Two-dimensional structures of the ionospheric variations generated by the acoustic resonance between the ground surface and the lower thermosphere was observed for the first time near the epicenter after the M 9.0 Tohoku earthquake on March 11, 2011. A short-period oscillation of total electron content was observed by a GPS receiver array after the earthquake for four hours in the vicinity of the epicenter. It was centered in the east of the epicenter where the tsunami was estimated to commence. The frequency of the dominant mode of the oscillation was 4.5 mHz, 222 seconds of period, while there were minor oscillations whose frequency were 3.7 mHz and 5.3 mHz. These periods are consistent with the periods of the acoustic resonance between the ground surface and the lower thermosphere, predicted by a numerical model. The amplitude of the TEC oscillation showed a gradual change of the amplitude. The two-dimensional distributions of TEC variations generated by this resonance had wave frontal structures that extended from northwest to southeast. The resonant oscillation of the TEC was accompanied by a depletion of TEC whose duration was about 60 minutes. The area of this depletion also centered on the epicenter.

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: Modal properties are recognized as indicators reflecting structural condition in structural health monitoring (SHM). However, changing environmental and operational variables (EOVs) cause variability in the identified modal parameters and subsequently obscure damage effects. To address the issue caused by the EOVs-related variability, this study investigated the variability of modal frequencies in long-term SHM of a steel plate-girder bridge. A Bayesian fast Fourier transform (FFT) method was used for operational modal analysis in a probabilistic viewpoint. Bayesian linear regression (BLR) and Gaussian process regression (GPR) models were utilized to capture the variability in the identified most probable values (MPVs) of modal frequencies as temperature-driven models, and the limitation of these models for data normalization with latent EOVs was discussed. To overcome the interference of latent EOVs indirectly, a long short-term memory (LSTM) network was established to trace the variability as an autocorrelated process, with a traditional seasonal autoregressive integrated moving average (SARIMA) model as a benchmark. Finally, an anomaly detection method based on residuals of one-step ahead predictions by LSTM was proposed associating with the Mann-Whitney U test.