Assessing the internal structure of landslide dams subject to possible piping erosion by means of microtremor chain array and self-potential surveys

Wang, Fawu; Okeke, Austin Chukwueloka; Kogure, Tetsuya; Sakai, Tetsuya; Hayashi, Hisao

doi:10.1016/j.enggeo.2017.12.023

Cited by 40 publications

(12 citation statements)

References 33 publications

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“…A good example of this interaction between piping and groundwater is found in the paper of Castañeda et al [85]. This has also been found as a source of risk, as was reported by Wang et al [86], due to the formation of landslide dams as a consequence of piping erosion. Piping is a key landform to understand the rapid evolution of the landforms and our research confirms that few factors control the piping development.…”

Section: Variable Importance Analysismentioning

confidence: 68%

Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data

Band

Janizadeh

Saha

et al. 2020

Land

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Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that were divided into training (70%) and validation (30%) for modeling. The area under curve (AUC) was used to assess the effeciency of the RF, SVM, and Bayesian GLM. Piping erosion susceptibility results indicated that all three RF, SVM, and Bayesian GLM models had high efficiency in the testing step, such as the AUC shown with values of 0.9 for RF, 0.88 for SVM, and 0.87 for Bayesian GLM. Altitude, pH, and bulk density were the variables that had the greatest influence on the piping erosion susceptibility in the Zarandieh watershed. This result indicates that geo-environmental and soil chemical variables are accountable for the expansion of piping erosion in the Zarandieh watershed.

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Section: Variable Importance Analysismentioning

confidence: 68%

Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data

Band

Janizadeh

Saha

et al. 2020

Land

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“…Back-analysis and modeling of past rock slope failures [7,8], combined with studies on landforms, sedimentology and internal structures of landslide deposits [9][10][11], allow insight into movement processes and emplacement sequences [12][13][14]. Reconstructing the processes that occur during release and emplacement provides fundamental data on the 2 of 26 reasons for past and possible future slope failures, and on the extent of possibly impacted areas [7].…”

Section: Introductionmentioning

confidence: 99%

Reconstructing the Gorte and Spiaz de Navesele Landslides, NE of Lake Garda, Trentino Dolomites (Italy)

et al. 2021

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We applied a multi-method approach to reconstruct the Gorte rock avalanche (85–95 Mm3) located at the northeastern end of Lake Garda. The combination of field mapping, characterization of bedrock discontinuities, Dan3D-Flex runout modeling and dating of boulders with cosmogenic 36Cl supports the conclusion that the deposits stem from a single rock avalanche at 6.1 ± 0.8 ka. The Gorte event may have triggered the Spiaz de Navesele–Salto della Capra landslide (3.2 Mm3), whose deposits cover the southern end of the Gorte deposits. First-order controls on detachment were the NNE–SSW- and WNW–ESE-oriented fractures in the limestone bedrock, related to the Giudicarie and Schio-Vicenza fault systems, respectively. Dan3D-Flex runout modeling sufficiently reproduced the Gorte rock avalanche, which involved detachment and sliding of a quasi-intact block, likely along marly interbeds, followed by rapid disintegration. The frictional rheology in the source area and the turbulent frictional rheology (Voellmy) in the remaining part best replicate the observed deposit extent and thickness. Heavy precipitation that occurred at that time may have contributed to failure at Gorte. Nonetheless, its timing overlaps with the nearby (<15 km) Dosso Gardene (6630–6290 cal BP) and Marocca Principale (5.3 ± 0.9 ka) landslides, making a seismic trigger plausible.

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“…Therefore, the study of landslide dam structure and mechanical properties is of the utmost importance, since it can allow understanding the evolution of their stability conditions (cf. Wang et al 2014Wang et al , 2018a.…”

Section: Introductionmentioning

confidence: 99%

Ambient noise and ERT data provide insights into the structure of co-seismic rock avalanche deposits in Sichuan (China)

Gaudio

Wąsowski

et al. 2021

Bull Eng Geol Environ

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The post-seismic history of the 2008 Mw7.9 Wenchuan earthquake shows that marginally stable deposits of large co-seismic landslide dams can pose persistent debris flow hazards for the downstream areas. Here, we combine analyses of single-station recordings of ambient noise with electrical resistivity tomography (ERT) surveys to explore the potential of drawing information on structure and geometry of the deposit of a large rock avalanche triggered by the Mw 7.9 2008 Wenchuan earthquake, which dammed the Yangjia stream in the Sichuan Province (China). The substantial thickness and heterogeneity of this kind of deposits limit the application of standard geophysical techniques, like active seismic surveys, which require highly energetic sources and long linear geophone arrays to reach adequate investigation depths. Passive single-station methods, relying on ambient noise recordings to determine site resonance properties, controlled by the contrast between soft surface layers and a stiffer substratum, offer the opportunity of investigating subsoil properties down to larger depths. In particular, we use a recently developed technique, which isolates the contribution of Rayleigh waves to ambient noise and draws information on sub-soil properties from the inversion of Rayleigh wave ellipticity curves plotted as function of frequency. In this framework, the ERT data can support the ellipticity curve inversion, typically affected by highly non-univocal solutions, by providing constraints for defining of the thickness of the uppermost surficial layers. The results allowed inferring the overlap of different layers within the 2008 rock avalanche deposit, as well as estimating lateral variations in their thickness and S-wave (Vs) velocities.

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Assessing the internal structure of landslide dams subject to possible piping erosion by means of microtremor chain array and self-potential surveys

Cited by 40 publications

References 33 publications

Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data

Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data

Reconstructing the Gorte and Spiaz de Navesele Landslides, NE of Lake Garda, Trentino Dolomites (Italy)

Ambient noise and ERT data provide insights into the structure of co-seismic rock avalanche deposits in Sichuan (China)

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