Oscillations and Mass Draining that Lead to a Sympathetic Eruption of a Quiescent Filament

In this paper, we present a multiwavelength analysis to mass draining and oscillations in a large quiescent filament prior to its successful eruption on 2015 April 28. The eruption of a smaller filament that was parallel and in close, ∼350″ proximity was observed to induce longitudinal oscillations and enhance mass draining within the filament of interest. The longitudinal oscillation with an amplitude of ∼25 Mm and ∼23 km s−1 underwent no damping during its observable cycle. Subsequently the slightly enhanced draining may have excited a eruption behind the limb, leading to a feedback that further enhanced the draining and induced simultaneous oscillations within the filament of interest. We find significant damping for these simultaneous oscillations, where the transverse oscillations proceeded with the amplitudes of ∼15 Mm and ∼14 km s−1, while the longitudinal oscillations involved a larger displacement and velocity amplitude (∼57 Mm, ∼43 km s−1). The second grouping of oscillations lasted for ∼2 cycles and had a similar period of ∼2 hr. From this, the curvature radius and transverse magnetic field strength of the magnetic dips supporting the filaments can be estimated to be ∼355 Mm and ≥34 G. The mass draining within the filament of interest lasted for ∼14 hr. The apparent velocity grew from ∼35 to ∼85 km s−1, with the transition being coincident with the occurrence of the oscillations. We conclude that two filament eruptions are sympathetic, i.e., the eruption of the quiescent filament was triggered by the eruption of the nearby smaller filament.

Assessing the optimal time interval between growth measurements using a combined data set of weights and heights from 5948 infants

BackgroundCurrent guidance on the optimum interval between measurements in infancy is not evidence based. We used routine data to explore how measurement error and short-term variation (‘noise’) might affect interpretation of infant weight and length gain (‘signal’) over different time intervals.MethodUsing a database of weights and lengths from 5948 infants aged 0–12 months, all pairs of measurements per child 2, 4 and 8 weeks apart were extracted. Separately, 20 babies aged 2–10 months were weighed on six occasions over 3 days to estimate the SD of the weight difference between adjacent measurements (=116 g). Values of 116 g and 0.5 cm for ‘noise’ were then used to model its impact on (a) the estimated velocity centile and (b) the chance of seeing no growth during the interval, in individuals.ResultsThe average gain in weight and length was much larger than the corresponding SD over 8-week and 4-week time intervals, but not over 2 weeks. Noise tended to make apparent velocity less extreme; after age 6 months, a 2-week velocity that appeared to be on to the ninth centile, would truly be on the second–third centile if measured with no noise. For 2-week intervals, there was a 16% risk of no apparent growth by age 10 months.ConclusionsGrowth in infancy is so rapid that the change in measurements 4–8 weeks apart is unlikely ever to be obscured by noise, but after age 6 months, measurements 2 weeks or less apart should be treated with caution when assessing growth faltering.

Accurate Realtime Motion Estimation Using Optical Flow on an Embedded System

Motion estimation has become one of the most important techniques used in realtime computer vision application. There are several algorithms to estimate object motions. One of the most widespread techniques consists of calculating the apparent velocity field observed between two successive images of the same scene, known as the optical flow. However, the high accuracy of dense optical flow estimation is costly in run time. In this context, we designed an accurate motion estimation system based on the calculation of the optical flow of a moving object using the Lucas–Kanade algorithm. Our approach was applied on a local treatment region implemented into Raspberry Pi 4, with several improvements. The efficiency of our accurate realtime implementation was demonstrated by the experimental results, showing better performance than with the conventional calculation.

Detection of acoustic wave generated by Baganuur mining explosion

We analyzed all available recorded infrasound data and determined the station detection capability in Mongolia. While in the winter times, we continuously detect acoustic signals from blasts at the Baganuur open-pit mine (150 km from infrasound I34MN station), which we rarely registered during the summer times. A previous study has shown that the noise level increased by 5 dB - 10 dB in the summer, which affected the detection capability of the stations. On the other hand, this could be connected to infrasound propagation model difference between winter and summer times. To verify this phenomenon, we installed a new infrasound IBHM station in a forest area at a distance of 350 km from the Baganuur mine area. When we compared the background noise level of the two stations, the noise level at the temporary station was lower than the permanent one, which allowed us to compare winter and summer time registered infra wave characteristics. With the comparison observed and modeled, apparent velocity suggested that winter and summer time detection difference could be due to the propagation model of infrasound wave itself.

Ultrasonic monitoring of stress and cracks of the 1/3 scale mock-up of nuclear reactor concrete containment structure

To evaluate the stress level and damage of a reinforced concrete containment wall (similar to those used in nuclear power plants) and its reaction to pressure variations, we conducted successive ultrasonic experiments on the exterior surface of the containment wall in the gusset area for three consecutive years (2017, 2018 and 2019). During each experiment, the pressure inside the containment wall increased gradually from 0 MPa to 0.43 MPa and then decreased back to 0 MPa. From the analysis of the ultrasonic coda waves obtained in the multiple scattering regime (80–220 kHz), we performed Coda Wave Interferometry to calculate the apparent velocity changes in the structure (denoted by dV/ V a) and Coda Wave Decorrelation (DC) measurements to produce 3D cartographies of stress and crack distribution. From three source–receiver pairs, located at the top, middle and bottom of the experimental region, we observe that coda waves dilate, shrink and remain almost unchanged, respectively. This corresponds to the decreasing, increasing and invariant pressure inside the concrete. The comparison of 3 years’ results demonstrates that the variation of dV/ V a and DC under the same pressure test increases through the years, which indicates the progressive deterioration and ageing of the concrete. From a large collection of source–receiver pairs at different times, the spatial–temporal variations of dV/ V a and DC are then used to produce a map of the structural velocity and scattering changes, respectively. We observe a decreasing velocity on the top part and an increasing in the middle one, which is in line with the dV/ V a analysis. The reconstructed scattering changes (or structural changes) highlight the active region during the inflation–deflation procedure, corresponding to the opening and closing (and sometimes the development) of cracks. The larger magnitude in 2019 than in 2017 indicates the increasing damage in the concrete.

Identification of and Favorable Conditions for a New Long-Offset Seismic Phase from Offshore–Onshore Seismic Surveys

Offshore–onshore seismic survey is one of the main methods to study crustal structures in offshore–onshore transitional zones. At present, the seismic waves commonly used in imaging are the crustal refraction (Pg), the crustal reflection from the Moho (PmP), and the upper-mantle refraction (Pn) waves. The propagation distances of Pg and PmP are commonly less than 210 km, and Pn propagates with an apparent velocity of ∼8 km/s. In 2015, two offshore–onshore wide-angle seismic lines with a length of ∼350 km were acquired in the Pearl River Estuary. In addition to Pg, PmP, and Pn, a new seismic phase was observed, which has a long propagation distance (offset of ∼170–290 km), low apparent velocity (∼5.85 km/s), and low frequency (∼4–7 Hz). Similar seismic phases have been widely reported in previous offshore–onshore and reservoir seismic surveys, but the understanding of these phases is still limited. Herein, we used both raytracing and waveform modeling methods to identify the new seismic phase as the secondary Pg phase, which reflects from the surface (named Pg2Pg). We also discuss favorable conditions for Pg2Pg, including (1) a thin sedimentary layer with low velocity at the surface in which the reflection of Pg occurs, which can reduce the incidence angles and hence increase the energy of the reflected waves; (2) a sedimentary basement dipping toward the sea at the positions of the air gun shots, which focuses seismic waves; (3) relatively smooth interfaces of the medium, which can reduce the scattering of Pg2Pg; and (4) air guns that can excite low-frequency signals, which can reduce the attenuation of seismic waves. Checkerboard tests and practical applications show that Pg2Pg can significantly improve upper-crustal resolution, especially for onshore areas. Our research promotes the data mining of offshore–onshore seismic surveys, which is important for obtaining more detailed crustal structures.

Multi-array analysis of volcano-seismic signals at Fogo and Brava, Cape Verde

Seismic arrays provide tools for the localization of events without clear phases or events outside of the network, where the station coverage prohibits classical localization techniques. Beamforming allows the determination of the direction (backazimuth) and the horizontal (apparent) velocity of an incoming wavefront. Here we combine multiple arrays to retrieve event epicenters from the area of intersecting beams without the need to specify a velocity model. The analysis is performed in the time-domain, which allows to select a relatively narrow time window around the phase of interest while preserving frequency bandwidth. This technique is applied to earthquakes and hybrid events in the region of Fogo and Brava, two islands of the southern chain of the Cape Verde archipelago. The results show that the earthquakes mainly originate near Brava whereas the hybrid events are located on Fogo. By multiple-event beam-stacking we are able to further constrain the locations of the hybrid events in the north-western part of the collapse scar of Fogo. In previous studies, these events were attributed to shallow hydrothermal processes. However, we obtain relatively high apparent velocities at the arrays, pointing to either deeper sources or to complex ray paths. For a better understanding of possible errors of the multi-array analysis, we also compare slowness values obtained from the array analysis with those derived from earthquake locations from classical (local network) localizations. In general, the results agree well, however, the arrays also show some aberrations that can be quantified for certain event locations.

Distributed acoustic sensing as a tool for exploration and monitoring: a proof-of-concept

We use PoroTOMO experimental data to compare the performance of Distributed Acoustic Sensing (DAS) and geophone data in executing standard exploration and monitoring activities. The PoroTOMO experiment consists of two "seismic systems": (a) a 8.6 km long optical fibre cable deployed across the Brady geothermal field and covering an area of 1.5 x 0.5 km with 100 m long segments, and (b) an array of 238 co-located geophones with an average spacing of 60 m. The PoroTOMO experiment recorded continuous seismic data between March 10th and March 25th 2016. During such period, a ML 4.3 regional event occurred in the southwest, about 150 km away from the geothermal field, together with several microseismic local events related to the geothermal activity. The seismic waves generated from such seismic events have been used as input data in this study. For the exploration tasks, we compare the propagation of the ML 4.3 event across the geothermal field in both seismic systems in term of relative time-delay, for a number of configurations and segments. Defined the propagation, we analyse and compare the amplitude and the signal-to-noise ratio (SNR) of the P-wave in the two systems at high resolution. For testing the potential in monitoring local seismicity, we first perform an analysis of the geophone data for locating a microseismic event, based on expert opinion. Then, we a adopt different workflow for the automatic location of the same microseismic event using DAS data. For testing the potential in monitoring distant event, data from the regional earthquake are used for retrieving both the propagation direction and apparent velocity of the wavefield, using a standard plane-wave-fitting approach. Our results indicate that: (1) at a local scale, the seismic P-waves propagation and their characteristics (i.e. SNR and amplitude) along a single cable segment are robustly consistent with recordings from co-located geophones (delay-times δt ∼ 0.3 over 400 m for both seismic systems) ; (2) the interpretation of seismic wave propagation across multiple separated segments is less clear, due to the heavy contamination of scattering sources and local velocity heterogeneities; nonetheless, results from the plane-wave fitting still indicate the possibility for a consistent detection and location of the event; (3) at high-resolution (10 m), large amplitude variations along the fibre cable seem to robustly correlate with near surface geology; (4) automatic monitoring of microseismicity can be performed with DAS recordings with results comparable to manual analysis of geophone recordings (i.e. maximum horizontal error on event location around 70 m for both geophones and DAS data) ; and (5) DAS data pre-conditioning (e.g., temporal sub-sampling and channel-stacking) and dedicated processing techniques are strictly necessary for making any real-time monitoring procedure feasible and trustable.

Application of artificial neural network to estimate bedload transport rates and bedload granulometry using outputs of stationary ADCP measurements

<p>Measuring and assessing the bedload data is a crucial for successful and efficient river management. Hence, the information about the bedload transport and characteristics helps to describe the dynamics of the river morphology and to evaluate the impacts on boat navigation, hydropower production, ecological systems and aquatic habitat.</p><p>Although the acoustic Doppler current profilers are designed to measure water velocities and discharges, they have been successfully used to measure some bedload characteristics, such as the apparent bedload velocity. The correlation between the apparent bedload velocity and the bedload transport rates measured by physical bedload samplers (e.g. pressure difference) has been examined and relatively high correlations have been reported. Moreover, laboratory experiments have proven that there is a strong correlation between the bedload concentration and particle size distribution and corrected backscattering strength obtained from the ADCPs.</p><p>The bedload transport rates yielded from the ADCPs outputs are usually derived as regression model-fitting of the measured apparent velocity and the physically collected bedload samples at the same time and position.  Alternatively, a semi-empirical kinematical approach is used, where the apparent bedload velocity is the main component and the bedload concentration is empirically estimated. However, the heterogeneous and sporadic motion of the bedload particles is often followed by high uncertainty and weak performance of these approaches.</p><p>Machine learning offers a relatively simple and robust method that has the potential to describe the nonlinearity of the complex bedload motion and so far, it has not been previously exploited for predicting transport rates. This study implements artificial neural network techniques to develop a model for predicting bedload transport rates by using only ADCP data outputs as training data. Data processing techniques are used to extract relevant features from the corrected backscattering strength and apparent velocity obtained from the ADCPs. More than 60 features were derived in the ADCPs dataset, and the most relevant features are selected through neighborhood component analysis. These features are used as inputs in conventional supervised neural network architecture which consists of two hidden layers and 35 neurons. This model is used to capture the distribution of the ADCP features for each output (e.g., physically measured transport rates and grain size from bedload samples) in the training sample. The back-propagation algorithm (BPA) is still one of the most widely used learning algorithms in the training process and thus herein applied. The learning rate, number of neurons and hidden layers were optimized by using Bayesian optimization techniques. The network was trained with more than 60 bedload samples and corresponding 5 - 10 min time series of ADCP preprocessed data. The rest of the samples were used for validation of the model. The validation resulted in correlation coefficients higher than 0.9 and the, which is significantly higher value than the corresponding values for the methodologies developed before. Aiming to develop a more robust and stable ANN model, further testing of different training algorithms must be performed, different ANN architecture should be tested, and more data shall be included.</p>

Updates on ambient noise correlation for landslide monitoring

<p>Monitoring landslides is essential to understand their dynamics and to reduce the risk of human losses by raising warnings before a failure. A decade ago, a decrease of apparent seismic velocity was detected several days before the failure of a clayey landslide, that was monitored with the ambient noise correlation method. It revealed its potential to detect precursor signals before a landslide failure, which could improve early warning systems. To date, nine landslides have been monitored with this method, and its ability to reveal precursors before failure seems confirmed on clayey landslides. However three challenges remain for operational early-warning applications: to detect velocity changes both rapidly and with confidence, to account for seasonal and daily environmental influences, and to check for potential instabilities in measurements. The ability to detect a precursory velocity change requires to adapt the processing workflow to each landslide: the key factors are the filtering frequency, the correlation time window, and the choice of temporal resolution. The velocity also fluctuates seasonally, by 1 to 6% on the reviewed landslide studies, due to environmental influences, with a linear trend between the amplitude of seasonal fluctuations and the filtering frequency over the 0.1–20 Hz range, encompassing both landslide and non-landslide studies. The environmental velocity fluctuations are caused mostly by groundwater levels and soil freezing/thawing, but could also be affected by snow height, air temperature and tide depending on the site. Daily fluctuations should also occur on landslides, and can be an issue when seeking to obtain a sub-daily resolution useful for early-warning systems. Finally, spurious fluctuations of apparent velocity—unrelated to the material dynamics—should be verified for. They can be caused by changes in noise sources (location or spectral content), in site response (change of scatterers, attenuation, or resonance frequency due to geometrical factors), or in inter-sensor distance. As a perspective, the observation of seismic velocity changes could contribute in assessing a landslide stability across time, both during the different creeping stages occurring before a potential failure, and during its reconsolidation after a failure.</p><p>----</p><p>Main references :</p><ul><li>Le Breton M., Bontemps N., Guillemont A., Baillet L., Larose E., 2021. Landslide Monitoring Using Seismic Ambient Noise Correlation: Challenges and Applications, Earth Science Reviews, In press</li> <li>Larose, E., Carrière, S., Voisin, C., Bottelin, P., Baillet, L., Guéguen, P., Walter, F., Jongmans, D., Guillier, B., Garambois, S., Gimbert, F., Massey, C., 2015. Environmental seismology: What can we learn on earth surface processes with ambient noise? Journal of Applied Geophysics 116, 62–74. https://doi.org/10.1016/j.jappgeo.2015.02.001</li> <li>Mainsant, G., Larose, E., Brönnimann, C., Jongmans, D., Michoud, C., Jaboyedoff, M., 2012. Ambient seismic noise monitoring of a clay landslide: Toward failure prediction. J. Geophys. Res. 117, F01030. https://doi.org/10.1029/2011JF002159</li> </ul>