Investigating rock mass failure precursors using a multi-sensor monitoring system. Preliminary results from a test-site (Acuto, Italy)

In the last few years, several approaches and methods have been proposed to improve early warning systems for managing risks due to rapid slope failures where important infrastructures are the main exposed elements. To this aim, a multi-sensor monitoring system has been installed in an abandoned quarry at Acuto (central Italy) to realise a natural-scale test site for detecting rock-falls from a cliff slope. The installed multi-sensor monitoring system consists of: i) two weather stations; ii) optical cam (Smart Camera) connected to an Artificial Intelligence (AI) system; iii) stress- strain geotechnical system; iv) seismic monitoring device and nano-seismic array for detecting microseismic events on the cliff slope. The main objective of the experiment at this test site is to investigate precursors of rock mass failures by coupling remote and local sensors. The integrated monitoring system is devoted to record strain rates of rock mass joints, capturing their variations as an effect of forcing actions, which are the temperature, the rainfalls and the wind velocity and direction. The preliminary tests demonstrate that the data analysis methods allowed the identification of external destabilizing actions responsible for strain effects on rock joints. More in particular, it was observed that the temperature variations play a significant role for detectable strains of rock mass joints. The preliminary results obtained so far encourage further experiments

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

Towards a standard typology of endogenous landslide seismic sources

The objective of this work is to propose a standard classification of seismic signals generated by gravitational processes and detected at close distances (1 Hz) where most of the seismic energy is recorded at the 1 km sensor to source distances. Several signal properties (duration, spectral content and spectrogram shape) are used to describe the sources. We observe that similar gravitational processes generate similar signals at different slopes. Three main classes can be differentiated mainly from the length of the signals, the number of peaks and the duration of the autocorrelation. The classes are the “slopequake” class, which corresponds to sources potentially occurring within the landslide body; the “rockfall” class, which corresponds to signals generated by rock block impacts; and the “granular flow” class, which corresponds to signals generated by wet or dry debris/rock flows. Subclasses are further proposed to differentiate specific signal properties (frequency content, resonance, precursory signal). The signal properties of each class and subclass are described and several signals of the same class recorded at different slopes are presented. Their potential origins are discussed. The typology aims to serve as a standard for further comparisons of the endogenous microseismicity recorded on landslides.Peer ReviewedPostprint (published version

Comparison of seismic sources for shallow seismic : sledgehammer and pyrotechnics

The pyrotechnic materials are one of the types of the explosives materials which produce thermal, luminous or sound effects, gas, smoke and their combination as a result of a self-sustaining chemical reaction. Therefore, pyrotechnics can be used as a seismic source that is designed to release accumulated energy in a form of seismic wave recorded by tremor sensors (geophones) after its passage through the rock mass. The aim of this paper was to determine the utility of pyrotechnics for shallow seismic engineering. The work presented comparing the conventional method of seismic wave excitation for seismic refraction method like plate and hammer and activating of firecrackers on the surface. The energy released by various sources and frequency spectra was compared for the two types of sources. The obtained results did not determine which sources gave the better results but showed very interesting aspects of using pyrotechnics in seismic measurements for example the use of pyrotechnic materials in MASW

Development and Application of Tools for Avalanche Forecasting, Avalanche Detection, and Snowpack Characterization

Avalanche formation is a complex interaction between the snowpack, weather, and terrain. However, detailed observations typically can only be made at a single point and must be extrapolated over the slope or regional scale. This study aims to provide avalanche forecasters with tools to evaluate the snowpack, avalanche hazard, and avalanche occurrence when manual observations are not feasible. Avalanches that occur within the new storm snow are a prevalent problem for the avalanche forecasters with the Idaho Transportation Department (ITD) along Highway 21. We have implemented a real time SNOw Slope Stability (SNOSS) model that provides an index to the stability of that layer. SNOSS has been run real time starting during the winter of 2011/2012 with model results outputted to a webpage for easy viewing by avalanche forecasters. To further improve the accuracy of SNOSS, the model was evaluated with a large database of avalanches from the Utah Department of Transportation (UDOT). Using weather data and SNOSS results, the probability of an avalanche day producing a natural direct action avalanche was calculated using a Balanced Random Forest (BRF). In the future, we hope that the BRF can provide a probability of an avalanche occurrence given the current weather and snowpack conditions that can be utilized by avalanche forecasters in their normal operations. The concern for avalanche forecasters with highway operations is the threat of an avalanche releasing and hitting a highway. Infrasound generated by an avalanche moving downhill can be detected and tracked using array processing techniques. This will allow avalanche forecasters to evaluate the avalanche hazard more effectively by determining when and where avalanches have occurred. An avalanche detection system has been developed to detect avalanches in near real time using infrasound arrays. The system processes the infrasound data on-site, automatically detects events, and classifies the events using multiple neural networks. If an avalanche has been detected, the system will transmit the necessary information over satellite to be viewed by avalanche forecasters on a webpage

Characterization of geohazards via seismic and acoustic waves

Thesis (Ph.D.) University of Alaska Fairbanks, 2023Earth processes, such as large landslides and volcanic eruptions, occur globally and can be hazardous to life and property. Geophysics -- the quantitative study of Earth processes and properties -- is used to monitor and rapidly respond to these geohazards. In particular, seismoacoustics, which is the joint study of seismic waves in the solid Earth and acoustic waves in Earth's atmosphere, has been proven effective for a variety of geophysical monitoring tasks. Typically, the acoustic waves studied are infrasonic: They have frequencies less than 20 hertz, which is below the threshold of human hearing. In this dissertation, we use seismic and acoustic waves and techniques to characterize geohazards, and we examine the propagation of the waves themselves to better understand how seismoacoustic energy is transformed on its path from a given source to the measurement location. Chapter 1 provides a broad overview of seismoacoustics tailored to this dissertation. In Chapter 2, we use seismic and acoustic waves to reconstruct the dynamics of two very large, and highly similar, ice and rock avalanches occurring in 2016 and 2019 on Iliamna Volcano (Alaska). We determine their trajectories using seismic data from distant stations, demonstrating the feasibility of remote seismic landslide characterization. Chapter 3 details the application of machine learning to a rich volcano infrasound dataset consisting of thousands of explosions recorded at Yasur Volcano (Vanuatu) over six days in 2016. We automatically generate a labeled catalog of infrasound waveforms associated to two different locations in Yasur's summit crater, and use this catalog to test different strategies for transforming the waveforms prior to classification model input. In Chapter 4, we use the coupling of atmospheric waves into the Earth to leverage a dense network of about 900 seismometers around Mount Saint Helens volcano (Washington state) as a quasi-infrasound network. We use buried explosions from a 2014 experiment as sources of infrasound. The dense spatial wavefield measurements permit detailed examination of the effects of wind and topography on infrasound propagation. Finally, in Chapter 5 we conclude with some discussion of future work and additional seismoacoustic topics