot of the slope. Amplification effects are most pronounced at slope breaks, peaks, or steep inclines, where geological disasters triggered by earthquakes often occurred. As altitude increases, the PGA amplification factors derived from HVSR curves range from 2.4 to 6.0. Notably, the PGA amplification factor at the lower elevation monitoring site (Q5, 5.2) exceeds that at the higher elevation monitoring site (Q6, 3.8). Meanwhile, the PGA amplification coefficient at the Q5 monitoring point (5.2) closely aligns with that of the Q11 monitoring point (5.3) (Luo et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The aftershock data collected from the low hilly area indicate that the PGA amplification factors in three directions (E-W, N-S, U-D) at lower elevations are 2.32, 4.28, and 2.22 respectively, while at higher elevations they are 1.42, 2.24, and 0.95 respectively. The difference between the two monitoring sites lies in their geological conditions: the low elevation site has a loose cover layer, whereas the high elevation site is bedrock (Wang and Jin \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The geological disasters triggered by the Ms7.0 earthquake in Jiuzhaigou in 2017 were predominantly concentrated on hilltops and prominent ridges, potentially linked to terrain amplification. Monitoring data indicates that the peak ground acceleration (PGA) on prominent ridges is 4\u0026ndash;11 times greater than that at the foot of the mountain (Zhao et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The geological disasters triggered by earthquakes are attributed to the amplification of PGA. This amplification effect is influenced by topographical features, lithology, and site conditions (He et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Panzera et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, exploring the seismic response of the slope during the earthquake is crucial for understanding disaster triggering mechanisms.\u003c/p\u003e \u003cp\u003eThis paper focuses on utilizing the Lushan Ms6.1 earthquake as a case study to investigate the development and distribution of geological hazards triggered by earthquakes in deep canyon landforms. By integrating multiple recorded seismic data, we explore the seismic response of slopes in deep canyon areas. Furthermore, we discuss the impact of slope seismic response on the evolution of seismic-triggered geological hazards. Exploring the seismic response characteristics of slopes can offer valuable insights into seismic response parameters for the construction of numerous engineering facilities and the enhancement of human production and life in the mountainous gorge geomorphic areas of western Sichuan, China.\u003c/p\u003e"},{"header":"2 Study area","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Geological setting\u003c/h2\u003e \u003cp\u003eOn June 1, 2022, an Ms6.1 earthquake struck Lushan County, Sichuan Province, occurring in the Longmenshan fault zone. The earthquake is classified as a thrust event. The nodal parameters of the focal mechanism are as follows: Nodal I - strike/dip/slip angles of 221.5\u0026deg;/44.4\u0026deg;/103.3\u0026deg;; Nodal II - strike/dip/slip angles of 23.2\u0026deg;/47.1\u0026deg;/77.3\u0026deg;, with a centroid depth of 15.5 km. The seismogenic fault dips southeastward and is situated at the southern end of the Longmenshan fault zone (Chen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince around 50\u0026nbsp;million years ago, the collision between the Indian Plate and the Eurasian Plate has uplifted the Tibetan Plateau. The plateau continues to move eastward. The Longmenshan fault zone formed along the boundary between the Tibetan Plateau and the Sichuan Basin due to the obstruction caused by the rigid nature of the basin (Zhao and Su \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Densmore et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Gan et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Jia et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Tapponnier et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yin and Harrison \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The Longmenshan thrust zone consists of three primary fault systems: the Maoxian-Wenchuan fault (also known as the back-range fault), the Yingxiu-Beichuan fault (also known as the central fault), and the Guanxian-Jiangyou fault (also known as the front-range fault) (Xu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Due to its geological structure, the junction area between the Longmenshan fault zone and the foreland basin is characterized by northeast-oriented structures. This region includes the Longmenshan thrust nappe tectonic belt, the western Sichuan foreland basin, the nappe-detachment superimposed subbelt, and the foreland fold belt. Following the collision, the Indian plate continued its northward movement, resulting in the accumulation of substantial strain energy. The displacement rate of the Bayan Har block decreased from 10 mm/yr to 6 mm/yr between 1984 and 2014. This period saw significant strain energy accumulation near the Longmenshan fault zone. As a result of the continuous release of this accumulated strain energy, the Longmenshan fault has become one of the most seismically active regions in China (Parsons et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Shen et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The Wenchuan Ms8.0 earthquake in 2008, the Lushan Ms7.0 earthquake in 2013, and the Jiuzhaigou Ms7.0 earthquake in 2017 occurred along the Longmenshan fault in recent decades.\u003c/p\u003e \u003cp\u003eThe epicenter area is situated in Lushan County and Baoxing County, Ya'an City, Sichuan Province, China (102.41\u0026deg;E-103.18\u0026deg;E, 30.01\u0026deg;N-30.93\u0026deg;N). The epicenter of the Lushan Ms6.1 earthquake was located within this region (102.94\u0026deg;E, 30.37\u0026deg;N). The central seismic zone is situated at the convergence of the Northwest Sichuan Inverted Triangle Block, Bayan Har Block, Sichuan-Yunnan Rhombohedral Block, and Sichuan Block. The stress within the target area continuously accumulates due to the compression between multiple tectonic plates. The study area, predominantly characterized by undulated mountainous topography, spans elevations from a low of 557 m to a high of 5290 m above sea level. The northwestern region features relatively high terrain, whereas the southeastern part is characterized by low-lying topography (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The terrain is characterized by deep canyons. The strata in the area are completely exposed, ranging from the Upper Proterozoic Sinian to loose Quaternary strata, with the absence of Cambrian and Carboniferous strata. The lithology in the study area consists primarily of sandstone, mudstone, shale, slate, and phyllite.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eRegional geological map of 2022 Lushan earthquake\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(F1: Minjiang fault, F2: Longmenshan fault zone, F3: Xianshuihe fault, F4: Anninghe fault, F5: Zemuhe fault, F6: Xiaojiang fault, F7: Huayingshan fault, F8: Jinshajiang fault, F9: Ganzi-Litang fault, F10: Litang-Dewu fault, F11: Yulongxi fault)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Distribution of the earthquake-induced landslides\u003c/h2\u003e \u003cp\u003eAfter the earthquake, the investigation group immediately entered Baoxing County, where the disasters were concentrated. Based on the aftershock of the Lushan Ms6.1 earthquake recorder by the National Earthquake Data Center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.earthquake.cn/\u003c/span\u003e\u003cspan address=\"https://data.earthquake.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the distribution of aftershocks has an elliptical shape with a short axis. The aftershock primarily concentrated within the zone bounded by the Guanxian-Jiangyou fault (Longmenshan front-range fault) and the Yingxiu-Beichuan fault (Longmenshan central fault).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eMainshock and aftershock distribution map; disasters distribution map\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBased on the seismic intensity map (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the contour map shows an elliptical shape oriented in the northeast direction. The seismic intensity reached a maximum of VIII degree near the epicenter. More than 238 geological disasters were triggered by the earthquake, comprising 171 small-scale, 52 medium-scale, 12 large-scale, and 3 giant geological disasters. Near the epicenter in Lushan County, there were no significant geological disasters observed. Only 5 disasters were triggered in Lushan County, with the remaining geological disasters occurring in Baoxing County. Lushan County is located in the VIII degree seismic intensity zone, whereas Baoxing County is in the VI degree zone. Surprisingly, the distribution of disasters is predominantly concentrated in the low-intensity region, which contradicts conventional cognitive patterns (Huang and Li \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009b\u003c/span\u003e). Geological disasters primarily concentrate on near faults, showing a linear pattern along seismogenic faults. The linear density of disasters in Baoxing is 3.09 per kilometer. The density of disaster development and distribution decreases as the distance from the fault increases. Geological disasters demonstrate a spatial distribution closely aligned with river networks, particularly evident in their proximity to the Baoxing river system and seismogenic structures. Following an earthquake event, all triggered geological disasters were found in close proximity to river systems, constituting 100% of the total occurrences. Specifically, within the Baoxing river area, 58 geological disasters occurred, with 77.58% (45 disasters) observed on the left bank. This asymmetric distribution highlights a greater incidence and scale of disasters on the left bank compared to the right. The occurrence of disasters is predominantly localized within a radius of 1 km from fault lines and water systems (Huang \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Earthquake-induced landslides types\u003c/h2\u003e \u003cp\u003eThe Lushan Ms6.1 earthquake induced landslides characterized by diverse failure mechanisms. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, the failure mechanism of the landslide on the right bank was identified as shattering-sliding type. Initially, this involves the presence of bedding or weak structural planes within the inner slope. Subsequently, seismic activity loosens or penetrates these structural planes. Finally, the slope rapidly slides along these weakened structures or bedding planes due to the sustained impact of a powerful earthquake force. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, the landslide occurred on the right bank of the river, where the rock strata outside the inclined slope created favorable conditions for earthquake-induced landslides. Despite these conducive conditions, the scale of the landslide was relatively small, affecting only a portion of the hazardous rock mass near the base of the slope. The sliding occurred along a planar surface, resulting in debris accumulation at the foot of the slope (Li et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB (Huang \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), the rear edge of the slope exhibited multiple sets of cracks oriented in various directions, forming distinct groups of wedges of different sizes. Under the influence of gravity, these cracks continued to propagate. During an earthquake, the strained rear edge underwent catastrophic failure, leading to the detachment and descent of large-scale wedges. These massive stone blocks were forcefully ejected and impacted the slope surface, fragmenting into smaller gravel pieces that accumulated within the gullies.\u003c/p\u003e \u003cp\u003eThe type of landslide observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC is a shallow surface landslide. The bedrock surface beneath the slope is overlain by residual slope deposits. When subjected to seismic loads, seismic waves within the overlying layer undergo multiple reflections and refractions, resulting in the accumulation of energy. The accumulation of energy in this process triggers the sliding of the overlying layer along the basement interface, initiating from the break in the slope. The material resulting from these shallow landslides is subsequently deposited on the surface of the slope.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eEarthquake-induced landslides\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe wedge illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA are formed by multiple sets of internal cracks within the slope, which develop on the right bank of the river. These cracks undergo slip under seismic loads and rainfall conditions. Over time, these internal cracks gradually evolve into sliding surfaces, leading to slope instability. The soil landslide triggered by the M6.1 Lushan earthquake is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB. This high-slope landslide originated on the left bank of the river. The landslide material moved at high speed, scraping the flow area, quickly crossing the riverbed, accumulating on the valley floor, and forming a barrier lake. Generally, the inner side of the highway is the source excavation slope, while the outer side is the source accumulation slope. The material is deposited on the bedrock outside the highway. During the earthquake, the low natural vibration frequency of the material leads to the development of ground cracks. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003eGeological disasters triggered by the Lushan Ms6.1 earthquake\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Seismic response of the slope","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data acquisition and monitoring profile\u003c/h2\u003e \u003cp\u003eThe monitoring profiles along the Xianhuihe, Tuosuohu, and Huayingshan faults have been established by the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (SKLGP) since 2008. The stations are equipped with the G01NET-3 seismic dynamic data acquisition instrument, developed by the Institute of Engineering Mechanics, China Earthquake Administration, featuring a fundamental sensitivity parameter of 1.1V/G.\u003c/p\u003e \u003cp\u003eThe earthquake monitoring profile is situated in Shimian County, Ya'an City, Sichuan Province, China, approximately 137 km from the epicenter. This region exhibits high seismic activity, establishing it as one of most seismically active areas China (He et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Allen et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). The monitoring profile extends northeastward along the Nanya River, featuring a vertical elevation difference of approximately 750 m. The profile features an average slope angle of approximately 40\u0026deg;. The lithology comprises early Sinian granite, with residual slope deposits covering the slopes on both sides. Monitoring site 1# is located on the relatively flat bedrock of an adit, situated at an elevation of 1160 m within the Jigongshan Mountains, approximately 410 m above the riverbed. The lithology predominantly comprises granite. Monitoring site 2# is positioned on the ridge in the upper half of Jigongshan Mountain, at an elevation of 1060 m, approximately 310 m higher than the riverbed. Site 3#, situated at an elevation of 900 m above sea level, is about 150 m distant from the riverbed. The overburden thickness ranges from 10 to 15 m and consists of strongly weathered granite layers. A fault known as the Shimian fault has been identified in close proximity to site 3#. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSeveral earthquakes had been recorded at the monitoring profile, notable including Lushan Ms6.1 earthquake in 2022, Lushan Ms4.5 earthquake in 2022, Changning Ms6.0 earthquake in 2019 and Changning Ms5.4 earthquake in 2019 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003eMonitoring profile of seismic station\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSeismic data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLat (\u0026deg;N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLon (\u0026deg;E)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDepth (km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01/06/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17:00:08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e102.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01/06/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17:03:09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e102.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17/06/2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22:55:43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e104.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22/06/2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22:29:56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e104.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data analysis and results\u003c/h2\u003e \u003cp\u003eThe time history of acceleration was baseline corrected and filtered using a Butterworth bandpass filter set to a frequency range of 0.5\u0026ndash;30 Hz in MATLAB. Subsequently, parameters such as Fourier spectrum and Arias intensity, along with other relevant metrics, were analyzed to investigate the seismic response characteristics of the slope.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePGA and Arias intensity analyses\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe acceleration time history curve is derived from processed seismic data (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), capturing the peak ground acceleration (PGA) and Fourier spectrum of the earthquake (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Arias intensity serves as a fundamental metric for quantifying vibrational energy, reflecting the continuous transformation of kinetic and potential energies of objects during vibration, consistent with the principles of energy conservation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe mass of the vibrating object remains constant throughout the seismic process. Velocity is utilized to characterize its kinetic energy. The Arias intensity formula entails integrating the square of the acceleration of the object over time during vibration, as exemplified by Formula (1):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\text{I}}a=\\frac{\\pi }{{2g}}\\int\\limits_{0}^{{td}} {\\mathop {\\left( {at} \\right)}\\nolimits^{2} } dt$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Ia\\)\u003c/span\u003e\u003c/span\u003e is the Arias intensity, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(td\\)\u003c/span\u003e\u003c/span\u003eis the vibration duration, \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e is the gravitational acceleration.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cb\u003eLushan Ms6.1 mainshock time history curve\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe comparison of multiple datasets in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e highlights the performance enhancement of horizontal (E-W, N-S) PGA at monitoring points. Horizontal amplitudes generally exceed the vertical (U-D) amplitude across all points, except for the 1# monitoring site during the Ms6.1 mainshock. Specifically, the amplitudes in each direction at the 1# monitoring point are lower compared to other sites, whereas the 3# monitoring point has the highest amplitudes among all. The peak of the time history curve corresponds to the PGA. Horizontal PGA (E-W, N-S) values also consistently exceed vertical PGA (U-D) values across all sites during the Lushan Ms4.5 earthquake. The PGA recorded at the 1# monitoring site is the smallest, while the PGA at the 3# monitoring site is the largest.\u003c/p\u003e \u003cp\u003eArias intensity is primarily influenced by earthquake acceleration and vibration duration. The Arias intensity values at each monitoring point exhibit a pattern similar to that of PGA. Specifically, Arias intensity values are higher in the horizontal directions (E-W, N-S) compared to the vertical direction (U-D). The Arias intensity at monitoring point 1# is relatively lower than at other sites, while monitoring point 3# records the highest Arias intensity among all stations. At the same monitoring profile, the seismic response parameters of slope during the Lushan Ms6.1 and Ms4.5 earthquakes show a nonlinear increasing trend in magnitude. Moreover, Arias intensity increases with higher earthquake magnitudes. The PGA and Arias intensity of the Changning Ms6.0 and Ms5.4 earthquakes follow a similar pattern to those observed during the Lushan earthquakes. Specifically, the PGA and Arias intensity of the Lushan Ms6.1 earthquake exceed those of the Lushan Ms4.5 earthquake, while the PGA and Arias intensity of the Changning Ms6.0 earthquake also surpass those of the Changning Ms5.4 earthquake (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSeismic response parameters of slope\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEarthquake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ePGA/(gal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eArias Intensity/(dm\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003ePredominant Frequency/Hz\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE-W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eU-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eE-W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eU-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eE-W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eU-D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLushan Ms6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003csup\u003e#\u003c/sup\u003e(1160m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003csup\u003e#\u003c/sup\u003e(1060m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003csup\u003e#\u003c/sup\u003e(900m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLushan Ms4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003csup\u003e#\u003c/sup\u003e(1160m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003csup\u003e#\u003c/sup\u003e(1060m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003csup\u003e#\u003c/sup\u003e(900m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eChangning Ms6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003csup\u003e#\u003c/sup\u003e(1160m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003csup\u003e#\u003c/sup\u003e(1060m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003csup\u003e#\u003c/sup\u003e(900m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eChangning Ms5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003csup\u003e#\u003c/sup\u003e(1160m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003csup\u003e#\u003c/sup\u003e(1060m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003csup\u003e#\u003c/sup\u003e(900m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFourier spectrum analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the frequency contents of the three directions (E-W, N-S, U-D) at the monitoring points during the same earthquake do not show significant differences in obtaining peak values.\u003c/p\u003e \u003cp\u003eAt the monitoring point, significant amplitudes are observed in the frequency range of 5\u0026ndash;10 Hz, particularly during the Ms6.1 earthquake, which shows larger amplitudes. Amplitudes in the high-frequency band are generally lower than those in the low-frequency band. Among the monitored points, the 1# monitoring point records the smallest amplitude peak, while the 3# monitoring point registers the largest, augmented by the overlay site. The predominant frequency range of the Changning earthquake is primarily distributed within the 1\u0026ndash;3 Hz band. The Fourier spectrum amplitude of the Lushan Ms6.1 earthquake exceeds that of the Lushan Ms4.5 aftershock, displaying non-linear growth with increasing magnitude in the low-frequency range. The predominant frequencies of the Lushan and Changning earthquakes vary within the same monitoring profile.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u003cb\u003eFourier spectrum of Lushan Ms6.1 mainshock\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e \u003cb\u003eFourier spectrum of Lushan Ms4.5 aftershock\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe horizontal-vertical spectral ratio (HVSR) method is a non-referenced field technique that assesses local ground motion amplification by comparing Fourier spectra from two directional components (EW/UD, NS/UD). This approach effectively mitigates site-specific amplification effects, terrain influences, and other variables. The HVSR calculation Formula (2) is presented as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$Rij(f)=\\frac{{\\sqrt {H1ij\\mathop {\\left( f \\right)}\\nolimits^{2} +H2ij\\mathop {\\left( f \\right)}\\nolimits^{2} } }}{{Vij\\mathop {\\left( f \\right)}\\nolimits^{2} }}=\\frac{{Hij\\left( f \\right)}}{{Vij\\left( f \\right)}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(H1ij(f)\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(H2ij(f)\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Vij(f)\\)\u003c/span\u003e\u003c/span\u003e represent the Fourier spectrum values of horizontal EW component, NS component and vertical UD component, respectively.\u003c/p\u003e \u003cp\u003eThe monitoring station gathers data on seismic source characteristics, propagation paths, epicentral distances, and medium properties through analytical methods. In calculating the HVSR curve, MATLAB is employed for fast Fourier spectrum analysis to obtain directional components for each monitoring point. Subsequently, a simple full-period average method is applied to smooth the obtained spectra.\u003c/p\u003e \u003cp\u003eAs depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, Particularly noteworthy is the consistency in peak HVSR values between the Lushan Ms4.5 and Ms6.1 earthquakes, both falling within similar frequency ranges. This trend is also observed in the HVSR results of the Changning earthquake. Additionally, the HVSR amplitude of the Lushan Ms6.1 earthquake exceeds that of the Lushan Ms4.5 earthquake.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparing the HVSR between the Lushan and Changning earthquakes can reveal significant characteristic differences. The HVSR of the Lushan earthquake exhibits multiple peaks, indicating significant energy concentration due to multiple reflections and refractions as seismic waves propagate through the monitoring profile. In contrast, the HVSR of the Changning earthquake shows a single prominent peak, primarily concentrated within the frequency range of 1\u0026ndash;5 Hz. This difference results in the Changning earthquake displaying higher peak amplitudes compared to the Lushan earthquake. In the context of slope dynamics, seismic waves with lower amplitudes and frequencies, such as those between 1 and 5 Hz, provide more precise analysis results than waves with higher amplitudes and frequencies. Therefore, selecting seismic waves within this optimal frequency range is crucial for accurately studying the seismic responses of slopes and the development of disasters in this region.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e \u003cb\u003eHVSR response curve of monitoring point\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eAcceleration response spectrum analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe reaction spectrum is defined as the correlation between the maximum absolute reaction value and the period of a series of single-degree-of-freedom systems with identical damping ratios. The acceleration response spectrum with different damping ratio is analyzed, the maximum response state of acceleration under seismic load can be displayed (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The amplitude of acceleration response spectrum gradually diminishes to zero as the period increases. The shape of the acceleration response spectrum remains consistent for each monitoring point in both horizontal and vertical directions across varying damping conditions, with peak values reached simultaneously. As damping increases, the damping ratio decreases in each direction of every monitoring point. At a 5% damping ratio, acceleration amplitudes peak, while they minimize at 20%. Field medium damping characteristics impact ground motion amplitude but do not alter seismic process characteristics. Monitoring points 1# and 2# show similar acceleration response spectrum patterns, with no significant directional differences in horizontal acceleration under identical damping ratios. Conversely, the horizontal acceleration response spectrum at 3# shows greater amplitude in the E-W direction under the same damping ratio. At 1# and 2#, vertical acceleration amplitudes exceed those in the horizontal direction under the same damping ratio, while at 3#, vertical amplitudes are lower compared to horizontal ones. Monitoring point 3# displays the highest response spectrum amplitude under the same damping conditions, while monitoring point 1# exhibits the lowest. The acceleration response spectrum amplitude at each monitoring point aligns with the acceleration time history curve. These findings indicate that monitoring point 3# experiences the strongest seismic response, whereas monitoring point 1# is the weakest.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e \u003cb\u003eLushan Ms6.1 earthquake acceleration response spectrum\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAccording to the Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, the amplitude reaches its maximum value and the response characteristics are most pronounced under a 5% damping condition, which is commonly used in design presets. The seismic influence coefficient, denoted by \u003cem\u003eα\u003c/em\u003e, is determined by seismic intensity, site category, characteristic period, and natural vibration period of the site. Formula (3) for calculating α is as follows:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\alpha =\\mathop {\\left( {\\frac{{Tg}}{T}} \\right)}\\nolimits^{\\gamma } =\\eta \\alpha \\hbox{max}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003e is seismic influence coefficient, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha \\hbox{max}\\)\u003c/span\u003e\u003c/span\u003eis the maximum value of seismic influence coefficient, \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e is site natural vibration period, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Tg\\)\u003c/span\u003e\u003c/span\u003eis characteristic period, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\gamma\\)\u003c/span\u003e\u003c/span\u003e-curve is the attenuation index of the descending section, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\eta\\)\u003c/span\u003e\u003c/span\u003e is damping adjustment coefficient.\u003c/p\u003e \u003cp\u003eAccording to the \u003cem\u003eCode for Seismic Design of Buildings\u003c/em\u003e (GB50011-2011), Shimian County is classified with a seismic fortification intensity of VIII degree. This classification entails a design basic seismic acceleration value of 0.20g, placing it within Group III for design category (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e \u003cb\u003eSeismic influence coefficient curves\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAs shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, the seismic intensity values vary among monitoring points. Monitoring point 3# registers the highest intensity value, while monitoring point 1# records the lowest. The intensity value at monitoring point 3# exceeds fortification standard VI but remains within the range of fortification standard VII. None of the intensity values at any point exceed fortification standard VII. Subsequent seismic design efforts should prioritize monitoring at point 3#, situated above the overburden.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e \u003cb\u003eSeismic intensity spectra of the Lushan earthquake\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Amplification effect of topography\u003c/h2\u003e \u003cp\u003eAccording to previous studies (Luo et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang and Jin \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the amplification coefficient of PGA exhibits a nonlinear increase with altitude, yet the data deviate from expected norms. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the PGA value measured at 2# monitoring point, located at lower elevation, exceeds that at 1#monitoring point, situated at higher elevation. This discrepancy is attributed to the 1# monitoring site located in a flat tunnel, whereas the 2# site is situated on a narrow ridge. The amplitude and Fourier spectrum of ground motion indicate amplification of seismic waves due to the presence of this narrow, steep ridge. (Hough et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The topographic amplification effect exceeds that of elevation alone.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAmplification factor of slope seismic response\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eAmplification coefficient\u003c/p\u003e \u003cp\u003ePGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eAmplification coefficient\u003c/p\u003e \u003cp\u003eArias intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLushan Ms6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003csup\u003e#\u003c/sup\u003e(1160m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003csup\u003e#\u003c/sup\u003e(1060m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003csup\u003e#\u003c/sup\u003e(900m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLushan Ms4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003csup\u003e#\u003c/sup\u003e(1160m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003csup\u003e#\u003c/sup\u003e(1060m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003csup\u003e#\u003c/sup\u003e(900m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eChangning Ms6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003csup\u003e#\u003c/sup\u003e(1160m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003csup\u003e#\u003c/sup\u003e(1060m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003csup\u003e#\u003c/sup\u003e(900m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eChangning Ms5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003csup\u003e#\u003c/sup\u003e(1160m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003csup\u003e#\u003c/sup\u003e(1060m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003csup\u003e#\u003c/sup\u003e(900m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Amplification effect of site conditions\u003c/h2\u003e \u003cp\u003eAccording to the Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the earthquake monitoring data has provided compelling evidence of site-specific amplification effects: PGA and Arias intensity at monitoring point 3# above the overburden layer exhibited the highest values among all seismic measurements within the same monitoring group. The dominant frequency of the overburden typically ranges from 1\u0026ndash;5 Hz, which is lower compared to that observed in the underlying bedrock. This lower frequency range facilitates resonance with low-frequency seismic waves, resulting in amplified PGA amplitudes. Furthermore, the overburden layer experiences multiple reflections and refractions of seismic wave energy, leading to energy accumulation and qualitative changes. These dynamics contribute to the development of cracks at the trailing edge of the slope, thereby reducing its structural constraint capacity while enhancing its vibrational resilience (Wang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the PGA amplification at the 2# monitoring point during the Lushan Ms6.1 earthquake ranged from 1.0 to 2.0 in all three directions (E-W, N-S, U-D). Similarly, the PGA amplification coefficients at the 2# monitoring site for the Lushan Ms4.5 earthquake, Changning Ms6.0 earthquake, and Changning Ms5.4 earthquake also fell within the range of 1.0 to 2.0. At station 3#, the PGA amplification ranged from 1.5 to 3.0 across all four earthquakes. These findings underscore that the PGA amplification at each monitoring point is predominantly influenced by site-specific conditions, showing minimal correlation with earthquake magnitude.\u003c/p\u003e \u003cp\u003eTo comprehensively study the seismic response of slopes in this region, it is essential to account for both the overburden effect of the site and the influence of slope structure. It is noteworthy that site-specific conditions exert a stronger amplification effect compared to terrain elevation. Specifically, sites situated along ridges are expected to experience less significant sediment amplification than those at lower elevations. This distinction highlights the critical role of site characteristics and topographical features in seismic response analysis. (Hough et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Directional effect\u003c/h2\u003e \u003cp\u003eThe variation in PGA serves as a critical indicator of disaster development. During the Lushan earthquake, the Donghe River area in Baoxing County experienced a concentration of disasters. Specifically, there were a total of 54 geological disasters in Donghe County, with 41 occurring on the left bank and 13 on the right bank. Significantly, the scale of disasters on the left bank of the Donghe River was notably larger than on the right bank, indicating directional characteristics in the distribution of these events.\u003c/p\u003e \u003cp\u003eAs depicted in the Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the seismic waves propagate along a direction of 300\u0026deg;, parallel to the orientation of the left bank slope of the Donghe River, which extends between 280\u0026deg; and 300\u0026deg;. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e, when the slope orientation coincides with the direction of seismic wave propagation, it assumes the role of a back-wave slope. Conversely, if the orientation of the slope opposes the direction of seismic wave propagation, it becomes a face-wave slope. For instance, along the left bank of the Donghe River, where the slope direction matches the propagation direction of seismic waves (300\u0026deg;), it qualifies as a back-wave slope. When seismic waves propagate to the back wave surface, the energy of the seismic waves on the posterior thin plane is greater than that on the face wave surface. Slope A develops numerous large-scale rock collapses, even though Slope B is an anti-dip slope. In contrast, Slope B develops relatively small-scale landslides. The cover layer of Slope B undergoes shallow sliding along the interface between the bedrock and the cover layer, with some landslide material accumulating on the slope surface. Consequently, Slope B shows a relatively limited number of small-scale shallow surface landslides. This alignment can significantly influence the nature and extent of landslide occurrences in such areas. The interaction between the back-wave slope and local terrain amplifies disaster development more significantly on the left bank compared to the right bank. This phenomenon is exemplified by landslides triggered by seismic events such as the Mw7.6 Chichi earthquake in Taiwan in 1999, the Mw7.6 Kashmir earthquake in Pakistan in 2005, and the Ms8.0 Wenchuan earthquake in 2008, all of which exhibited a pronounced back-wave effect. (Xu and Li \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e \u003cb\u003eDirection effect diagram\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Earthquake-triggered landslide failure mechanism\u003c/h2\u003e \u003cp\u003eThe development of geological disasters is shaped by a complex interplay of human engineering activities and natural factors, such as topography, lithology, geological structure, and seismic events.\u003c/p\u003e \u003cp\u003eThe topography of Lushan County is characterized by gentle slopes and relatively flat terrain, providing better rock stability compared to Baoxing County. As a result, Lushan County experiences fewer and smaller-scale geological disasters. In contrast, the terrain of Baoxing features high elevations and steep slopes, particularly within the 25\u0026deg; to 40\u0026deg; range, where most disasters occurred. The landscape of Lushan includes developed tributaries and valleys eroded over time, resulting in significant elevation differences and cutting depths of around 1000 m. Its undulating topography and steep slopes, often V-shaped valleys, create conditions favorable for geological disasters such as slope breaks, mountain bulges, narrow ridges, isolated peaks, and areas prone to instability. These geological features are exacerbated during seismic events, leading to abrupt changes in PGA parameters and increased risk of slope instability. The occurrence of disasters highlights the critical influence of geological and hydrological factors on the spatial distribution of such events (Huang and Li \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009a\u003c/span\u003e). Fault lines, as zones of tectonic activity, often serve as epicenters for earthquakes and related ground failures. Similarly, proximity to water systems can exacerbate the severity of disasters through mechanisms such as soil liquefaction during seismic events or increased erosion and landslide susceptibility in saturated terrains. Understanding this localized pattern is essential for effective risk assessment and the implementation of targeted mitigation strategies.\u003c/p\u003e \u003cp\u003eIn the study area, Mesozoic sandstone dominates the Lushan side, covered by a layer of residual and weathered soil varying in thickness from 10 to 20 meters. Seismic waves interacting with this weathered overburden undergo reflection and refraction, resulting in energy accumulation. This interaction leads to a sudden increase in PGA within the overburden compared to values observed at bedrock stations. Therefore, the overburden not only acts as a source of geological disasters but also amplifies their impact.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Seismogenic structure and Epicenter\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e, it is a schematic diagram of the nappe structure in Lushan and Baoxing, and the Longmenshan fault zone passes through the Baoxing and Lushan. the 2013 Lushan earthquake originated from the Longmenshan front-range fault (Guanxian-Jiangyou fault), whereas the 2022 Lushan earthquake was associated with the Longmenshan central fault (Yingxiu-Beichuan fault). Both faults are nappe tectonics, where older rock strata uplifted to the surface result in fragmented displacement. Despite distance of Baoxing from the epicenter, disaster development was pronounced, influenced by the 'epicenter offset' caused by the nappe structure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring an earthquake, the seismic source is not a singular point but a fault plane, which is often inclined rather than strictly vertical. If the inclination angle is relatively small, even though the initial rupture occurs at the location with the maximum slip, the projection of the focal point on the ground (instrument epicenter) will significantly deviate from the fault line, which is the intersection between the fault plane and the ground surface. Consequently, the epicenter cannot coincide with the surface line, leading to a noticeable disparity between the macro epicenter and the micro epicenter. Macroscopic epicenters, which closely correlate with densely affected disaster areas, differ in distance from microseismic epicenters. Speculating on the macroscopic epicenter can enhance earthquake disaster prediction by pinpointing actual epicenter locations. Including additional reference factors in earthquake disaster assessments can expedite relief efforts and improve disaster prevention efficacy. Epicenter determination should consider seismic intensity alongside monitoring data, especially in nappe structure areas. Engineering facility parameters should also be based on macro epicenters to account for regional tectonic factors in future construction planning.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e \u003cb\u003eNappe tectonics\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThe primary objective of this study is to investigate the seismic response characteristics and disaster development laws of slopes in deep canyon landforms. Based on the field surveys of disaster distribution and analysis of seismic monitoring data, the following findings have been obtained:\u003c/p\u003e \u003cp\u003e(1) The Lushan Ms6.1 earthquake resulted in a concentration of geological disasters predominantly in Baoxing County. These disasters are primarily clustered along seismic faults and show linear alignment with water systems. Significantly, the left bank of the river shows a higher incidence and larger scale of geological disasters compared to the right bank.\u003c/p\u003e \u003cp\u003e(2) Based on seismic data, the PGA amplification at the 2# monitoring point within the monitoring profile ranges from 1.0 to 2.0, while at the 3# monitoring point it ranges from 1.5 to 3.0. PGA values increase with the earthquake magnitude. In addition to topographic features, geological structure, and lithology significantly influence site amplification. Thus, the PGA amplification effect is not solely dependent on seismic magnitude but also on site-specific conditions. Comparing the Lushan and Changning earthquakes, the amplification effect in the monitoring profile area prioritizes site conditions over topography and elevation effects, with the order being: site condition amplification effect\u0026thinsp;\u0026gt;\u0026thinsp;topography amplification effect\u0026thinsp;\u0026gt;\u0026thinsp;elevation amplification effect. Analysis of HVSR curves from multiple seismic events reveals significant seismic response in this region, particularly within the frequency range of 1Hz to 5Hz. Understanding the impact of low-frequency seismic waves on site behavior is crucial for assessing seismic response and designing slopes resilient to seismic activity.\u003c/p\u003e \u003cp\u003e(3) The development of disasters in the Baoxing area can be attributed to a combination of factors, including steep terrain, loose soil layers, and slope orientation. These conditions collectively amplify the PGA on slopes, thereby increasing the susceptibility to disaster occurrences.\u003c/p\u003e \u003cp\u003e(4) The post-earthquake disaster pattern following the Lushan earthquake shows a distinct deviation from the epicenter. Specifically, the disaster concentration is observed primarily in areas characterized by lower intensity levels, specifically Level VII. This phenomenon can be attributed to the distance difference between the macroscopic epicenter and microscopic epicenter within nappe tectonic regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgment\u003c/h2\u003e\n\u003cp\u003eWe sincerely appreciate the National Nature Science Foundation of China (Grant NO. 41877235) and the National Key Research and Development Program of China (Grant No. 2017YFC1501000) support for the preliminary data collection for this paper.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the completion of this article. Professor Yunsheng Wang provided guidance on the core direction of the article. Haochen Wu and the Researcher Jianxian He analyzed the data presented in this paper. Professor Yonghong Luo led the investigation team into the disaster area. Gang Jin contributed data collected in 2019, and Huaying Song participated in data processing. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was partially supported by the National Natural Science Foundation of China (Grant No. 42207194) and State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (SKLGP2023Z028).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement:\u003c/strong\u003e The submitted work is original and it not published elsewhere in any form or language. It is not submitted to any other journal for simultaneous consideration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllen CR, Luo Z, Qian H, Wen X, Zhou H, Huang W (1991) Field study of a highly active fault zone: the Xianshuihe Fault of southwestern China. Geological Society of America Bulletin 103 (9):1178-1199. doi:10.1130/0016-7606(1991)103\u0026lt;1178:FSOAHA\u0026gt;2.3.CO;2\u003c/li\u003e\n\u003cli\u003eAssimaki D, Gazetas G, Kausel E (2005) Effects of local soil conditions on the topographic aggravation of seismic motion: Parametric investigation and recorded field evidence from the 1999 Athens earthquake. 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Landslides 19 (5):1131-1141. doi:10.1007/s10346-022-01853-3\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lushan earthquake, Filed observation, Macroscopic epicenter, Seismic response of the slope","lastPublishedDoi":"10.21203/rs.3.rs-4717589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4717589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, the frequent occurrence of intense seismic events in the mountainous regions of western China has led to numerous geological disasters, resulting in significant human casualties and extensive property damage. Understanding the seismic response of slopes is crucial for elucidating the failure mechanism of earthquake-induced landslides. The distribution of geological landslides and the seismic response of slopes in Lushan are examined through post-earthquake field investigations, landslides inventories, and comprehensive field monitoring. The landslides triggered by the earthquake were primarily concentrated along both banks of the Donghe River in Baoxing County, predominantly manifesting as rockfalls. Geological disasters are predominantly occurred along fault zones and water systems, where vulnerabilities are heightened near 1 km of these faults. The topographic features, lithological composition, and rock mass structure significantly influences the Peak Ground Acceleration (PGA). Notably, PGA experience a sudden increase in areas with slope breaks and loose soil layers, leading to initiation location of the landslide. In the monitoring profile, the PGA amplification factors increases significantly along the slope surface: PGA at the upper slope is 1 to 2 times greater than that of the hilltop reference point, and within the loose soil layer, it ranges from 1.5 to 3.0. Seismic waves in the 1\u0026ndash;5 Hz frequency range are notably amplified in this profile, as evidenced by analysis of the Fourier spectrum and Horizontal to Vertical Spectral Ratio (HVSR) curve. The monitoring profile data reveals that site conditions have a pronounced influence on the amplitude of the acceleration, surpassing the magnification effects of terrain and elevation. In disaster investigations, deviations in the development of disasters from the epicentral area are observed, especially in regions with complex geological structures like nappe tectonics. In such cases, it is crucial to emphasize the impact of both the macroscopic and microscopic epicentershaode. Additionally, more attentions should be paid to understanding the seismic response of slopes, particularly concerning earthquake-triggered landslides.\u003c/p\u003e","manuscriptTitle":"Distribution and failure mechanism of landslides associated with seismic response of the slope during the 2022 Lushan Ms6.1 earthquake","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 07:03:41","doi":"10.21203/rs.3.rs-4717589/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6771bd18-4d9b-4f1e-978c-4ac4f5eec42f","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-16T09:20:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-14 07:03:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4717589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4717589","identity":"rs-4717589","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}