The assessment of submarine slope instability in Baiyun Sag using gray clustering method

Li, Cuilin; Wu, Shiguo; Zhu, Zhenqi; Bao, Xingxian

doi:10.1007/s11069-014-1241-1

Cited by 13 publications

(5 citation statements)

References 16 publications

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“…The s is the number of landslide susceptibility classes, which is 5 in this study. Gray clustering for the landslide susceptibility mapping has the following steps [51][52][53]…”

Section: The Improved Information Value Model Based On Gray Clusterinmentioning

confidence: 99%

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Chen

Deng³

et al. 2017

IJGI

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Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.

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“…The s is the number of landslide susceptibility classes, which is 5 in this study. Gray clustering for the landslide susceptibility mapping has the following steps [51][52][53]…”

Section: The Improved Information Value Model Based On Gray Clusterinmentioning

confidence: 99%

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Chen

Deng³

et al. 2017

IJGI

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“…However, when the hydrate decomposes, the contribution made by hydrate as an agent of cementation also disappears and ultra-static pore pressure is generated. The strength of the hydrate and hence surrounding strata decreases, which can trigger a submarine landslide or submarine turbidity current and cause wellbore instability and platform overturning (Li et al, 2014;Li and Han, 2021;Liu et al, 2017;Rutqvist et al, 2009;Sultan et al, 2004;Zhang et al, 2021). In addition, one of these geological events may also trigger another, initiating what has been called a "disaster chain" (Lu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Influence of hydrate exploitation on stability of submarine slopes

et al. 2022

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The decomposition of natural gas hydrate will reduce the cementation effect of hydrate and produce ultra-static pore pressure, which will change the mechanical characteristics of the reservoir. Eventually, a series of geological disasters could be triggered, of which the submarine landslide is a typical example. In order to analyze the stability of hydrate-bearing submarine slopes and to explore the internal relationship between hydrate decomposition and submarine landslides, a "two-step reduction method" was described in this paper. This method was based on a strength reduction approach, which can be used to assess the effects of the initial geostress balance and hydrate decomposition on substrate strength reduction. This method was used to reveal the essence of hydrate decomposition and then a joint operation mode of multi-well was proposed. The internal relationship between hydrate decomposition and submarine landslides were analyzed in detail. And the development process and mechanism of submarine landslide were deeply discussed. The results showed that hydrate decomposition is a dynamic process of stress release and displacement, where the "stress inhomogeneity" distributed along the slope is transformed into "displacement inhomogeneity". We concluded that hydrate decomposition could trigger a submarine landslide, especially along a sliding surface. The formation of submarine landslide is a gradual development process, and presents the dual characteristics of time and space.

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“…ereby, it is necessary to comprehensively consider the influence of quantitative and qualitative indexes on slope stability. At present, analytic hierarchy process (AHP) [6,7], fuzzy mathematical method [8][9][10], grey clustering method [11], catastrophe theory [12], cloud model [13,14], support vector machine [15,16], and other "soft computing" methods have been widely used in evaluation of slope stability taking into account quantitative and qualitative indicators comprehensively. When using these methods to calculate the stable state of the slope, the weights of indicators must be determined.…”

Section: Introductionmentioning

confidence: 99%

A New Evaluation Method for Slope Stability Based on TOPSIS and MCS

Chen

Zhao

Liu

et al. 2020

Advances in Civil Engineering

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Slope evaluation is a basic geotechnical engineering issue. The rationality of index weight greatly affects the accuracy of evaluation results in the evaluation system. Furthermore, in practical engineering, some indexes can be considered random variables obeying a certain distribution. Traditional evaluation methods of slope stability ignore the effect of this index uncertainty. Therefore, it is necessary to obtain the evaluation results of slope stability reasonably by modifying the previous weighting methods and considering the uncertainty values of the indexes. A new method has been introduced to solve the problem mentioned previously based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and Monte Carlo simulation (MCS). TOPSIS is used as a basic model for evaluating slope stability. TOPSIS and MCS are coupled to establish multiobjective optimization simulation model, which can search the more optimal and reliable weight. The optimal weight is then substituted into the TOPSIS basic model to calculate the status of slope stability. In this calculation process, MCS is introduced into the TOPSIS basic model to consider the uncertainty value of index. The new method of evaluating slope stability was demonstrated by taking a practical project as an example. Compared with other weighting methods, the coupled TOPSIS and MCS model can obtain the most reliable weight, and the reliability is 48.7%. Then, the evaluation of slope stability was examined with the certainty and uncertainty cases, respectively. The results demonstrate that the proposed new evaluation method is more realistic than the traditional methods for evaluating the slope stability. The new method has high accuracy and is easy to use.

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The assessment of submarine slope instability in Baiyun Sag using gray clustering method

Cited by 13 publications

References 16 publications

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Influence of hydrate exploitation on stability of submarine slopes

A New Evaluation Method for Slope Stability Based on TOPSIS and MCS

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