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This paper discusses the relationship between the vertical ground motion and ionospheric disturbances before the Kumamoto earthquake on 16 April 2016, in Kyushu, Japan, using the vertical ground motion measured by slant gauges widely distributed in Kyushu, and the NmF2 observed by ionosondes in Japan and another region. We provide evidence that vertical ground motion excites internal gravity waves (IGWs) that disturb changes in the ionospheric plasma density. From the spectral analysis results of the vertical ground motion data, the summation of various period (frequency) components analyzed from the original data of the slant gauge shows a possible correlation with the change of NmF2 before the earthquake. On the other hand, the influence of the geomagnetic disturbance on vertical ground motion seems to exist. However, we cannot confirm that vertical ground motion is influenced by the geomagnetic disturbance (Kp index) and that the earthquake is triggered by the geomagnetic disturbance. There are two conditions for the vertical ground motion to disturb variations in the ionospheric plasma density: (1) The effective vertical ground motion period should be shorter than 5 h. In addition, (2) vertical ground motion should continue to exist so that wave energy can be continuously injected into the atmosphere. A possible mechanism with which to modify the ionosphere is discussed. The results of this study can provide a basis for the future ionospheric precursors of earthquakes by using the vertical ground motion. [ABSTRACT FROM AUTHOR]

The eruption of the Hunga Tonga–Hunga Ha'apai volcano on 15 January 2022 was the first powerful explosive eruption in history to be recorded with high quality by a wide range of geophysical equipment. The atmospheric Lamb wave caused by the explosion repeatedly circled the Earth and served as one of the reasons for the formation of tsunami waves. In this paper, the Lamb wave manifestations are analyzed in the recordings of tsunamimeters, i.e., in data from DONET and DART pressure sensors located in the area of the Japanese Islands. The work is aimed at studying the physics of the formation of pressure variations at the ocean floor in order to develop a method for isolating free gravity waves in records obtained by bottom pressure sensors. Within the framework of shallow water theory, an analysis of the response of the water layer to the atmospheric Lamb wave was performed. This response combines a forced perturbation, the amplitude of which depends on the depth of the ocean, and free gravity waves arising as a result of the restructuring of the forced perturbation on the submarine slopes. Analytical formulas are given for the amplitude and energy of the forced perturbation and free waves arising at the depth jump. With the aid of numerical simulation, the finite length of a slope was revealed to significantly affect the parameters of free waves when exceeding 50 km. The analysis of in situ data (DONET, DART) confirms the validity of theoretical concepts presented in the work. In particular, it is shown that variations of bottom pressure in the deep ocean exceed the amplitude of atmospheric pressure fluctuations in the Lamb wave. [ABSTRACT FROM AUTHOR]

Spotlight synthetic aperture radar (SAR) achieves a high azimuth resolution with long integration times. Meanwhile, the long integration times also cause defocused and smeared images of moving objects such as cruising ships This is a typical imaging mechanism for moving objects in Spotlight SAR images. Conversely, ships can be classified as stationary or moving from the amount of smearing, and this classification method is, in general, based on manual observation. This paper proposes an automatic method for detecting cruising ships using deep learning known as the "You Only Look Once (YOLO) v5 model", which is one of the frameworks of the YOLO family. In this study, ALOS-2/PALSAR-2 L-band Spotlight SAR images over the waters around the Miura Peninsula, Japan, were analyzed using the YOLO v5 model with a total of 53 ships' images and compared with Automatic Identification System (AIS) data. The results showed a precision of approximately 0.85 and a recall rate of approximately 0.89 with an F-measure of 0.87. Thus, sufficiently high values were achieved in the automatic detection of moving ships using the deep learning method with the YOLO v5 model. As for false detections, images of breakwaters were classified as ships cruising in the azimuth direction. Further, range moving ships were found to be difficult to detect. From the present preliminary study, it was found that the YOLO v5 model is limited to ships cruising predominantly in the azimuth direction. [ABSTRACT FROM AUTHOR]

Since the appearance and evolution of earthquake ionospheric precursors are expected to show a nonlinear and complex behaviour, the use of nonlinear predictor models seems more appropriate. This paper proposes a new approach based on deep learning as a powerful tool for extracting the nonlinear patterns from a time series of ionospheric precursors. A Long Short-Term Memory (LSTM) network as a type of Recurrent Neural Network (RNN) was used to investigate 52 six-month time series, deduced from the three Swarm satellite (Alpha (A), Bravo (B) and Charlie (C)) measurements, including electron density (Ne), electron temperature (Te), magnetic scalar and vector (X, Y, Z) components, Slant and Vertical Total Electron Content (STEC and VTEC), for day and night periods around the time and location of a seismic event. This new approach was tested on a strong Mw = 7.1 earthquake in Japan on 13 February 2021, at 14:07:50 UTC by comparing the results with two implemented methods, i.e., Median and LSTM methods. Furthermore, clear anomalies are seen by a voting classification method 1, 6, 8, 13, 31 and 32 days before the earthquake. A comparison with atmospheric data investigation is further provided, supporting the lithosphere–atmosphere–ionosphere coupling (LAIC) mechanism as a suitable theory to explain the alteration of upper geolayers in the earthquake preparation phase. In other words, using multi-method and multi-precursor analysis applied to 52 time series and also to the orbit-by-orbit investigation, the observed anomalies on the previous day and up to 32 days before the event in normal solar and quiet geomagnetic conditions could be considered as a striking hint of the forthcoming Japan earthquake. [ABSTRACT FROM AUTHOR]

The authors wish to make the following corrections to this paper [[1]]: There is a typing error in the value of HT ht on pages 8 and 10. In second paragraph in Section 3.2 on page 8, " HT ht " should be replaced with " HT ht ". Correction: Obata, K.; Yoshioka, H. A Simple Algorithm for Deriving an NDVI-Based Index Compatible between GEO and LEO Sensors: Capabilities and Limitations in Japan. [Extracted from the article]

Interferometric synthetic aperture radar (InSAR) enables us to obtain precipitable water vapor (PWV) maps with high spatial resolution through the phase difference caused by refraction in the atmosphere. Although previous studies have evaluated the error level of InSARPWV observations, they validated it only with C-band InSARPWV observations. Since ionospheric disturbance seriously contaminates the InSAR phase in the case of the lower-frequency SAR system, it is necessary for a PWV error level evaluation correcting the ionospheric effect appropriately if we use lower-frequency SAR systems, such as the Advanced Land Observing Satellite-2 (ALOS-2). In this paper, we evaluated the error level of the L-band InSARPWV observation obtained from ALOS-2 data covering four areas in Japan. We compared the InSAR observations with global navigation satellite system (GNSS) atmospheric observations and estimated the L-band InSARPWV error value by utilizing the error propagation theory. As a result, the L-band InSARPWV absolute error reached 2.83 mm, which was comparable to traditional PWV observations. Moreover, we investigated the impacts of the seasonality, the interferometric coherence, and the height dependence on the PWV observation accuracy in InSAR. [ABSTRACT FROM AUTHOR]