A seismic wavefield reconstruction framework based on compressed sensing using the data-driven reduced-order model (ROM) is proposed and its characteristics are investigated through numerical experiments. The data-driven ROM is generated from the dataset of the wavefield using the singular value decomposition. The spatially continuous seismic wavefield is reconstructed from the sparse and discrete observation and the data-driven ROM. The observation sites used for reconstruction are effectively selected by the sensor optimization method for linear inverse problems based on a greedy algorithm. The proposed framework was applied to simulation data of theoretical waveform with the subsurface structure of the horizontally-stratified three layers. The validity of the proposed method was confirmed by the reconstruction based on the noise-free observation. Since the ROM of the wavefield is used as prior information, the reconstruction error is reduced to an approximately lower error bound of the present framework, even though the number of sensors used for reconstruction is limited and randomly selected. In addition, the reconstruction error obtained by the proposed framework is much smaller than that obtained by the Gaussian process regression. For the numerical experiment with noise-contaminated observation, the reconstructed wavefield is degraded due to the observation noise, but the reconstruction error obtained by the present framework with all available observation sites is close to a lower error bound, even though the reconstructed wavefield using the Gaussian process regression is fully collapsed. Although the reconstruction error is larger than that obtained using all observation sites, the number of observation sites used for reconstruction can be reduced while minimizing the deterioration and scatter of the reconstructed data by combining it with the sensor optimization method.
The ``big'' seismic data not only acquired by seismometers but also acquired by vibrometers installed in buildings and infrastructure and accelerometers installed in smartphones will be certainly utilized for seismic research in the near future. Since it is impractical to utilize all the seismic big data in terms of the computational cost, methods which can select observation sites depending on the purpose are indispensable. We propose an observation site selection method for the accurate reconstruction of the seismic wavefield by process-driven approaches. The proposed method selects observation sites suitable for accurately estimating physical model parameters such as subsurface structures and source information to be input into a numerical simulation of the seismic wavefield. The seismic wavefield is reconstructed by the numerical simulation using the parameters estimated based on the observed signals at only observation sites selected by the proposed method. The observation site selection in the proposed method is based on the sensitivity of each observation site candidate to the physical model parameters; the matrix corresponding to the sensitivity is constructed by approximately calculating the derivatives based on the simulations, and then, observation sites are selected by evaluating the quantity of the sensitivity matrix based on the D-optimality criterion proposed in the optimal design of experiments. In the present study, physical knowledge on the sensitivity to the parameters such as seismic velocity, layer thickness, and hypocenter location was obtained by investigating the characteristics of the sensitivity matrix. Furthermore, the effectiveness of the proposed method was shown by verifying the accuracy of seismic wavefield reconstruction using the observation sites selected by the proposed method.
The present study proposed the framework of the spatiotemporal superresolution measurement based on the sparse regression with dimensionality reduction using the proper orthogonal decomposition (POD). The non-time-resolved particle image velocimetry (PIV) and the time-resolved near-field acoustic measurements using microphones were simultaneously performed for a Mach 1.35 supersonic jet. POD is applied to PIV and microphone data matrices and the sparse linear regression model of the reduced-order data is calculated using the least absolute shrinkage and selection operator regression. The effects of the hyperparameters of the superresolution measurement were quantitatively evaluated through randomized cross-validation. The superresolved velocity field indicated the smooth convection of the velocity fluctuations associated with the screech tone, while the convection of the large-scale structures at the downstream side was not observed. The proposed framework can reconstruct the unsteady fluctuation with multiple frequency phenomena, although the reconstruction is limited to the phenomena that are associated with the microphone output.
This study proposes a method for predicting the wind direction against the simple automobile model (Ahmed model) and the surface pressure distributions on it by using data-driven optimized sparse pressure sensors. Positions of sparse pressure sensor pairs on the Ahmed model were selected for estimation of the yaw angle and reconstruction of pressure distributions based on the time-averaged surface pressure distributions database of various yaw angles, whereas the symmetric sensors in the left and right sides of the model were assumed. The surface pressure distributions were obtained by pressure-sensitive paint measurements. Three algorithms for sparse sensor selection based on the greedy algorithm were applied, and the sensor positions were optimized. The sensor positions and estimation accuracy of yaw angle and pressure distributions of three algorithms were compared and evaluated. The results show that a few optimized sensors can accurately predict the yaw angle and the pressure distributions.
The effects of oxygen mole fraction on the static properties of pressure-sensitive paint (PSP) were investigated. Sample coupon tests using a calibration chamber were conducted for polymer-based PSP (PHFIPM-PSP), polymer/ceramic PSP (PC-PSP), and anodized-aluminium PSP (AA-PSP). The oxygen mole fraction was set to be between 0.1-100% and the ambient pressure was set to be between 0.5-140 kPa. The localized Stern-Volmer coefficient $B_{\rm local}$ once increases and then decreases as the oxygen mole fraction increases. The value of $B_{\rm local}$ depends on both ambient pressure and oxygen mole fraction, but the effect of this parameter can be characterized as a function of the partial pressure of oxygen. The value of $B_{\rm local}$ of AA-PSP and PHFIPM-PSP, which are low-pressure type and relatively low-pressure type PSP, have a peak at the relatively low partial pressure of oxygen, and $B_{\rm local}$ of PC-PSP, which are atmospheric pressure type PSP, has a peak at the relatively high partial pressure of oxygen. The peak of the intensity change with respect to pressure fluctuation proportional to the ambient pressure $S_{\mathcal{PR}}$ appears at the lower partial pressure of oxygen than that of $B_{\rm local}$. This is because the intensity of PSP becomes quite low at the high partial pressure of oxygen even if $B_{\rm local}$ is higher. Hence, an optimal partial oxygen mole fraction exists depending on the type of PSP and ambient pressure range of the experiment, and its optimal value can be found based on the partial pressure of oxygen.
The multilayer thin film structure of the superconductor has been proposed by A. Gurevich to enhance the maximum gradient of SRF cavities. The lower critical field Hc1 at which the vortex starts penetrating the superconducting material will be improved by coating Nb with thin film superconductor such as NbN. It is expected that the enhancement of Hc1 depends on the thickness of each layer. In order to determine the optimum thickness of each layer and to compare the measurement results with the theoretical prediction proposed by T. Kubo, we developed the Hc1 measurement system using the third harmonic response of the applied AC magnetic field at KEK. For the Hc1 measurement without the influence of the edge or the shape effects, the AC magnetic field can be applied locally by the solenoid coil of 5mm diameter in our measurement system. ULVAC made the NbN-SiO2 multilayer thin film samples of various NbN thicknesses. In this report, the measurement result of the bulk Nb sample and NbN-SiO2 multilayer thin film samples of different thickness of NbN layer will be discussed.
In recent years, it has been pointed out that the maximum accelerating gradient of a superconducting RF cavity can be increased by coating the inner surface of the cavity with a multilayer thin-film structure consisting of alternating insulating and superconducting layers. In this structure, the principal parameter that limits the performance of the cavity is the critical magnetic field or effective $H_{C1}$ at which vortices begin penetrating into the superconductor layer. This is predicted to depend on the combination of the film thickness. We made samples that have a NbN/SiO$_2$ thin-film structure on a pure Nb substrate with several layers of NbN film deposited using DC magnetron sputtering method. Here, we report the measurement results of effective $H_{C1}$ of NbN/SiO$_2$(30 nm)/Nb multilayer samples with thicknesses of NbN layers in the range from 50 nm to 800 nm by using the third-harmonic voltage method. Experimental results show that an optimum thickness exists, which increases the effective $H_{C1}$ by 23.8 %.
The photoion spectrum of atomic potassium was measured over the 3s -> np excitation region with the photoion time-of-flight method and monochromatized synchrotron radiation. An unusual spectrum with paired windows structure was found instead of a simple regular Rydberg series. Such subsidiary windows have not been observed in the 3s -> np resonances of Ar, which has a closed outer shell. Based on Dirac-Fock calculations, the dual window structure at 36.7 eV and at 37.4 eV was assigned to the 3s^-13p^64s4p resonance. The line shape can be fitted by Fano's formula and the Fano parameters were obtained.
Braneshop, and
the US National Science Foundation.
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