A Method to Predict the Time of Slope Failure Caused by Rainfall Using the Inverse Number of Velocity of Surface Displacement

Fukuzono, Teruki

doi:10.3313/jls1964.22.2_8

Cited by 124 publications

(101 citation statements)

References 1 publication

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“…The collapse is forecasted to occur when the extrapolated line reaches the abscissa axis (corresponding to a theoretical infinite velocity). Such a line may either be convex, straight or concave (Fukuzono, 1985a). When it is straight this phenomenon is sometimes referred to as the Saito effect (Petley et al, 2008).…”

Section: The Inverse Velocity Forecasting Methodsmentioning

confidence: 99%

“…One of the most famous methods is Fukuzono's (1985a), which derives from Saito's (1969), from here on simply called the F and S method respectively. The F method requires that during the acceleration typical of the final stage of the creep (tertiary creep), the inverse of displacement velocity (v −1 ) decreases with time.…”

Section: The Inverse Velocity Forecasting Methodsmentioning

confidence: 99%

“…This is especially important for operative early warning systems. This probabilistic approach is achieved by reiterating the equations from Saito (1969), Fukuzono (1985a) and Mufundirwa et al (2010) (the latter method will be called the M method from here on) for finding t f , using continuously new data and enabling the calculation of the standard deviation.…”

Section: Methodsmentioning

confidence: 99%

“…In spite of this, few empirical methods for predicting the time of failure based on movement monitoring data have been developed (Azimi et al, 1988;Fukuzono, 1985a;Mufundirwa et al, 2010;Saito, 1969) and further investigated on a physical basis (Voight et al, 1988). They are all based on the hypothesis that if a landslide follows a peculiar timedependant geomechanical behaviour (called creep; Dusseault and Fordham, 1994), it will display a hyperbolic acceleration of displacements before failure; by extrapolating this trend from a displacement time series through empirical arguments, it is possible to obtain the predicted time of failure.…”

Section: The Inverse Velocity Forecasting Methodsmentioning

confidence: 99%

“…Predicting landslides, with respect to other natural hazards, is a complex task due to the influence of many factors, such as geomechanical properties, rainfall, ground saturation, topography, earthquakes and many others. So far, few empirical landslide forecasting methods exist (Azimi et al, 1988;Fukuzono, 1985a;Mufundirwa et al, 2010;Saito, 1969;Voight et al, 1988) and none furnish a reliability degree of the prediction, making them unsuitable for decision making. In particular, when mentioning geomechanics, the reference is to the study of landslide behaviour concerning its deformation in relation to the applied stress and with special reference to its postrupture conditions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Landslide forecasting and factors influencing predictability

Intrieri

Gigli

2016

Nat. Hazards Earth Syst. Sci.

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Abstract. Forecasting a catastrophic collapse is a key element in landslide risk reduction, but it is also a very difficult task owing to the scientific difficulties in predicting a complex natural event and also to the severe social repercussions caused by a false or missed alarm. A prediction is always affected by a certain error; however, when this error can imply evacuations or other severe consequences a high reliability in the forecast is, at least, desirable.In order to increase the confidence of predictions, a new methodology is presented here. In contrast to traditional approaches, this methodology iteratively applies several forecasting methods based on displacement data and, thanks to an innovative data representation, gives a valuation of the reliability of the prediction. This approach has been employed to back-analyse 15 landslide collapses. By introducing a predictability index, this study also contributes to the understanding of how geology and other factors influence the possibility of forecasting a slope failure. The results showed how kinematics, and all the factors influencing it, such as geomechanics, rainfall and other external agents, are key concerning landslide predictability.

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Section: The Inverse Velocity Forecasting Methodsmentioning

confidence: 99%

Section: The Inverse Velocity Forecasting Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: The Inverse Velocity Forecasting Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Landslide forecasting and factors influencing predictability

Intrieri

Gigli

2016

Nat. Hazards Earth Syst. Sci.

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Real‐time eruption forecasting using the material Failure Forecast Method with a Bayesian approach

et al. 2015

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Many attempts for deterministic forecasting of eruptions and landslides have been performed using the material Failure Forecast Method (FFM). This method consists in adjusting an empirical power law on precursory patterns of seismicity or deformation. Until now, most of the studies have presented hindsight forecasts based on complete time series of precursors and do not evaluate the ability of the method for carrying out real‐time forecasting with partial precursory sequences. In this study, we present a rigorous approach of the FFM designed for real‐time applications on volcano‐seismic precursors. We use a Bayesian approach based on the FFM theory and an automatic classification of seismic events. The probability distributions of the data deduced from the performance of this classification are used as input. As output, it provides the probability of the forecast time at each observation time before the eruption. The spread of the a posteriori probability density function of the prediction time and its stability with respect to the observation time are used as criteria to evaluate the reliability of the forecast. We test the method on precursory accelerations of long‐period seismicity prior to vulcanian explosions at Volcán de Colima (Mexico). For explosions preceded by a single phase of seismic acceleration, we obtain accurate and reliable forecasts using approximately 80% of the whole precursory sequence. It is, however, more difficult to apply the method to multiple acceleration patterns.

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An analysis of the nonlinear magma‐edifice coupling at Grimsvötn volcano (Iceland)

et al. 2017

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Continuous monitoring of seismicity and surface displacement of active volcanoes can reveal important features of the eruptive cycle. Here high‐quality GPS and earthquake data recorded at Grimsvötn volcano by the Icelandic Meteorological Office during the 2004–2011 intereruptive period are analyzed. These showed a characteristic pattern, with an initial ∼2 year long exponential decay followed by ∼3 year long constant surface displacement inflation rate. We model it by using a one magma reservoir model in an elastic damaging edifice, with incompressible magma and constant pressure at the base of the magma conduit. Seismicity rate and damage were first modeled, and simple analytical expressions were derived for the magma reservoir overpressure and surface displacement as functions of time. Very good fits of the seismicity and surface displacement data were obtained by fitting only three phenomenological parameters. Characteristic time and power strain show maxima from which reference times were inferred that split the intereruptive period into five periods. After the pressurization periods, damage occurring in the third period induced weakly nonlinear variations in magma overpressure and flow, and surface displacement. During the fourth period, the damage dominated and variations became more strongly nonlinear, the reservoir overpressure decreased, and magma flow increased. This process lasted until the power strain reached its second maximum, where instability was generalized. This maximum is a physical limit, the occurrence of which shortly precedes rupture and, eventually, eruption. This analysis allows characterization of the state of the volcanic edifice during the intereruptive period and supports medium‐term prediction of rupture and eruption.

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A Method to Predict the Time of Slope Failure Caused by Rainfall Using the Inverse Number of Velocity of Surface Displacement

Cited by 124 publications

References 1 publication

Landslide forecasting and factors influencing predictability

Landslide forecasting and factors influencing predictability

Real‐time eruption forecasting using the material Failure Forecast Method with a Bayesian approach

An analysis of the nonlinear magma‐edifice coupling at Grimsvötn volcano (Iceland)

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