[1] A three-dimensional ionospheric reconstruction system is presented that models ionospheric dynamics and accounts for well-known limitations in the available data by using geometrically transformed prior models based on available models, such as the Parameterized Ionospheric Model (PIM) or the International Reference Ionosphere (IRI), to produce density solutions consistent with available Total Electron Content (TEC) measurement data. It is known that current state of the art prior density models, such as PIM or IRI, can only simulate a statistical mean ionosphere. As a result, sudden variations in ionospheric electron density will not be represented in these models. This paper focuses on a three-dimensional ionospheric density reconstruction system that uses a set of geometrical transformations to produce Flexible Prior Models (FPM). The Flexible Prior Model process allows means to include flexibility in a prior model in the form of geometrical variations that are not well represented in current state-of-the-art ionospheric prior density models. The updated priors are used as initial solution models for a set of (A, B) Multiplicative Algebraic Reconstruction Technique (ABMART) algorithms, that can easily incorporate additional information and provide a spatially constrained density solution, consistent with the available data and our current knowledge of ionospheric dynamics. The ultimate goal is to create a three-dimensional adaptive ionospheric electron density reconstruction system that would make it possible to generate real-time ionospheric maps supported by available TEC data. Such maps would be of significant utility in predicting and correcting the impact of electron density gradients and irregularities on radio waves.

With regards to the correlations between TEC and pre-earthquake and seismic activities, the TEC is an important parameter of study because it has the potential for showing the changes in the ionosphere due to these activities. It is because seismic and pre-earthquake activities create stress in rocks in the earth's crust. These stresses are known to positively charge the rocks on the earth's crust. As the positive charges accumulate at the rocks outer surfaces, they create a difference in potential which in turn creates a flow of charges that can travel fast and far from their point of origin. As the charges travel upward under the influence of the electric field lines between the surface of earth and the bottom of the ionosphere, they reach the bottom of the ionosphere, disturbing the equilibrium of the electrons in the ionosphere (Freund, Takeuchi & Lau, 2006). These disturbances can be seen in the TEC which makes TEC a potential candidate as an earthquake precursor. If TEC disturbances could be used as an earthquake precursor, tracking those disturbances could be used as part of an earthquake forecasting system which would improve earthquake warning systems, in turn saving countless lives.This study uses TEC data from Japan and current knowledge of the Tōhoku Japan earthquake to determine whether pre-earthquake and seismic activities correlate with TEC changes around the time of the earthquake. METHODSTEC disturbances can be observed using GPS signals. It is because the ionosphere creates a phase delay in the electromagnetic signals, sent from a receiver on earth to a GPS satellite in orbit. The phase delays change based on several variables: the frequency of the emitted signals, the path from the receiver to the satellite and the associated electron density along the path. The phase delay can be used to estimate the distance (called pseudo-range) from the GPS satellite and the receiver. More specifically, since the iono-

[1] The problem of reconstructing ionospheric electron density from ground-based receiver to satellite total electron content (TEC) measurements is formulated as an underdetermined discrete linear inverse problem. If receivers and satellite orbit are coplanar, then a single two-dimensional (2-D) imaging plane can be used as a geometrical model. In most cases, receiver locations are determined by convenience and availability of sites, and thus a 3-D imaging volume is required in order to capture the part of the electron density solution that is associated with gaps between receiver stations within a chain. It is well understood that spurious features, or ''wings,'' associated with these gaps can be produced in tomographic image reconstructions where ray path coverage from individual stations is lost or minimal. In this paper, a 3-D reconstruction system that takes advantage of a multiple-chain data acquisition geometry and provides a solution consistent with available TEC data everywhere within a receiver chain is presented. The reconstruction system exploits the fact that gaps, created by longitudinally unaligned receivers within a chain, can be captured as additional planes of constant longitudes within the 3-D imaging volume. With parameterized ionospheric model (PIM)-generated data as a nonnegative prior estimate of the electron density, the reconstruction algorithm uses constraints based on prior knowledge of the 3-D spatial Fourier transform of the prior electron density as a smoothing mechanism in the tomographic reconstruction process. Consequent to the underlined Fourier transform formulation, a unique solution is produced at locations that contributed to the measured TEC data, and a solution is interpolated within the remainder of the imaging volume. The band-limited BISMART algorithm has been evaluated using a multiple-receiver chain TEC data acquisition system, under known ionospheric conditions. The algorithm satisfactorily reconstructs density solutions, consistent with small-scale enhancements, irregularities, and troughs in the auroral ionosphere, from the available TEC data. The quality of the density reconstructions, coupled with the computational efficiency of this algorithm, indicates the potential utility of this technique for real-time three-and four-dimensional ionospheric tomography.Citation: Cornely, P.-R. J., and W. S. Kuklinski (2005), Three-dimensional ionospheric tomography via band-limited constrained iterative cross-entropy minimization, Radio Sci., 40, RS5S90,

No abstract

A number of important optimization problems have been classified as mapping applied towards segmentation of important features. The segmentation of important features can be formulated as configurational mapping problems by representing mapping configurations as solutions to problems of interest. One example of such configuration mapping is found in image segmentation where an image can be represented as unique subsets of a complete image and then evolved through mapping to become a segment of specific interest within an image. An effective segmentation mapping algorithm must determine the specific image subsets of an image field that best exhibit an a priori set of quantitative and qualitative characteristics. In this paper, a Genetic Optimization Mapping Algorithm is used to produce a population of sub-images, characteristic of specific image subsets of interest that were tested via a quantitative objective function, ranked using a linear fitness scheme, and modified using a genetic Crossover operator. The mapping algorithm is found to converge, within fifty to one hundred generations of maps, to a good fit to the targeted mapping configuration in a very robust and efficient manner.