A review of the advantages and limitations of geophysical investigations in landslide studies

Landslide deformations involve approximately all geological materials (natural rocks, soil, artificial fill, or combinations of these materials) and can occur and develop in a large variety of volumes and shapes. The characterization of the material inhomogeneities and their properties, the study of the deformation processes, and the delimitation of boundaries and potential slip surfaces are not simple goals. Since the ‘70s, the international community (mainly geophysicists and lower geologists and geological engineers) has begun to employ, together with other techniques, geophysical methods to characterize and monitor landslides. Both the associated advantages and limitations have been highlighted over the years, and some drawbacks are still open. This review is focused on works of the last twelve years (2007-2018), and the main goal is to analyse the geophysical community efforts toward overcoming the geophysical technique limitations highlighted in the 2007 geophysics and landslide review. To achieve this aim, contrary to previous reviews that analysed the advantages and limitations of each technique using a “technique approach,” the analysis was carried out using a “material landslide approach” on the basis of the more recent landslides classification

Kinematic reconstruction of a deep-seated gravitational slope deformation by geomorphic analyses

On 4 November 2010, a deep-seated gravitational slope deformation (North Italy) reactivated with sudden ground movement. A 450,000 m2 mountainous area moved some metres downslope, but the undeniable signs were only connected to the triggering of a debris flow from the bulging area’s detrital cover and the presence of a continuous perimeter fracture near the crown area. Based on two detailed LiDAR surveys (2 m × 2 m) performed just a few days before and after the event, a quantitative topographic analysis was performed in a GIS environment, integrating morphometric terrain parameters (slope, aspect, surface roughness, hill shade, and curvature). The DEMs analysis highlighted some morphological changes related to deeper as well as shallow movements. Both global and sectorial displacements were widely verified and discussed, finally inferring that the geometry, persistence, and layout of all movements properly justify each current morphostructure, which has the shape of a typical Sackung-type structure with impulsive kinematics. Moreover, a targeted field survey allowed specific clues to be found that confirmed the global deduced dynamics of the slope deformation. Finally, thanks to a ground-based interferometric radar system (GB-InSAR) that was installed a few days after the reactivation, the residual deep-seated gravitational slope deformation (DSGSD) movements were also monitored. In the landslide lower bulging area, a localized material progression of small entities was observed for some months after the parossistic event, indicating a slow dissipation of forces in sectors more distant from the crown area

Landslides and geophysical investigations: advantages and limitations

This special issue is dedicated to the geophysical methods applied to investigate, characterize, and monitor landslides. Over the years, both the advantages and limitations of these techniques have been highlighted, and some drawbacks are still open. Some papers were submitted to this special issue, and, after a thorough peer review process, only five articles were selected to be included in this special issue. This relatively small number is probably caused by the difficulty in applying geophysical techniques on slope movements given hard-operating conditions (e.g., high slopes, distance from access roads, and lack of security for the technical operator) and not because the methods limitations are greater than the advantages

Microseismic signal denoising and separation based on fully convolutional encoder–decoder network

Denoising methods are a highly desired component of signal processing, and they can separate the signal of interest from noise to improve the subsequent signal analyses. In this paper, an advanced denoising method based on a fully convolutional encoder–decoder neural network is proposed. The method simultaneously learns the sparse features in the time–frequency domain, and the mask-related mapping function for signal separation. The results show that the proposed method has an impressive performance on denoising microseismic signals containing various types and intensities of noise. Furthermore, the method works well even when a similar frequency band is shared between the microseismic signals and the noises. The proposed method, compared to the existing methods, significantly improves the signal–noise ratio thanks to minor changes of the microseismic signal (less distortion in the waveform). Additionally, the proposed methods preserve the shape and amplitude characteristics so that it allows better recovery of the real waveform. This method is exceedingly useful for the automatic processing of the microseismic signal. Further, it has excellent potential to be extended to the study of exploration seismology and earthquakes

A framework for temporal and spatial rockfall early warning using micro-seismic monitoring

Rockfall risk is usually characterized by a high frequency of occurrence, difficulty in prediction (given high velocity, lack of noticeable forerunners, abrupt collapse, and complex mechanism), and a relatively high potential vulnerability, especially against people and communication routes. Considering that larger rockfalls and rockslides are generally anticipated by an increased occurrence of events, in this study, a framework based on microseismic monitoring is introduced for a temporal and spatial rockfall early warning. This approach is realized through the detection, classification, and localization of all the rockfalls recorded during a 6-month-long microseismic monitoring performed in a limestone quarry in central Italy. Then, in order to provide a temporal warning, an observable quantity of accumulated energy, associated to the rockfall rolling and bouncing and function of the number and volume of events in a certain time window, has been defined. This concept is based on the material failure method developed by Fukuzono-Voight. As soon as the first predicted time of failure and relative warning time are declared, all the rockfalls occurred in a previous time window can be located in a topographic map to find the rockfall susceptible area and thus to complement the warning with spatial information. This methodology has been successfully validated in an ex post analysis performed in the aforementioned quarry, where a large rockfall was forecasted with a lead time of 3 min. This framework provides a novel way for rockfall spatiotemporal early warning, and it could be helpful for activating traffic lights and closing mountain roads or other transportation lines using the knowledge of the time and location of a failure. Since this approach is not based on the detection of the triggering events (like for early warnings based on rainfall thresholds), it can be used also for earthquake-induced failures

Joint detection and classification of rockfalls in a microseismic monitoring network

A rockfall (RF) is a ubiquitous geohazard that is difficult to monitor or predict and poses a significant risk for people and transportation in several hilly and mountainous environments. The seismic signal generated by RF carries abundant physical and mechanical information. Thus, signals can be used by researchers to reconstruct the event location, onset time, volume and trajectory, and develop an efficient early warning system. Therefore, the precise automatic detection and classification of RF events are important objectives for scientists, especially in seismic monitoring arrays. An algorithm called DESTRO (DEtection and STorage of ROckfalls) aimed at combining seismic event automatic detection and classification was implemented ad hoc within the MATLAB environment. In event detection, the STA/LTA (short-time-average through long-time-average) method combined with other parameters, such as the minimum duration of an RF and the minimum interval time between two continuous seismic events is used. Furthermore, nine significant features based on the frequency, amplitude, seismic waveform, duration and multiple station attributes are newly proposed to classify seismic events in a RF environment. In particular, a three-step classification method is proposed for the discrimination of five different source types: RFs, earthquakes (EQs), tremors, multispike events (MSs) and subordinate MS events. Each component (vertical, east–west and north–south) at each station within the monitoring network is analysed, and a three-step classification is performed. At a given time, the event series detected from each component are integrated and reclassified component by component and station by station into a final event-type series as an output result. By this algorithm, a case study of the seven-month-long seismic monitoring of a former quarry in Central Italy was investigated by means of four triaxial velocimeters with continuous acquisition at a sampling rate of 200 Hz. During this monitoring period, a human-induced RF simulation was performed, releasing 95 blocks (in which 90 blocks validated) of different sizes from the benches of the quarry. Consequently, 64.9 per cent of EQs within 100 km were confirmed in a one-month monitoring period, 88 blocks in the RF simulation were classified correctly as RF events and 2 blocks were classified as MSs given their small energy. Finally, an ad hoc section of the algorithm was designed specifically for RF classification combined with EQ recognition. The algorithm could be applied in slope seismic monitoring to monitor the dynamic states of rock masses, as well as in slope instability forecasting and risk evaluation in EQ-prone areas

Microseismic signal denoising and separation based on fully convolutional encoder–decoder network

Denoising methods are a highly desired component of signal processing, and they can separate the signal of interest from noise to improve the subsequent signal analyses. In this paper, an advanced denoising method based on a fully convolutional encoder–decoder neural network is proposed. The method simultaneously learns the sparse features in the time–frequency domain, and the mask-related mapping function for signal separation. The results show that the proposed method has an impressive performance on denoising microseismic signals containing various types and intensities of noise. Furthermore, the method works well even when a similar frequency band is shared between the microseismic signals and the noises. The proposed method, compared to the existing methods, significantly improves the signal–noise ratio thanks to minor changes of the microseismic signal (less distortion in the waveform). Additionally, the proposed methods preserve the shape and amplitude characteristics so that it allows better recovery of the real waveform. This method is exceedingly useful for the automatic processing of the microseismic signal. Further, it has excellent potential to be extended to the study of exploration seismology and earthquakes

Evaluation of the natural risk perception, awareness, and preparedness at school by means of ad hoc questionnaires

One of the Sendai Framework 2015-2030 targets is to reduce the disruption of basic services, like educational facilities. Disaster education is actually considered to be an important factor to promote disaster risk reduction. The school resilience is not related to a specific hazard and vulnerability, but it takes into account many factors, including the people’s natural risk perception and awareness, along with their knowledge and capability of how to behave in an emergency. The scientific literature provides various notions of risk, risk perception, risk awareness, and risk preparedness. The literature on children’s natural risk perception is scarce and very recent compared with that about adults. Indeed, children’s perceptions about nature and environment are truly different from those of adults. The available research mainly concerns the implementation of earthquake emergency measures, while not much is available on flood-risk perception and even less on landslides. The relationship between risk perception, awareness, and preparedness is widely studied, but once again, there is no unambiguous or unique result that depends on the approach and the context. We decided to refine the questionnaire that contributes to assess the school-resilience employed in the Geo-hazard Safety Classification method (GSC). We designed 7 different questionnaires, one for the adult personnel and six for the students, taking into account the peculiarity of each age. These questionnaires were thought through and designed to investigate three main awareness fundamentals: i) knowledge of the correct behaviours and procedure during a natural emergency that occurs at school; ii) perception of the natural risk of the area where the school is located; and iii) general knowledge of the correct behaviours during a natural emergency at school. Three different analyses were carried out on the 5899 filled in questionnaires (820 by personnel and 5079 by students of each school stage) distributed in 27 schools of Tuscany Region (Central Italy): a) school by school; b) questionnaire typology (i.e., different school age); and c) topics (awareness fundamentals i, ii, and iii) and questionnaire typology (i.e., different school age). The results are coherent and show that a) young children’s knowledge is perfectly adequate to their age, b) as the age and responsibilities increase, the awareness and preparation do not increase proportionally, and c) the competences of the school personnel are not sufficient, probably caused by critical issues emerged (i.e., it is not clear where family reunification must take place) and because the wrong hazard perception leads to underestimating the importance of prevention actions and disaster education and. This last outcome turns out to be unexpected. These questionnaires are a suitable, quick, easy and low-cost tool, even if considered separately from the GSC method. The school head-masters or the local and national educational offices actually could use them a) to evaluate the geo-hydrological and seismic risk knowledge and awareness of students, professors and school personnel; b) to project and design actions needed to improve the school-resilience; c) to verify the goodness of the activities developed at point b); and d) as an educational tool to improve the disaster education