Geomorphological transformations and future deformation estimations of a large potential landslide in the high-order position area of Diexi, China

doi:10.21203/rs.3.rs-2116428/v1

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Geomorphological transformations and future deformation estimations of a large potential landslide in the high-order position area of Diexi, China

https://doi.org/10.21203/rs.3.rs-2116428/v1

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Landslides in high-order position areas pose a serious threat to residents located below the area. Therefore, research on the evolution process and underlying dynamic mechanisms is crucial. The majority of relevant studies are based on landslides that have already occurred, however, the investigation of potential landslides is of higher value. In this study, a progressive potential landslide identification method is proposed with the application of SBAS-InSAR (Small Baseline Subset InSAR) and subsequently combined with Google Earth, GF-1 (Gaofen-1), GF-2 (Gaofen-2), ZY-3 (Ziyuan-3)and UAV (Unmanned Aerial Vehicle) imagery, and DEM (Digital elevation model) for further validation. The proposed method is employed to determine the potential landslide in Tuanjiecun by integrating the multi-period data to simultaneously analyze the evolution and mechanism of the potential landslide. The LSTM (Long Short-Term Memory) method is then adopted to predicted the evolution trend based on accumulative deformation from SBAS-InSAR. The results suggest that the Tuanjiecun potential landslide will be relatively stable in the next five years although the largest deformation increased to -238.57mm. The analyses presented here aims to identify the current stage and future development of this potential landslide in terms of the underlying mechanism and transformation, so as to provide effective guidance for disaster prevention and mitigation across the globe.

potential landslide

evolution mechanism

deformation prediction

Tuanjiecun

machine learning

Landslides are considered to be one of the most harmful geological disasters in the world, bringing an enormous loss of property and life each year ¹. Landslides in high-order position areas typically pose huge dangers with their kinetic and gravitational potential energy, yet they can be difficult to recognize on time, particularly if they are concealed ². Landslides are always accompanied by devastating disasters and can even bury entire villages. For example, the Xinmo landslide in 2017 buried the whole village with over 100 inhabitants in just a few seconds ³. Numerous landslides occur each year in the mountainous area of Southwest China, particularly in Sichuan province, threatening the ecology, life and infrastructure ⁴.

Systematic research on landslides began in the 1960s in order to try and qualitatively understand landslides ⁵. Subsequently, numerous studies were performed, making substantial progress and transforming landslide research from qualitative to quantitative over the next 20 years ^6–8. Following this, RS (Remote Sensing) and GIS (Geographic Information System) technology has gradually become an important tool in landslide research with the development and popularization of computer technology and the accuracy improvements in satellite imagery ^9,10. Since the beginning of the 21st century, studies on landslides have become more in-depth and focus on analyzing the relevant mechanisms ^11–13, with a switch from passive investigation to active exploration, including in-situ observations ¹⁴, satellite interferometry ¹⁵, machine learning algorithms ¹⁶, etc. Such advanced technical approaches have increased the effectiveness of the identification, monitoring and management of landslides, greatly reducing the damage of landslide disasters ¹⁷. Therefore, the early identification, monitoring and immediate hedging of huge landslides has have become the key themes of landslide research ¹⁸.

Early landslide identification in the late 1970s focus on recognizing the characteristics of landslides via Landsat images ¹⁹ as well as the analysis and observation of landslide occurrence characteristics by integrated approaches for forecasting ²⁰. Since the 1990s, the geologic evaluation of landslide stability in engineering was typically preformed to ascertain the shear surface, failure plane, internal slider movement and hydrogeological regime of the landslide. However, such geophysical surveys are usually high in costs ²¹. Thus, GPS, phototopography, and InSAR have been important tools to timely monitor the activities of existing landslides ²². In the years following 2000, studies were able to determine the landslide type and quantify changes via high resolution remote sensing images ^23,24. However, as optical images are limited by the influence of clouds and atmosphere etc., the combination of optical images and SAR for the detection and monitoring of landslides has been of great significance in recent years ^25,26. This technique is not only limited to the monitoring of landslide changes, but can also predict the evolution based on highly-developed models ^27,28. The evolution and development of some huge landslides have much common ground which was mainly creep sliding in early stage and rupture at crucial moment, so some landslides have been successfully predicted according to study on early creep sliding ²⁹.

Recent research on landslide identification generally integrates various methods, including the combination of optical and microwave remote sensing ³⁰, as well as the combination of micro and macro approaches, allowing for the more precise early identification of landslides ^31,32. Although physical model experiments are still labor and cost-intensive for landslide monitoring and such approaches are important and have made great advancements ³³, model scholars also prefer the application of machine learning models ³⁴. In particular, machine learning models exhibit a better performance than other mathematical models, and the representation of different machine learning models is variant ³³. In addition, the majority have a strong reliability, particularly improved models that perform better ³⁵. Some organizations require the monitoring and prediction of multiple landslides, thus early warning systems have been developed ³⁶. However, considering the complexity of engineering geological conditions of an individual landslide, it is difficult to replace individual monitoring with generalized systems. Therefore, in the current study, we focus on the monitoring of a single landslide. Moreover, the existing literature generally absorbed in existing landslides, such as the Xinmo landslide in 2017 ³⁷ and the Baige landslide in 2018 ³⁸. The early identification of potential landslides can avoid the occurrence of landslides in a timely manner. In particular, potential landslides can be determined by integrating optical and radar imagery to carry out a series of in-depth evaluations, followed by the implementation of long-term forecasts and regular investigation. The current research is based on an comprehensive integration of previous studies, providing important guidance to reduce losses of landslides timely.

Landslides are the most common geological hazards in Southwest China, particularly high-order position and large-scale landslides. Diexi in southwest China, with frequent earthquakes and a complex geo-structure, is selected as the study area of this work. The suspected potential landslide areas are first identified by SBAS-InSAR data, followed by the recognition of these suspected potential landslides by multiple-source remote sensing images to further identify the suspected potential landslides. Finally, Tuanjiecun suspected potential landslide is selected as the key survey target and field investigations are conducted. Three key steps are performed after the selection of the Tuanjiecun potential landslide: a) the analysis of over 50 months of SBAS-InSAR data; b) high precision image comparisons over a 10-year period and c) differential DEM analysis at various periods. Following this, the deformation velocity of the first 48 periods from SBAS-InSAR data are used to predict the future deformation velocity by machine learning and verified by the subsequent 10 periods. The proposed prediction model can be applied to future deformation predictions and of great significance to in-depth landslide studies.

The Tuanjiecun potential landslide is located in Diexi, the Sichuan province of China, where geological hazards frequently occur due to the strong compression from the Qinghai Tibet Plateau and Sichuan Basin. As shown in Fig. 1, the observed potential landslide lies on the crushed steep strips of a typical meizoseismal area. Dozens of earthquakes have occured around Tuanjiecun in the past 100 years. In addition, the Tianlonghu hydropower station has an impact on the Tuanjiecun potential landslide, and has been in operation beside to Diexi Lake close to Tuanjiecun since 2003. The storage - discharge - storage of water over time affects the distribution of stress and guides ground sinking. Field surveys have revealed in Tuanjiecun different degrees of cracks on the walls of almost all houses and pavement, as well as the soil ground on roadside (Fig. 2).

Insert Fig. 1 here.

Additional in depth surveys have determined, the presence of not only cracks in every corner, but extensive roadbed cavities under the cement pavement with depths exceeding 90cm in Tuanjiecun. More specifically, the depth of these cavities is smaller in the north compared to the south, and all cracks are located at the corners or on steep pavements. This demonstrations that the slope is inclining to the south, with a major slip direction at 160° after measurement. This is also proved by the 5–20 cm gaps between the pavement and north mountain, which are currently expanding. Numerous underground cavities are located in the mountain, particularly in the central and lower parts, (Fig. 3 (a)-(d)). Underground excavation to determine the deformation characteristics did not identify an obvious collapsing or accumulation bodies on the ground, yet the hydrostatic sedimentary layer is observed everywhere (Fig. 3 (b) and (c)). It is not possible to ascertain the exact time of the initial creeping, however we can observe that slope is moving at least 1 cm each year.

Insert Fig. 2 here.

The front edge coordinates of the landslide were determined as 32°2'31.32"N, 103°39'54.74"E, 2153 m and the rear edge coordinates as 32°2'53.29"N, 103°39'43.92"E, 2534 m. The height difference between the front and rear edges of the potential landslide is approximately 400 m, with a longitudinal length close to 800 m and a transverse width of 700 m. The main sliding direction is approximately 160°, and the landslide slope is 36°. The stratigraphic structure is relatively complex. The front region is the quaternary accumulation layer and super floodplain terrace alluvium, and is mainly composed of loess. The bottom region is gravel with breccia, with a thickness of 128–225 m. The central area is the Bocigou formation of the Xikang Group of the Lower Triassic system. The upper component of the lithology is gray, gray-green or purple-red and silty slate, intercalated with a small amount of gray thin-layer sandstone or gray thin-layer limestone, while the bottom is the ferromanganese layer. The lower part is a gray and gray-white thin layer of thin plate limestone mixed with calcareous slate or phyllite, and locally produces a thin layer of iron and manganese, with a thickness of 16–101 m. The posterior part is the Zagunao formation of the Xikang Group of the Middle Triassic system. The rock stratum strike is determined as 10°, with an occurrence of 100°∠45°.

Insert Fig. 3 here.

Research on potential landslides proves to be more complicated compared to landslides that have already occurred. In order to overcome this, we adopted multi-source data and approaches, including potential landslide searching, the corresponding geological environment, deformation history, deformation prediction and verification, deformation mechanisms, kinematic characteristics and disaster assessments. Figure 4 summarizes the data and methodology applied in this study. The key steps of the methodological approach are described in the following.

1) Sedimental identification with InSAR

Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) is frequently implemented for the large-area, long-term subsidence monitoring of land, and in particular, for landslide detection in recent years ³⁹. This technique has highly precise and has a wide area coverage, and can be used to determine comprehensive information of surface deformation ⁴⁰. InSAR technology can also approximate the time at which landslides began. ⁴¹. In particular, it can detect surface changes at the millimeter scale ⁴², and therefor compute surface deformation prior to landslides according to previous records. In this work, we employed 157 single-look complex (SLC) datasets spanning from March 2017 to December 2021 (5 years) and a DEM with a 12.5 m spatial resolution. The datasets were analyzed using the SBAS-InSAR approach by GAMMA2020 platform to determine the potential landslide. The post processing was divided into the following two components; i) a model was constructed with deformation data from the previous four years of potential landslides using a machine learning method in MATLAB (2019b). The scheme was set up to second-guess the future deformation based on existing data. ii) The deformation predictions of the last year were then checked, with a greater consistency indicating a greater accuracy of the model. Then the following deformation over a period of time in future would forecast via this model.

2) Multi-source optical images for visual interpretation

Optical remote sensing imagery is require to define a potential landslides when InSAR detects surface deformations. In order to determine the grade of potential landslides, the deformation is taken as relative not absolute. This can be complicated for InSAR data, thus optical images can be employed to estimate ³⁷ the topography, landforms, geological environment, location, vegetation, slope, size, etc. of landslides ⁴³. The optical imagery used in this study include Google Earth online maps, Tianditu online maps, Gaofen-2 (GF-2) satellite imagery with a 1 m resolution and Ziyuan-3 (ZY-3) satellite imagery with a 2 m resolution.

Areas that may be potential landslides were identified by combining optical images with InSAR deformation data. An initial screening excluded the impact from glaciers and urban land subsidence. Following this, each potential site was evaluated separately based on the visual interpretation of high resolution images. Large potential landslides often occur on ancient landslides, therefore, special attention should be paid to the deformation of ancient landslide. Finally, a field verification was performed using high-resolution UAV optical images and orthophoto maps with a 0.5 m resolution, identifying gullies, cracks, collapses etc. In addition, the final verification required a more detailed engineering geological survey.

3) Comparison of DEMs from several time periods

The InSAR data identified deformation around Tuanjiecun village in 2017–2019, and thus a 0.2-m resolution DEM was derived using the UAV data for comparisons. The topography and slope information were obtained from the same time period. It lasts deformation around Tuanjiecun village monitoring by InSAR from 2019 to 2020, and thus a finer resolution DEM (0.056 m) was obtained by the UAV. Superposition calculations were performed in ArcGIS 10.8 (ESRI) between these two DEMs to analyze topography changes. In addition, in-depth analysis of the deformation mechanism and possible trailing edges was conducted using the higher resolution DEM. The changes between the two DEMs were employed to verify the InSAR deformation results, too.

Insert Fig. 4 here.

4.1 Ascertainment of Tuanjiecun potential landslide

The multi-source optical images were integrated with the SBAS-InSAR method. First, we obtained the subsidence accumulation area of the surface through large-scale analysis via SBAS-InSAR technology to select several critical points. The multi-source optical images of these critical points were then evaluated based on the following factors: high-order, threatened the inhabitants, large scale, prominent round-back armchair terrain and keep exacerbating etc. This was then used to ascertain the Tuanjiecun potential landslide. Following this, the landslide was divided into four regions by deformation points within the 3D model, namely, Q, W, E and R. E and R are subsided points while Q and W are uplifting points (Fig. 5). The subside of zone R is much stronger than that of zone E, and zone R (located at the bottom of the potential landslide) is the main human activity area of the study site. There are two cracks in the zone Q both of which are almost undetectable unless viewed from the 3D model. The deformation, high-order position, perniciousness to residents in foot of the hill and the crack distribution in the trailing edge indicated this site to be a potential landslide.

Insert Fig. 5 here.

4.2 Deformation history of Tuanjiecun potential landslide

The deformation history of the landslide was investigated based on three methods, namely, SBAS-InSAR data, changes in the DEM, and the multi-source optical images.

Analysis was performed on the SBAS-InSAR image sequences from March 2017 to December 2021 in order to define the period of deformation (Fig. 6). The deformation began in the low altitude eastern zone, corresponding to the residential corner and lower region of this potential landslide in early 2017, yet deformation levels remained low. The deformation of the low altitude eastern zone and lower region increased in 2017. In 2018, the range was enhanced but did not connect the two zones. The deformation reached to the center of the potential landslide, and the maximum cumulative variable changed from − 57 mm to -94 mm. The deformation subsequently increased in 2020, whereby the maximum cumulative variable reached − 158 mm, yet there was no significant deformation observed in the zone between the center and eastern low altitude area. In 2021, the zone between the center and eastern low altitude area began to exhibit signs of deformation, and the maximum cumulative variable over − 200mm.

Insert Fig. 6 here.

High-resolution UAV data from 2019 was used to obtain a DEM in real time in order to obtain more detailed observations. The same was done for 2020 and the two DEMs were compared (Fig. 7). The D-value of the two DEMs generally exhibited negative values, and only a areas presented no obvious changes. In addition, several positive values were observed, representing the surface change of the Tuanjiecun potential landslide. For areas with negative D-values, the darker the green color, the greater the difference. The changes in the south exceed those in north of Tuanjiecun village, indicating that the lower part of this mountain (where residents, roads and arable land are located) has a larger deformation compared to the higher zone. The deformation trend is consistent with that of SBAS-InSAR data. The higher-resolution UVA images can be used to determine much finer details compared to the satellite images.

Insert Fig. 7 here.

Two cracks were observed in the trailing of the potential landslide from the multi-source optical images in 2003, 2014, 2017, 2018, 2019 and 2020 (Fig. 8). Table 1 reports the state of these cracks for each year. The cracks seem to have formed prior to 2003. From 2003 to 2014, the Crack A experienced significant deformation, with an increase in length from 45.97 m to 112.60 m and in width from 1.63 m to 3.83 m. Crack B exhibited relatively less changes, with a change in length from 124.22 m to 147.38 m and width from 5.05 m to 10.07 m. The changes in the following years were relatively small, with lengths of 130.7 m, 131.09 m, 131.94 m, and 132.22 m, and widths of 7.38 m, 8.10 m, 9.05 m, and 9.36 m for Crack A; and lengths of 149.90 m, 150.03 m, 151.35 m, and 151.64 m, and widths of 10.45 m, 10.45 m, 10.59 m, and 10.66 m for Crack B in 2017, 2018, 2019 and 2020, respectively.

Table 1

Crack parameters at the rear of the Tuanjiecun potential landslide.
Year		2003	2014	2017	2018	2019	2020
Data Source		Google Earth	ZY-3	GF-1	GF-2	UVA	UVA
A	Length(m)	45.97	112.60	130.7	131.09	131.94	132.22
A	Width(m)	1.63	3.83	7.38	8.10	9.05	9.36
B	Length(m)	124.22	147.38	149.90	150.03	151.35	151.64
B	Width(m)	5.05	10.07	10.45	10.45	10.59	10.66

Insert Fig. 8 here.

In summary, both cracks formed before 2003 and are still evolving. Due to the lack of the high-precision images prior to 2003, we are unable to determine the exact time of their formation. Crack A grew rapidly over the study period, while Crack B exhibited a more gradual growth rate. Note that the growth occurred in microscopic increments on an annual basis.

Insert Table 1 here.

4.3 Future deformation estimation of the Tuanjiecun potential landslide

Based on of deformation history, a machine learning method was employed to forecast the future deformation. Machine learning approaches build models by learning from existing data. In this study, the LSTM (Long Short-Term Memory) method, with its proven performance ^44,45 was chosen to identify the trend of deformation and the corresponding values by learning from the deformation history. First, learning was performed from March 2017 to March 2021 to forecast the deformation of April 2021 to December 2021. Following this, the forecast data was compared with the results of SBAS-InSAR from April 2021 to December 2021 (Fig. 9), confirming the accuracy of the former. Finally, a forecast of the future deformation was performed for the period subsequent to December 2021 using data from March 2017 to December 2021. Figure 10 depicts the results, revealing the continuous deformation of the landslide.

Insert Fig. 9 here.

The predicted results are highly consistent with the actual deformation results from the macro analysis. In order to further verify the accuracy, the distribution values of the actual and predicted cumulative variables of the Tuanjiecun potential landslide are depicted in Fig. 11, representing a micro comparative analysis and confirming the relative accuracy of the results. This is a variable analysis of each month in the longitudinal direction, while for the latitudinal direction the Pearson correlation analysis of the deformation trends between the actual and predictive values during the nine months was used. A positive correlation denotes a consistent deformation trend between the actual and predicted values during the nine months at a given point, and negative correlation is used otherwise. The positive correlations account for 71.09% of the total correlations, demonstrating the high accuracy of the trend predictions.

Insert Fig. 10 here.

In general, the comparison and verification of the actual cumulative deformation variables and predicted cumulative variables of the Tuanjiecun potential landslide from April 2021 to December 2021 indicate the high accuracy of LSTM method. Thus, it is used for the subsequent prediction analysis. The prediction results indicated that the accumulative deformation increases consistently, however the scope of deformation does not change significantly.

Insert Fig. 11 here.

Long term monitoring based on SBAS-InSAR data was integrated with multi-source optical image analysis and situ investigations to identify and verify the deformation of a potential landslide and subsequently obtain the corresponding mechanism and mode. Then the most striking significance is the time this potential landslide will break out or which evolution stage it is in, next, the deformation mechanism and forecast time will be discussed in order to argue it.

5.1 Kinematics and deformation mechanism analysis

Analysis of the deformation proves in Section 4.2 reveals that the deformation process initially occurred in the bottom and central lower part of the landslide site, and subsequently these two part expanded and connected gradually. The field survey reveals that at the rear edge, the landslide is bounded by bedrock while the slope deposit is thick. The central area exhibits obvious sink rate and further behind there is a steep slope consisting of a deposit and lake sediment layer. The front edge is thin and contains residential and cultivated land close to the steep slope area, while the left side of the foot is eroded by Diexi Lake. Combining both of deformation process and field investigation determines the location of the initial deformation in the regions of the thick deposit and front edge, with the uplift area positioned in this rear edge area. The characteristics of the section profile at this potential landslide are denoted as the rear edge zone, steep zone, subsidence zone and eroded zone, respectively (Fig. 12).

Insert Fig. 12 here.

In the following we describe the source deformation, main deformation and shear-outlet of these four components.

The source deformation area compromises the eroded zone and subsidence zone, which is confirmed by cavities in Fig. 3 and is attributed to the dredging and erosion over a long period of time. These cavities are distributed widely in the two zones and exhibit a trend of expansion. The extensive distribution of the lacustrine deposit is also a source of the observed cavities. The loss of soil in these cavities, as well as the long-term lake erosion at the top of the slope, accelerated the land subsidence in the eroded and subsidence zones. Furthermore, the operation of the Tianlonghu hydropower station from 2003 sped up the dredging and erosion. The cracks observed on local houses and the curved broken pavement surrounding the mountain and leaning towards the mountain toe offer additional evidence of sliding (Fig. 3). The curved broken pavement proves the uneven settlement of the ground and the area beside the mountain foot sinks more than that at the mountaintop. This further demonstrates the subsiding of the eroded and subsidence zones, which is consistent with the SBAS- InSAR results.

The main deformation area is the steep zone. This is also the area with the largest settlement value, close to 70 mm/y. Its deformation is due to the instability caused by the sinking of the eroded and subsidence zones, generating an integral downward slide. As large slope with two overall cracks is formed. The unequal down-sliding of the subsidence and steep zones induced the slight surface collapse. The thickness of the sliding body is difficult to determine due to the low deformation speed, however it is estimated to exceed 20 m by field estimations of the accumulation deposit. Moreover, the rear edge zone close to the steep zone exhibits an uprising current. This zone is mainly composed of bedrock and overlying accumulation, which begin to slightly uplift following the marginal subsiding in the lower region and subsequently slide down when the lower components are unstable.

Through the above analysis, two possible shear-outlets were ascertained at the edge of the eroded zone and on the junction of the bulging and steep zones, respectively, the occurrence of which depend on the deformation speed. If the cumulative deformation of the two parts are nearly equal the shear-outlet will be at the edge of the eroded zone; and if the cumulative deformation of the steep zone is much larger than the eroded and subsidence zones, the shear-outlet will be at the foot of the steep zone. Note that this work does not consider the sudden occurrence of earthquakes, downpours, flooding etc.

Moreover, rural roads cross the front edge, while a small number of residential houses and a large area of cultivated land are located at the foot of the slope, which along with other engineering activities accelerate the sinking of the subsidence zone.

5.2 Analysis of future deformation

The velocity and accumulative deformation-time value are the factors employed to identify the evolution stage of landslides. ^46,47. The current settlement velocity of the Tuanjiecun potential landslide is still at a relatively stable deformation stage, denoted as the early creep stage ⁴⁸. The cumulative deformation reveals that the landslide is far from reaching the critical threshold ⁴⁹. We subsequently predicted the evolution trend over the next five years (Fig. 10). The deformation trend was difficult to identify intuitively from Fig. 10, thus the distribution histogram of the predicted cumulative deformation values were presented in Fig. 13. This agrees with the division level in Fig. 10. The cumulative deformation histograms from 2022 to 2026, based on predicted values, reveal the maximum number of points in the middle of the histogram to gradually evolve from − 11.775 mm to -26.203 mm. This indicates the significant downward trend of the whole region. The number of potential landslide points (i.e. points in red bracket) has not increased significantly, indicating that the scope of potential landslide has not expanded. The potential landslide deformation area exhibits a slow growth trend, suggesting that the Tuanjiecun potential landslide will continue to be in a relatively stable stage for a long time. However, the whole slope and surrounding areas are slowly sinking, and the potential landslide area sinks more seriously .

Insert Fig. 13 here.

In summary, our results reveal that in the absence of large earthquakes, excessive precipitation, abnormal human activities, etc., the Tuanjiecun potential landslide will not be active in the next five years. At present, the Tuanjiecun potential landslide is in a slow creep state, and requires a certain amount of time and gravity accumulation to evolve into the tertiary creep state. However, it is necessary to closely observe the area considering the devastating destruction of landslides in high-order position areas and the complex engineering geological conditions around the village.

Diexi, China is an ecologically fragile mountainous area with complex geological conditions, frequent earthquakes and various human engineering activities. In order to avoid the recurrence of major catastrophes such as the Xinmocun landslide, the early identification of landslides must be performed. This study determines deformation characteristics from SBAS-InSAR data as the key information source, and integrates it with Google Earth, GF-1, GF-2, ZY-3, and UAV imagery, as well as DEM data and field investigations of the Tuanjiecun potential landslide to analyze the failure mechanism. Low resolution data is used for the initial investigation, while high resolution data is employed for further analysis and to verify the initial investigation. This progressive identification method improves the safety and efficiency of potential landslide investigations.

The LSTM method is a machine learning prediction approach that employs the cumulative displacement to predict the future settlement based on the existing displacement information. This method can effectively predict the deformation in a certain period. The Tuanjiecun potential landslide is predicted to remain stable for the following several years in the absence of extreme events. This provides immediate risk aversion and guidance for the establishment of an effective disaster system.

This paper solves the problem of determining and analyzing the evolution of a certain potential landslide, changing the landslide research from passive avoidance to active prevention. Moreover, the Tuanjiecun potential landslide deformation was observed to occur prior to 2014 due to the lack of historical data, however the specific time at which it begun remains unknown. The deformation position can only be identified from the existing data, while the initial deformation value requires further research .

Data availability

The datasets used and analysed during the current study available from the corresponding author on reasonable request.

Acknowledgements

This work was financially supported by the National Key Research and Development Program of China (No. 2017YFC1501004).

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author contributions

Y.L. finished the Investigation, Methodology, Software, Formal analysis, Writing – original draft. P.X. finished the Conceptualization, Investigation, Writing – review & editing, Project administration, Funding acquisition. C.C. finished the Validation, Writing ,Investigation, Project administration. W.Z. finished the Conceptualization, Methodology, Validation, Investigation. M.Z. finished the Validation, Software, Data curation. K.Z. finished the Formal analysis, Data curation. All authors reviewed the manuscript.

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No competing interests reported.

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Geomorphological transformations and future deformation estimations of a large potential landslide in the high-order position area of Diexi, China

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Abstract

Figures

1. Introduction

2. Study Area And Geological Settings

3. Data And Methods

4. Ascertainment Process And Deformation Analysis

4.1 Ascertainment of Tuanjiecun potential landslide

4.2 Deformation history of Tuanjiecun potential landslide

4.3 Future deformation estimation of the Tuanjiecun potential landslide

5. Discussion

5.1 Kinematics and deformation mechanism analysis

5.2 Analysis of future deformation

6. Conclusion

Declarations

References

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