High-accuracy real-time microseismic analysis platform : case study based on the super-sauze mud-based landslide

Understanding the evolution of landslide and other subsurface processes via microseismic monitoring and analysis is of paramount importance in predicting or even avoiding an imminent slope failure (via an early warning system). Microseismic monitoring recordings are often continuous, noisy and consist of signals emitted by various sources. Automated analysis of landslide processes comprises detection, localization and classification of microseismic events (with magnitude <2 richter scale). Previous research has mainly focused on manually tuning signal processing methods for detecting and classifying microseismic signals based on the signal waveform and its spectrum, which is time-consuming especially for long-term monitoring and big datasets. This paper proposes an automatic analysis platform that performs event detection and classification, after suitable feature selection, in near realtime. The platform is evaluated using seismology data from the Super-Sauze mud-based landslide, which islocated in the southwestern French Alps, and features earthquake, slidequake and tremor type events

Archaeological evidence of a destructive earthquake in Patras, Greece

Oriented collapse of columns, large-scale destruction debris and temporary abandonment of the area deduced from an archaeological excavation provide evidence for a major (intensity IX) earthquake in Patras, Greece. This, and possibly a cluster of other earthquakes, can be derived from archaeological data. These earthquakes are not included in the historical seismicity catalogues, but can be used to put constraints to the seismic risk of this city. Patras was affected by a cluster of poorly documented earthquakes between 1714 and 1806. The city seems to be exposed to risks of progressive reactivation of a major strike-slip fault. A magnitude 6.4 earthquake in 2008 has been related to it. This fault has also been associated with a total of four events in the last 20 years, a situation reminiscent of the seismic hazard at the western edge of the North Anatolian Fault

Interpretations of Reservoir Induced Seismicity may not always be valid : the case of seismicity during the impoundment of the Kremasta dam (Greece, 1965-1966)

The ‘Kremasta seismic sequence’ in western Greece is one of the most commonly cited examples of Reservoir Induced Seismicity (RIS). Here, we show that this ‘sequence’ is a result of normal tectonic activity and that only some small, unrelated microseismic events are reservoir induced. Shortly after the beginning of the impoundment of the Kremasta Dam in 1965, the then newly established seismic monitoring network in Greece recorded two Ms ≥ 6.0 events and numerous small shocks spread over a 120 km wide region. These were interpreted as a single seismic sequence (namely the Kremasta seismic sequence), and assumed to be reservoir induced. We revisit the epicenter locations of these events and interpret them in the framework of the regional tectonic context and the local hydrogeology. Placing these events into the local context shows that they represent an amalgamation of separate, ordinary (tectonic) seismic sequences. Further, the regional rocks are highly fragmented by small faults and the spatial distribution of seismic events is not consistent with a model of stress transfer from reservoir loading. In addition, it is not likely that events at such long (> 20-30 km) distances from the reservoir could be induced by an initial reservoir load head of 30 m. Whilst the larger magnitude events are tectonic, after impoundment local residents reported an unusual frequency of small microseismic events felt only within 10 km of the dam. We provide evidence that these are a result of the collapse of numerous shallow karstic cavities adjacent and beneath the reservoir due to increased water load (locally 100-150 m depth). This study has significant implications for interpretation of seismic triggering mechanisms in other regions: earthquake occurrence within the proximity of reservoirs during and after impoundment time cannot be assumed to be RIS unless supported by seismological, geological and hydrogeological evidence

Unmanned Aerial Vehicle (UAV) based mapping in engineering geological surveys : considerations for optimum results

UAVs have been used in engineering for at least two decades, mainly focusing on structural health monitoring, geological surveys and site inspections, especially at cases where a rapid assessment is required, for example after a natural disaster. While there is a wide range of recognition algorithms for the automatic identification of structural damage, structural geological features etc. from the acquired images, the parameters affecting the resolution of these images are often overlooked. As a result, the UAV technology is not used at its full potential and at times, it is even regarded as leading to poor outcomes. This paper discusses the main parameters affecting the resolution of the images acquired by a UAV. We present a case study of the structural geological mapping of a coastal area carried out using two types of UAVs: a fixed wing and a hexacopter. A comparison between the structural geological maps based on the orthophotos and one produced using conventional techniques shows that the level of detail is the same and the time spent is at least 5 times less when using a UAV. The fixed wing is faster and therefore, can cover large areas while the copter gives better resolution images as it can fly at lower heights. The latter is cost and time effective only if it is used for surveys limited to small areas. The characterization of some structural geological features has not been possible based solely on the orthophotos. We show that in order to achieve the desired accuracy, a ground sample distance of at least half that value is required. We discuss technical aspects, such as the effect of topography and UAV orientation on the overlap value, the camera calibration, number of control points and lighting conditions, that should be taken into account prior to flying a UAV and provide recommendations on how to obtain optimum results, i.e. orthophotos that suit the needs of the project

Near surface full waveform inversion via deep learning for subsurface imaging

In order to meet increasing safety standards and technological requirements for underground construction, the estimation of Earth models is needed to characterize the subsurface. This can be achieved via near-surface or standard Full-Waveform Inversion (FWI) velocity model building, which reconstructs the Earth model parameters (compressional and shear wave velocities, density) via recordings obtained on the field. The wave function characterizing the Earth model parameters is inherently non-linear, rendering this optimization problem complex. With advances in computational power, including graphics processing units (GPUs) computing, data driven approaches to solve FWI via Deep Neural Networks (DNN) are increasing in popularity due to its ability to solve the FWI problem accurately. In this paper, we leverage on DNN-based FWI applied to field data, to demonstrate that instead of depending on observed data collected from multiple boreholes across a large distance, it is possible to obtain accurate Earth model parameters for areas with varied geotechnical characteristics by using geotechnical data as prior knowledge and constraining the training models according to a single borehole to map the large geological earth cross section. Also we propose a methodology to simulate acoustic recordings indirectly from laboratory tests on soil samples obtained from boreholes, which were analysed for compressive strength of intact rock and Geological Strength Index. Layers’ geometry and properties for a section of total 3.0 km are used for simulating 15 2D elastic spaces of 200 m width and 50m depth assuming receivers and Ricker-wavelet sources. We adopt a Fully Convolutional Neural Network for Velocity Model Building, previously shown to work well with synthetic data, to generate the 2D predicted Earth model. The results of this study show that the velocity model can be accurately predicted via DNN through the appropriate training with minimum demands for borehole data. The performance is evaluated through both metrics focused on image quality and on velocity values giving a multifaceted understanding of the model’s true ability to predict the subsurface

Microseismic monitoring illuminates phases of slope failure in soft soils

The role of microseismic monitoring in rock slope stability has been long established: large microseismic events associated with rock failure can be detected by seismometers, even at distances of a few kilometres from the source. This is a favourable characteristic for the monitoring of mountainous areas prone to failure. We show that microseismic monitoring, using short-period arrays and a sufficiently high sampling rate, can also record weak precursory signals, that could represent early phases of a larger scale slope failure in soft soils. We validate this hypothesis with field observations. We find that, even in high attenuation material such as clays, it is possible to record and detect in the frequency domain, soil failures at source-to-receiver distances up to 10 m for crack formation/propagation to more than 43 m for small (less than 2.5 m3) events. Our results show for the first time, an extended frequency range (10 Hz to 380 Hz) where small soil failures can be detected at short monitoring distances, even at sites with high background noise levels. This is the first published study focusing on ground-truthed only, slope failure induced seismic signals in soft soils at field scale and within the seismic frequency range (1–500 Hz). We suggest that microseismic monitoring could complement existing monitoring techniques to characterize the response and structural integrity of earth structures, such as embankments, where the monitoring distances are a few 10s of metres, with the potential to detect any material deterioration at the very early stages. This study does not focus on automatic classification of slope failure signals, however, our observations and methodology could form the basis for the future development of such an approach

Multichannel Coherency Migration grid search (MCMgs) in locating microseismic events by a surface array

Microseismic monitoring has been used in geo-energy related activities, such as shale-gas ex-ploitation, mining, deep geothermal exploitation, geotechnical and structural engineering, for detecting and locating fractures, rock failures, and micro-earthquakes. The success of micro-seismic monitoring depends on reliable detection and location of the recorded microseismic-ity. Multichannel Coherency Migration (MCM) is a detection and location waveform migra-tion based approach which does not require phase picking, identification, and association and performs well on noisy data. Its caveat is a high computational cost, which impedes its ap-plication of MCM on large datasets or for real-time monitoring. To address this issue, we propose an improved approach, the Multichannel Coherency Migration grid search (MCMgs), by introducing an adaptive grid optimization technique. Based on results from synthetic and real data, we show that MCMgs reduces the computation time up to 64 times. In addition, MCMgs generates multiple maximum coherency values with various grid sizes instead of a single (maximum) coherency value that links to a single grid point and size, thus resulting in more accurate locations. Our simulation results on different deployment geometries demon-strate that MCMgs is effective even with a small number of recordings available - a minimum of seven. We conduct a sensitivity analysis to assess how the detectability of events is affected by the spatial arrangement of the deployed monitoring array. If a limited number of seismome-ters are available for deployment, our analysis favors a patch array deployment geometry. We show that twelve seismometers deployed at a patch array geometry can have similar detection and localisation capability as a large rectangular array of more than 100 seismometers but at a much lower computational and deployment cost

Do intraplate and plate boundary fault systems evolve in a similar way with repeated slip events?

As repeated slip events occur on a fault, energy is partly dissipated through rock fracturing and frictional processes in the fault zone and partly radiated to the surface as seismic energy. Numerous field studies have shown that the core of intraplate faults is wider on average with increasing total displacement (and hence slip events). In this study we compile data on the fault core thickness, total displacement and internal structure (e.g., fault core composition, host rock juxtaposition, slip direction, fault type, and/or the number of fault core strands) of plate boundary faults to compare to intraplate faults (within the interior of tectonic plates). Fault core thickness data show that plate boundary faults are anomalously narrow by comparison to intraplate faults and that they remain narrow regardless of how much total displacement they have experienced or the local structure of the fault. By examining the scaling relations between seismic moment, average displacement and surface rupture length for plate boundary and intraplate fault ruptures, we find that for a given value of displacement in an individual earthquake, plate boundary fault earthquakes typically have a greater seismic moment (and hence earthquake magnitude) than intraplate events. We infer that narrow plate boundary faults do not process intact rock as much during seismic events as intraplate faults. Thus, plate boundary faults dissipate less energy than intraplate faults during earthquakes meaning that for a given value of average displacement, more energy is radiated to the surface manifested as higher magnitude earthquakes. By contrast, intraplate faults dissipate more energy and get wider as fault slip increases, generating complex zones of damage in the surrounding rock and propagating through linkage with neighbouring structures. The more complex the fault geometry, the more energy has to be consumed at depth during an earthquake and the less energy reaches the surface

Detection of weak seismic signals in noisy environments from unfiltered, continuous passive seismic recordings

Robust event detection of low signal-to-noise ratio (SNR) events, such as those characterized as induced or triggered seismicity, remains a challenge. The reason is the relatively small magnitude of the events (usually less than 2 or 3 in Richter scale) and the fact that regional permanent seismic networks can only record the strongest events of a microseismic sequence. Monitoring using temporary installed short-period arrays can fill the gap of missed seismicity but the challenge of detecting weak events in long, continuous records is still present. Further, for low SNR recordings, commonly applied detection algorithms generally require pre-filtering of the data based on a priori knowledge of the background noise. Such knowledge is often not available. We present the NpD (Non-parametric Detection) algorithm, an automated algorithm which detects potential events without the requirement for pre-filtering. Events are detected by calculating the energy contained within small individual time segments of a recording and comparing it to the energy contained within a longer surrounding time window. If the excess energy exceeds a given threshold criterion, which is determined dynamically based on the background noise for that window, then an event is detected. For each time window, to characterize background noise the algorithm uses non-parametric statistics to describe the upper bound of the spectral amplitude. Our approach does not require an assumption of normality within the recordings and hence it is applicable to all datasets. We compare our NpD algorithm with the commonly commercially applied STA/LTA algorithm and another highly efficient algorithm based on Power Spectral Density using a challenging microseismic dataset with poor SNR. For event detection, the NpD algorithm significantly outperforms the STA/LTA and PSD algorithms tested, maximizing the number of detected events whilst minimizing the number of false positives

Microseismic events cause significant pH drops in groundwater

Earthquakes cause rock fracturing, opening new flow pathways which can result in the mixing of previously isolated geofluids with differing geochemistries. Here we present the first evidence that seismic events can significantly reduce groundwater pH without the requirement for fluid mixing, solely through the process of dynamic rock fracturing. At the Grimsel Test Site, Switzerland, we observe repeated, short-lived groundwater pH drops of 1-3.5 units, while major and minor ion groundwater concentrations remain constant. Acidification coincides with reservoir drainage and induced microseismic events. In laboratory experiments, we demonstrate that fresh rock surfaces made by particle cracking interact with the in situ water molecules, likely through creation of surface silanols and silica radicals, increasing the H+ concentration and significantly lowering groundwater pH. Our findings are significant; pH exerts a fundamental control on the rate and outcome of most aqueous geochemical reactions and microseismic events are commonplace, even in seismically inactive regions