High resolution bolt pre-load looseness monitoring using coda wave interferometry

This paper proposes a new concept, named the Detectable Resolution of Bolt Pre-load (DRBP), by using the coda wave interferometry (CWI) to quantitatively measure the pre-load looseness at a high resolution. Due to its characteristics of roughness, irregularity, and randomly distributed asperities, the contact surface of the bolted components can function as a natural interferometer to scatter the propagation waves. The multiply-scattered coda waves can amplify the slight changes in the travel path and show the visible perturbation in the time domain. By calculating the time-shifted correlation coefficient of coda waves before and after the slight pre-load looseness, the tiny pre-load changes can be clearly revealed. To evaluate the feasibility of the proposed method, a theoretical model considering the time shifts of coda waves and the variations of pre-load is established. Based on the acoustoelastic effect and the wave path summation theory of coda wave interferometry, the model shows that the time shifts of coda waves change linearly with the variations of pre-load. Verification experiments are conducted, and the results show that the R-square values of the fitting curves are larger than 0.9216. In addition, the proposed approach has the feature of high resolution. The ultimate pre-load resolution of the proposed approach is 0.331%, that is, when the variation of pre-load is larger than 0.331%, it can be detected. Therefore, theoretical analysis and experimental results prove that the CWI-based pre-load detection approach holds great potential for the detection of bolt pre-load looseness, especially during the initial stage.

Towards Data-Driven Evaluation of Site Effects From Coda of Strong Motion Timeseries Using Recurrent Neural Networks

It is known that coda of strong motion records are products of numerous scatterings of body and surface waves within the subsurface soil structure. Several studies have successfully simulated coda wave envelopes by modelling the energy decay. However, due to limitations of quantifying soil heterogeneities, deterministic simulation of scattered wavefields is much more challenging. Therefore, the reverse problem of estimating non statistical properties of subsurface structure from coda waves remains a theoretical potential. On the other hand, machine learning techniques have proven useful in dealing with problems of similar nature, where a theoretical solution is imaginable yet hard to achieve due to a great number of unknown variables. This study utilizes artificial neural networks to propose a new approach of evaluating site effects from coda waves, with the future prospect of obtaining the similar results from microtremor records. A Long Short-Term Memory recurrent neural network is designed using Tensorflow 2 library in Python language. The study utilizes a strong motion dataset consisting of about 60000 3-component records as well as borehole data at 464 stations of Kiban-Kyoshin Network across Japan. The prediction input is coda wave timeseries of strong motion records, defined based on a parametric energy criterion, and all 3 seismograph components EW, NS and UD are used as parallel sequential features. In the first step, the prediction target is a vector of 3 site effect proxies namely, time-averaged shear-wave velocities for the upper 30-m depth Vs30 and the upper 10-m depth Vs10 and predominant frequency f0. In this step, different model parameter combinations are tested to ensure the basic model’s ability in extracting site-specific information from the input coda waves. One of the combinations is then used in the second step, in which the prediction target is surface to downhole ratio of Fourier Amplitude Spectra. For each of the 3 components EW, NS and UD, 100 identical networks are trained to each predict the desired ratio at a certain target frequency. Accuracy of test sample predictions confirms applicability of the proposed approach as well as its potential for future works on microtremor timeseries instead of coda waves.

Scattering Attenuation of the Martian Interior through Coda-Wave Analysis

ABSTRACT We investigate the scattering attenuation characteristics of the Martian crust and uppermost mantle to understand the structure of the Martian interior. We examine the energy decay of the spectral envelopes for 21 high-quality Martian seismic events from sols 128 to 500 of InSight operations. We use the model of Dainty, Toksöz, et al. (1974) to approximate the behavior of energy envelopes resulting from scattered wave propagation through a single diffusive layer over an elastic half-space. Using a grid search, we mapped the layer parameters that fit the observed InSight data envelopes. The single diffusive layer model provided better fits to the observed energy envelopes for high-frequency (HF) and very-high-frequency (VF) than for the low-frequency and broadband events. This result is consistent with the suggested source depths (Giardini et al., 2020) for these families of events and their expected interaction with a shallow scattering layer. The shapes of the observed data envelopes do not show a consistent pattern with event distance, suggesting that the diffusivity and scattering layer thickness is nonuniform in the vicinity of InSight at Mars. Given the consistency in the envelope shapes between HF and VF events across epicentral distances and the trade-offs between the parameters that control scattering, the dimensions of the scattering layer remain unconstrained but require that scattering strength decreases with depth and that the rate of decay in scattering strength is the fastest near the surface. This is generally consistent with the processes that would form scattering structures in planetary lithospheres.

Hanging glacier monitoring with icequake repeaters and seismic coda wave interferometry: a case study of the Eiger hanging glacier

Driven by the force of gravity, hanging glacier instabilities can lead to catastrophic rupture events. Reliable forecasting remains a challenge as englacial damage leading to large-scale failure is masked from modern sensing technology focusing on the ice surface. The Eiger hanging glacier, located in the Swiss Alps, was intensely monitored between April and August 2016 before a moderate 15,000 m3 break-off event from the ice cliff. Among different instruments, such as an automatic camera and interferometric radar, four 3-component seismometers were installed on the glacier. A single seismometer operated throughout the whole monitoring period. It recorded over 200,000 repeating icequakes showing strong englacial seismic coda waves. We propose a novel approach for hanging glacier monitoring by combining repeating icequake analysis, coda wave interferometry, and attenuation measurements. Our results show a seasonal 0.1 % decrease in relative englacial seismic velocity dv/v and an increase in coda wave attenuation Qc−1 (Qc decreases from ~50 to ~30). Comparison of dv/v and Qc with air temperature suggests that these changes are driven by a seasonal increase in the glacier’s ice and firn pack temperature that might affect the top 20 m of the glacier. Diurnal cycles of Qc−1, repeating icequake activity, and the velocity of the glacier front shift from cosinusoidal to sinusoidal variations under the presence of meltwater. The proposed approach extends the monitoring of the hanging glacier beyond the ice surface and allows for a better understanding of the glacier’s response to time-dependent external forcing, which is an important step towards improved break-off forecasting systems.

Damage Detection at a Reinforced Concrete Specimen with Coda Wave Interferometry

Reinforced concrete is a widely used construction material in the building industry. With the increasing age of structures and higher loads there is an immense demand for structural health monitoring of built infrastructure. Coda wave interferometry is a possible candidate for damage detection in concrete whose applicability is demonstrated in this study. The technology is based on a correlation evaluation of two ultrasonic signals. In this study, two ways of processing the correlation data for damage detection are compared. The coda wave measurement data are obtained from a four-point bending test at a reinforced concrete specimen that is also instrumented with fibre optic strain measurements. The used ultrasonic signals have a central frequency of 60 kHz which is a significant difference to previous studies. The experiment shows that the coda wave interferometry has a high sensitivity for developing cracks and by solving an inverse problem even multiple cracks can be distinguished. A further specialty of this study is the use of finite elements for solving a diffusion problem which is needed to state the previously mentioned inverse problem for damage localization.

Coda Waves for Health Monitoring of Composites Under Low-Velocity Impact

Coda waves have been shown to be sensitive to lab-controlled defects such as very small holes in fibrous composite material. In the real world, damages are subtler and more irregular. The main objective of this work is to investigate coda wave capability to detect low-velocity impact damages. The emphasis is to detect the presence of barely visible impact damages using ultrasonic waves. Detection of incipient damage state is important as it will grow over the life of the structure. Differential features, previously used in similar work, have been utilized to detect realistic impact damages on carbon fiber composites. Quasi-isotropic composite laminates were subjected to low-velocity impact energy ranging from 2J to 4.5J. Two differential features reported could be used detect the presence of damage. It is also observed that ply orientation can be a deterministic factor for indicating damages. The size and shape of the impact damage has been characterized using ultrasonic C-scans. Results indicate that coda waves can be used for the detection of damage due to low-velocity impact.