▶ Gathers original articles on topics in earth and planetary sciences ▶ Coverage includes geomagnetism, aeronomy, space science, seismology, volcanology, geodesy and planetary science ▶ Official journal of the Society of Geomagnetism and Earth, Planetary and Space Sciences, The Seismological Society of Japan, The Volcanological Society of Japan, The Geodetic Society of Japan, and The Japanese Society for Planetary Sciences

「Earth,Planets and Space」のオープンアクセス出版

Unsupervised spectral clustering for shield tunneling machine monitoring data with complex network theory

During the last decade, the TOPEX/Poseidon mission has provided a wealth of data pertaining to total electron content (TEC) measurements over the oceans, where conventional measurements are sparse. In this study, a comprehensive comparison of the TOPEX TEC measurements with the recent version of the International Reference Ionosphere (IRI-2001) was performed. The study covered solar cycle, seasonal, geomagnetic activity, and longitudinal variations. First, it was found that both the IRI and TOPEX TEC show a negligibly small geomagnetic dependency, regardless of the solar activity and seasonal conditions. For solar activity, however, not only the TECs from the IRI and TOPEX measurements, but also the difference between them, strongly depend on the solar activity. The comparison also shows that the daytime low-latitude ionosphere from the IRI always develops earlier than the corresponding TOPEX measurements, appearing as an overestimate of IRI TEC at around 7 magnetic local time (MLT) in the morning. With respect to the annual and seasonal variations of TEC, the TOPEX TEC shows stronger annual and semiannual anomalies than the IRI TEC both at low latitudes and at upper mid-latitudes. However, the IRI and TOPEX TECs show a similar seasonal anomaly only for high solar activity. Finally, the longitudinal variations of the IRI TEC show good agreement with the TOPEX measurements for low solar activity, but for high solar activity, large discrepancies occur in the Pacific sector.

Comparison of IRI-2001 With TOPEX TEC Measurements

Hyperspectral band selection via region-aware latent features fusion based clustering

Band selection (BS) is a commonly used dimension reduction technique for hyperspectral images. In this article, we propose a similarity-based ranking (SR) strategy inspired by a density-based clustering algorithm. The representativeness of a band is evaluated according to its ability to become a cluster center. We introduce structural similarity (SSIM) to measure the relationships between the bands. Thus, our proposed ranking-based BS method is called SR-SSIM. We picked state-of-the-art BS methods as competitors and carried out classification experiments on different data sets. The results illustrated that SR-SSIM outperformed the other methods. It is demonstrated, in this article, that the SSIM is more suitable for hyperspectral BS than the Euclidean distance since the SSIM can mine the spatial information contained in the band images. Furthermore, we discuss the application of BS methods on deep learning classifier. We found that proper preprocessing by the BS method can effectively eliminate redundant information and avoid overfitting.

A Similarity-Based Ranking Method for Hyperspectral Band Selection

Combining analyses of spatial and temporal characteristics of the ionosphere is of great significance for scientific research and engineering applications. Tensor decomposition is performed to explore the temporal–longitudinal–latitudinal characteristics in the ionosphere. Three-dimensional tensors are established based on the time series of ionospheric vertical total electron content maps obtained from the Centre for Orbit Determination in Europe. To obtain large-scale characteristics of the ionosphere, rank-1 decomposition is used to obtain $$U^{(1)}$$
 U
 (
 1
 )
 , $$U^{(2)}$$
 U
 (
 2
 )
 , and $$U^{(3)}$$
 U
 (
 3
 )
 , which are the resulting vectors for the time, longitude, and latitude modes, respectively. Our initial finding is that the correspondence between the frequency spectrum of $$U^{(1)}$$
 U
 (
 1
 )
 and solar variation indicates that rank-1 decomposition primarily describes large-scale temporal variations in the global ionosphere caused by the Sun. Furthermore, the time lags between the maxima of the ionospheric $$U^{(2)}$$
 U
 (
 2
 )
 and solar irradiation range from 1 to 3.7 h without seasonal dependence. The differences in time lags may indicate different interactions between processes in the magnetosphere–ionosphere–thermosphere system. Based on the dataset displayed in the geomagnetic coordinates, the position of the barycenter of $$U^{(3)}$$
 U
 (
 3
 )
 provides evidence for north–south asymmetry (NSA) in the large-scale ionospheric variations. The daily variation in such asymmetry indicates the influences of solar ionization. The diurnal geomagnetic coordinate variations in $$U^{(3)}$$
 U
 (
 3
 )
 show that the large-scale EIA (equatorial ionization anomaly) variations during the day and night have similar characteristics. Considering the influences of geomagnetic disturbance on ionospheric behavior, we select the geomagnetic quiet GIMs to construct the ionospheric tensor. The results indicate that the geomagnetic disturbances have little effect on large-scale ionospheric characteristics.

/pdf/analysis-of-temporal-longitudinal-latitudinal-zx3bbbhxzj.pdf

Analysis of temporal–longitudinal–latitudinal characteristics in the global ionosphere based on tensor rank-1 decomposition

Modeling the global ionospheric variations based on complex network

. Complex networks have emerged as an essential approach of geoscience to
generate novel insights into the nature of geophysical systems. To investigate
the dynamic processes in the ionosphere, a directed complex network is
constructed, based on a probabilistic graph of the vertical total electron content (VTEC) from 2012. The results of the power-law hypothesis test show
that both the out-degree and in-degree distribution of the ionospheric
network are not scale-free. Thus, the distribution of the interactions in the
ionosphere is homogenous. None of the geospatial positions play an eminently
important role in the propagation of the dynamic ionospheric processes. The
spatial analysis of the ionospheric network shows that the interconnections
principally exist between adjacent geographical locations, indicating that
the propagation of the dynamic processes primarily depends on the geospatial
distance in the ionosphere. Moreover, the joint distribution of the edge
distances with respect to longitude and latitude directions shows that the
dynamic processes travel further along the longitude than along the latitude
in the ionosphere. The analysis of “small-world-ness” indicates that the
ionospheric network possesses the small-world property, which can make the
ionosphere stable and efficient in the propagation of dynamic processes.

/pdf/complex-network-description-of-the-ionosphere-53yipf4ifp.pdf

Xihai Li

Papers

A Similarity-Based Ranking Method for Hyperspectral Band Selection

Analysis of temporal–longitudinal–latitudinal characteristics in the global ionosphere based on tensor rank-1 decomposition

Modeling the global ionospheric variations based on complex network

Complex network description of the ionosphere

Spatiotemporal Variations of Quiet Time Equatorial Ionosphere Longitudinal Structure Under Low Solar Activity