Simultaneous Iterative Reconstruction Technique Research Articles

Estimation of anisotropy parameters in vertical transverse isotropy media using the ray‐based tomography

AbstractIn seismic exploration, it is very important to consider the presence of anisotropy in order to image the subsurface correctly. The knowledge of anisotropic parameters leads to more precise characterization of reservoir, fracture density and flow paths. However, conventional geophysical methods do not directly measure these parameters, and it is useful to have a method to estimate them from seismic data. In the weak transverse isotropy approximation, the fields to be considered are the vertical and horizontal velocity components and the Thomsen parameters and . We present a method for estimating the anisotropic Thomsen parameters in the presence of weak vertical transverse isotropy using P‐wave traveltime tomography based on anisotropic ray tracing. Depending on the available information, we propose different approaches to retrieve the unknowns. A conventional three‐dimensional traveltime tomography algorithm has been extended to include anisotropic ray tracing and using the algebraic reconstruction technique or modified simultaneous iterative reconstruction technique to retrieve the unknowns. We test the method on synthetic examples for the inversion of transmitted and reflected traveltimes, and we evaluate the sensitivity of the tomographic results to the available information. Furthermore, we also consider the case of tilted transverse isotropy in a seismic reflection example.

Ultrasonic computed tomography-based internal-defect detection and location of underwater concrete piers

Concrete piers are the key load-bearing components of wading bridges. Most detection equipment and methods focus on the surface defects of underwater piers, whereas the detection methods of internal defects are very deficient. This study proposes an ultrasonic computed tomography method combining linear travel time interpolation and simultaneous iterative reconstruction techniques for detecting internal defects in underwater concrete piers. The two-dimensional median filtering and self-organizing feature map are utilized to post-process tomographic images and the confidence approach is employed to detect defects. The efficacy of the proposed method is validated through numerical simulation and underwater experiments, respectively. The Comsol Multiphysics is utilized to simulate the two-dimensional random aggregate model of the concrete and internal defects are added to validate the method’s effectiveness from a mesoscopic perspective. Then actual scale piers containing embedded defects are cast and detected. The results demonstrate that the method can identify and locate internal defects in numerical simulations and practical tests. The numerical simulation experiments results are superior to those of scale concrete specimens. In addition, underwater concrete piers with larger internal defects, square holes, and no steel reinforcements tend to have better detection results.

Three-dimensional reconstruction of Y-IrNi rhombic dodecahedron nanoframe by STEM/EDS tomography

The structural analysis of nanocrystals via transmission electron microscopy (TEM) is a valuable technique for the material science field. Recently, two-dimensional images by scanning TEM (STEM) and energy-dispersive X-ray spectroscopy (EDS) have successfully extended to three-dimensional (3D) imaging by tomography. However, despite improving TEM instruments and measurement techniques, detector shadowing, the missing-wedge problem, X-ray absorption effects, etc., significant challenges still remain; therefore, the various required corrections should be considered and applied when performing quantitative tomography. Nonetheless, this 3D reconstruction technique can facilitate active site analysis and the development of nanocatalyst systems, such as water electrolysis and fuel cell. Herein, we present a 3D reconstruction technique to obtain tomograms of IrNi rhombic dodecahedral nanoframes (IrNi-RFs) from STEM and EDS images by applying simultaneous iterative reconstruction technique and total variation minimization algorithms. From characterizing the morphology and spatial chemical composition of the Ir and Ni atoms in the nanoframes, we were able to infer the origin of the physical and catalytic durability of IrNi-RFs. Also, by calculating the surface area and volume of the 3D reconstructed model, we were able to quantify the Ir-to-Ni composition ratio and compare it to the EDS measurement result.

CONCRETE CORE DEFECTS TOMOGRAPHIC IMAGINATION FOR MESOSCALE CFST MEMBERS USING STRESS WAVE MEASUREMENT

To visualize concrete core void defects in concrete-filled steel tube (CFST) members considering the mesoscale heterogeneity and randomness of concrete core is critical in practice. A stress wave travelling time tomography imagination method using piezoelectric-lead-zirconium-titanate (PZT) patches as actuators or sensors is proposed and validated numerically. A two-dimensional (2D) multi-physics and mesoscale coupling CFST-PZT model composed of number of PZT actuators and sensors, steel tubes, and concrete core with randomly distributed circular aggregates is established. The first stress wave arrival times of the mesoscale coupling CFST-PZT model without or with a concrete core void defect are recorded by PZT sensors when a PZT actuator is excited by pulse signals. A random walking algorithm (RWA) with the Snell laws is employed to determine the shortest wave travel path between each PZT actuator and each PZT sensor. Finally, the velocity fields of the mesoscale coupling models are identified using a simultaneous iterative reconstruction technique (SIRT) to minimize the difference between the first arrival time of the stress wave and the shortest wave travel time. Results show that the location and dimension of the void defect in the models with heterogeneous concrete core can be imaged with acceptable resolution.

Ultrasonic Tomography of Core Plug Using Fresnel Volume Approach

Tomography was a non-destructive method for investigating the internal structure of an object, usually used to find internal anomalies caused by differences in the physical parameters. The fresnel volume tomography method was an alternative method for reconstructing the image of an object using travel times, whereas this method did not use a ray path in its calculations. In calculating the fresnel zone, the finite difference method which was the solution to the equation of wave propagation was used. The frequency of the waves was also considered, here we used ultrasonic waves with a frequency of 1 Mhz for the inverse modelling process. The reconstruction algorithm we used was Modified Simultaneous Iterative Reconstruction Technique for Fresnel volume. The application of the inversion of synthetic data resulted in an estimation of a velocity model that has good imaging quality and similarity with the synthetic model.

Visualization of the distortion induced by nonlinear noise reduction in computed tomography.

We developed a method to visualize the image distortion induced by nonlinear noise reduction algorithms in computed tomography (CT) systems. Nonlinear distortion was defined as the induced residual when testing a reconstruction algorithm by the criteria for a linear system. Two types of images were developed: a nonlinear distortion of an object () image and a nonlinear distortion of noise () image to visualize the nonlinear distortion induced by an algorithm. Calculation of the images requires access to the sinogram data, which is seldomly fully provided. Hence, an approximation of the image was estimated. Using simulated CT acquisitions, four noise levels were added onto forward projected sinograms of a typical CT image; these were noise reduced using a median filter with the simultaneous iterative reconstruction technique or a total variation filter with the conjugate gradient least-squares algorithm. The linear reconstruction technique filtered back-projection was also analyzed for comparison. Structures in the image indicated contrast and resolution reduction of the nonlinear denoising. Although the approximated image represented the original image well, it had a higher random uncertainty. The image for the median filter indicated both stochastic variations and structures reminding of the object while for the total variation filter only stochastic variations were indicated. The developed images visualize nonlinear distortions of denoising algorithms. The object may be distorted by the noise and vice versa. Analyzing the distortion correlated to the object is more critical than analyzing a distortion of stochastic variations. The absence of nonlinear distortion may measure the robustness of the denoising algorithm.

Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time

Noninvasive X-ray imaging of nanoscale three-dimensional objects, such as integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of the complex electromagnetic field through the IC; combined with a tomographic scan, which collects these complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach to drastically reduce the amount of angular scanning, and thus the total acquisition time. APT is machine learning-based, utilizing axial self-Attention for Ptycho-Tomographic reconstruction. APT is trained to obtain accurate reconstructions of the ICs, despite the incompleteness of the measurements. The training process includes regularizing priors in the form of typical patterns found in IC interiors, and the physics of X-ray propagation through the IC. We show that APT with ×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique (SART) with the original set of angles. When using the same set of reduced angles, then APT also outperforms Filtered Back Projection (FBP), Simultaneous Iterative Reconstruction Technique (SIRT) and SART. The time needed to compute the reconstruction is also reduced, because the trained neural network is a forward operation, unlike the iterative nature of these alternatives. Our experiments show that, without loss in quality, for a 4.48 × 93.2 × 3.92 µm3 IC (≃6 × 108 voxels), APT reduces the total data acquisition and computation time from 67.96 h to 38 min. We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging, including materials science and biological imaging.

Accuracy and Precision of Iodine Quantification in Subtracted Micro-Computed Tomography: Effect of Reconstruction and Noise Removal Algorithms

PurposeTo evaluate the effect of reconstruction and noise removal algorithms on the accuracy and precision of iodine concentration (CI) quantified with subtracted micro-computed tomography (micro-CT).ProceduresTwo reconstruction algorithms were evaluated: a filtered backprojection (FBP) algorithm and a simultaneous iterative reconstruction technique (SIRT) algorithm. A 3D bilateral filter (BF) was used for noise removal. A phantom study evaluated and compared the image quality, and the accuracy and precision of CI in four scenarios: filtered FBP, filtered SIRT, non-filtered FBP, and non-filtered SIRT. In vivo experiments were performed in an animal model of chemically-induced mammary cancer.ResultsLinear relationships between the measured and nominal CI values were found for all the scenarios in the phantom study (R2 > 0.95). SIRT significantly improved the accuracy and precision of CI compared to FBP, as given by their lower bias (adj. p-value = 0.0308) and repeatability coefficient (adj. p-value < 0.0001). Noise removal enabled a significant decrease in bias in filtered SIRT images only; non-significant differences were found for the repeatability coefficient. The phantom and in vivo studies showed that CI is a reproducible imaging parameter for all the scenarios (Pearson r > 0.99, p-value < 0.001). The contrast-to-noise ratio showed non-significant differences among the evaluated scenarios in the phantom study, while a significant improvement was found in the in vivo study when SIRT and BF algorithms were used.ConclusionsSIRT and BF algorithms improved the accuracy and precision of CI compared to FBP and non-filtered images, which encourages their use in subtracted micro-CT imaging.

Crosshole radio electromagnetic tomography using apparent source intensity and logarithmic barrier

Crosshole radio-frequency (typically from 1 to 36 MHz) electromagnetic methods are powerful tools for locating conductive ores and distribution of electric properties of rocks between boreholes. In the range of these frequencies, electromagnetic field can be approximated by the wave field. Usually, crosshole radio-frequency electromagnetic inversion requires exact source intensity but it is difficult to determine that.We present a ray-based inversion method of crosshole radio electromagnetic data which does not require exact source intensity. We introduce a concept of apparent source intensity, establish the method of determining that and realize absorption coefficient and conductivity imaging of crosshole radio electromagnetic data using that. And then we improve accuracy and reliability of tomographic inversion of crosshole radio electromagnetic data using positive constraints for absorption and conductivities. Inversion is based on simultaneous iterative reconstruction technique or adaptive control gradient method.Tests of synthetic and field data show that the method we propose has high reliability.

Tomographic image reconstruction techniques for accurate spent fuel assembly verification

Non-proliferation and the security of nuclear materials are essential. The international atomic energy agency (IAEA) considers a tomographic image acquisition technique of spent fuel assemblies a promising technique to accurately verify the rod-by-rod spent fuel conditions stored in a water pool. Researchers at Yonsei University in Korea developed the bismuth germanate (BGO) scintillator-based Yonsei Single-photon Emission Computed Tomography (YSECT). Previous research validated the YSECT system experimentally to quickly evaluate the radioactivity distribution of test fuel rods in the Korea Institute of Nuclear Nonproliferation and Control (KINAC). Quick verification of the fuel assembly requires the development of a high-quality image reconstruction algorithm that enables image acquisition within a short time. This study examined various tomographic image reconstruction techniques to identify patterns of missing fuel rods accurately. Rotational projection image data sets were obtained for 15 patterns of test fuel rods for 900 seconds using the single-photon emission computed tomography (SPECT) system installed at KINAC. The projection images were acquired every 5° while four 64-channel detectors rotated 90°. The acquired images were reconstructed using the following methods: filtered back-projection, simultaneous iterative reconstruction technique, order-subset simultaneous algebraic reconstruction technique, maximum likelihood expectation maximization (MLEM), and Fast-Iterative Shrinkage-Thresholding algorithm (FISTA). Among the reconstruction algorithms used in this study, the image quality of MLEM showed the best performance, and the image contrast of FISTA showed the highest result. Therefore, the signal-to-noise ratio of the tomographic image was improved using the image reconstruction technique optimized for the YSECT system to verify the patterns of fuel rods. Hence, even for the low-quality measured data with the short-time scan of the SPECT system, this advanced technique is expected to show better discriminability of the patterns of fuel rods in the assembly.

SIRT methods for the iterative solution of sparse OPT data reconstruction

Optical projection tomography (OPT) is an advanced three-dimensional (3D) imaging technology, which uses the filtered backprojection (FBP) algorithm to recover the 3D volume with sufficient number of projections. As to in vivo imaging, it is urgent to reduce the number of projections because the acquisition time could be minimized. However, reconstructing from undersampled OPT data can lead to artifacts and the decline in image quality. The simultaneous iterative reconstruction technique (SIRT) is introduced to remove artifacts and improve the image quality. The image qualities reconstructed from FBP and SIRT separately are compared, and the structural similarity and peak signal-to-noise ratio are calculated. Through simulated phantoms and OPT data of in vivo zebrafish embryo, SIRT consistently outperforms FBP in terms of reduced artifacts and enhanced image contrast especially when the projection numbers are reduced. SIRT method can provide high-quality reconstruction with 50 or fewer projections, thereby significantly reducing the minimum acquisition time and light dose while maintaining reconstruction quality. Through optimization and GPU acceleration, the SIRT algorithm can converge faster so as to reduce the image processing time. To our knowledge, this is the first time one SIRT algorithm is used for reconstruction of sparse OPT data. The experimental results show that SIRT algorithm outperform FBP especially when the number of projections is reduced. In addition, SIRT performs better in the preservation of vascular signal, which is significant for the monitoring of angiogenesis.

Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning.

A reconstruction algorithm is proposed, based on multi-dictionary learning (MDL), to improve the reconstruction quality of acoustic tomography for complex temperature fields. Its aim is to improve the under-determination of the inverse problem by the sparse representation of the sound slowness signal (i.e., reciprocal of sound velocity). In the MDL algorithm, the K-SVD dictionary learning algorithm is used to construct corresponding sparse dictionaries for sound slowness signals of different types of temperature fields; the KNN peak-type classifier is employed for the joint use of multiple dictionaries; the orthogonal matching pursuit (OMP) algorithm is used to obtain the sparse representation of sound slowness signal in the sparse domain; then, the temperature distribution is obtained by using the relationship between sound slowness and temperature. Simulation and actual temperature distribution reconstruction experiments show that the MDL algorithm has smaller reconstruction errors and provides more accurate information about the temperature field, compared with the compressed sensing and improved orthogonal matching pursuit (CS-IMOMP) algorithm, which is an algorithm based on compressed sensing and improved orthogonal matching pursuit (in the CS-IMOMP, DFT dictionary is used), the least square algorithm (LSA) and the simultaneous iterative reconstruction technique (SIRT).

Mapping conduits in two-dimensional heterogeneous karst aquifers using hydraulic tomography

Hydraulic tomography (HT) is a well-established approach to yield the spatial distribution of hydraulic conductivity of an aquifer. This work explores the potential of HT for the characterization of the distribution and connectivity of conduits in a two-dimensional sandbox and its corresponding synthetic aquifer. Two inversion techniques were implemented and compared: the geostatistics-based inversion which uses the simultaneous successive linear estimator (SimSLE) algorithm to conduct stochastic inversions on the transient hydraulic heads, and the travel time-based inversion which employs the simultaneous iterative reconstruction technique (SIRT) algorithm on the hydraulic travel times for tomography reconstructions. Four artificial karst conduits of different geometries were placed in an aquifer of layer with different hydraulic conductivities. We conducted 6 pumping tests at 6 different locations, and the resultant pressure responses were recorded at 42 observation points in both the sandbox and the corresponding synthetic aquifer. The measured data were then used for the inversion of hydraulic diffusivity using the SimSLE and SIRT algorithms. Our results show that both algorithms were able to approximately identify the embedded karst conduits and yield similar hydraulic diffusivity distribution. Statistically, the travel time-based inversion re-constructed high-contrast diffusivities which clearly differentiate the karst structures from the surrounding matrix. The geostatistics-based SimSLE algorithm yields a better agreement on the positions and the shapes of the embedded karst structures, compared to those obtained by the travel time-based SIRT algorithm. Uncertainties and limitations of our results are also discussed in this work, followed by recommendations on hydraulic tests in karst aquifers.

Uncertainty quantification method for elastic wave tomography of concrete structure using interval analysis

This paper presents a non-probabilistic uncertainty quantification method for the elastic wave tomography of concrete structures. An interval analysis model is applied to establish the relationship between the measurement error and identified tomography images. Measurements of parameters such as travel time and wave amplitude are modeled as intervals described by their upper and lower boundaries. Based on the straight-ray assumption of the wave propagation path, the relationship between wave velocity and travel time measurements is described by a set of linear equations. The inversion linear equation is converted into an extended fuzzy linear equation using interval analysis. Subsequently, a simultaneous iterative reconstruction technique is applied to solve this fuzzy linear equation. In contrast to the traditional methods, the obtained tomography images are described in terms of the upper and lower boundaries. Finally, numerical simulation and experimental investigation using several plain concrete models are conducted to verify the proposed approach.

Quantitative analysis of mesoporous structures by electron tomography: A phantom study

Electron tomography (ET) has attracted significant attention for a quantitative analysis of mesoporous materials, especially for complex disordered pore structures, as no priori assumption on the pore shape is needed, which is normally inevitable when using traditional bulk characterization techniques. However, a reliable quantification of such pore structures from ET critically depends on the fidelity of the segmented reconstruction, which can be significantly affected, e.g. by the raw data quality, the limited tilting range, artifacts introduced during alignment and further depends on the reconstruction algorithm. Therefore, we systematically investigated the reconstruction reliability of three main-stream algorithms including simultaneous iterative reconstruction technique (SIRT), total variation minimization (TVM) and discrete algebraic reconstruction technique (DART) for mesoporous materials using different imperfect (realistic) tilt-series based on a set of phantom simulations. We found that DART outperforms the other two methods in reliably revealing small pores and narrow channels, especially when the number of projections is strongly constrained. The accurately segmented reconstruction from DART makes it possible to achieve reliable quantification of the pores structure, which in turn leads to reliable evaluation of effective diffusion coefficients. We discuss the influence of different acquisition and reconstruction parameters on the reconstructed 3D volume and the quantitative analysis of pore features. We aim to provide a practical guideline for optimizing acquisition and reconstruction parameters and how to evaluate the accuracy when describing the mesoporous structure.

Tomographic reconstruction techniques optimized for velocity-map imaging applications.

Examples of extracting meaningful information from image projection data using tomographic reconstruction techniques can be found in many areas of science. Within the photochemical dynamics community, tomography allows for complete three-dimensional (3D) charged particle momentum distributions to be reconstructed following a photodissociation or photoionization event. This permits highly differential velocity- and angle-resolved measurements to be made simultaneously. However, the generalized tomographic reconstruction strategies typically adopted for use with photochemical imaging-based around the Fourier-slice theorem and filtered back-projection algorithms-are not optimized for these specific types of problems. Here, we discuss pre-existing alternative strategies-namely, the simultaneous iterative reconstruction technique and Hankel Transform Reconstruction (HTR)-and introduce them in the context of velocity-map imaging applications. We demonstrate the clear advantages they afford, and how they can perform considerably better than approaches commonly adopted at present. Most notably, with HTR we can set a bound on the minimum number of projections required to reliably reconstruct 3D photoproduct distributions. This bound is significantly lower than what is currently accepted and will help make tomographic imaging far more accessible and efficient for many experimentalists working in the field of photochemical dynamics.

A novel QA phantom based on scintillating fiber ribbons with implementation of 2D dose tomography for small-field radiotherapy.

To develop a novel instrument for real-time quality assurance (QA) procedures in radiotherapy. The system implements a scintillation-based phantom and associated signal acquisition and processing modules and aims to monitor two-dimensional (2D) dose distributions of small fields. For the proposed phantom, we have designed and realized a prototype implementing six high-resolution tissue-equivalent scintillating fiber ribbons stacked with in-plane 30° rotated orientations from each other. Each ribbon output is coupled to a silicon photodiode linear array (with an element pitch of 400μm) to detect scintillating signal, which represents the projected irradiation profile perpendicular to the ribbon's orientation. For the system providing six acquired projected dose profiles at different orientations, we have developed a two-step signal processing method to perform 2D dose reconstruction. The first step is to determine irradiation field geometry parameters using a tomographic geometry approach, and the second one is to perform specific penumbra estimation. The QA system prototype has been tested on a Novalis TrueBeam STX with a 6-MV photon beam for small elliptic fields defined by 5- and 10-mm cone collimators and for 10×10- and 20×10-mm2 rectangular fields defined by the micro-multileaf collimator. Gamma index analysis using EBT3 films as reference has been carried out with tight 2%-dose-difference (DD)/700-μm-distance-to-agreement (DTA) as well as 1%-DD/1-mm-DTA criteria for evaluating the system performances. The testing also includes an evaluation of the proposed two-step field reconstruction method in comparison with two conventional methods: filtered back projection (FBP) and simultaneous iterative reconstruction technique (SIRT). The reconstructed 2D dose distributions have gamma index pass rates higher than 95% for all the tested configurations as compared with EBT3 film measurements with both 2%-DD/700-μm-DTA and 1%-DD/1-mm criteria. 2D global gamma analysis shows that the two-step and FBP radiation field reconstruction methods systematically outperform the SIRT approach. Moreover, higher gamma index success rates are obtained with the two-step method than with FBP in the case of the fields defined with the stereotactic cones. The proposed small-field QA system makes a use of six water-equivalent scintillating detectors (fiber ribbons) to acquire dose distribution. The developed two-step signal processing method performs tomographic 2D dose reconstruction. A system prototype has been built and tested using hospital facilities with small rectangular and elliptic fields. Testing results show 2D reconstructed dose distributions with high accuracy and resolution. Such a system could potentially be an alternative approach to film dosimetry for small-field QA, which is still widely used as reference in clinical practice.

An improved ART algorithm for attenuation coefficient reconstruction of tomographic gamma scanners

A method of reconstructing the attenuation coefficient in the case of tomographic gamma scanning (TGS) imaging of nuclear waste tanks is proposed. The method was developed on the basis of the algebraic reconstruction technique (ART) method and is called the simultaneous iterative reconstruction techniques diagonally relaxed orthogonal projections (SIRT-DROP) method. To evaluate the accuracy of the method, a multi-layer model was designed. Each layer consisted of 25 (5 × 5) voxels, and each pixel measured 12 ×12× 12 cm. Results indicated that a maximum uncertainty of 34.88% and an average uncertainty of 14% were obtained when applying the ART method, while a maximum uncertainty of 16.11% and an average uncertainty of 9.96% were obtained when applying the proposed SIRT-DROP method. By performing linear regression in a C-language program on the energy–mass attenuation coefficient, the maximum and average uncertainties of the energy–mass attenuation coefficient were 35.32% and 15.67%, respectively, when the ART method was applied. When the SIRT-DROP method was applied, the maximum and average uncertainties of the energy–mass attenuation coefficient were lowered to 22.43% and 8.01%, respectively. Monte Carlo simulation results verified the capability of the proposed method, and the reconstructed attenuation coefficient was in an agreement with the pre-set value.

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing.

Acoustic tomography (AT) is one of a few non-contact measurement techniques that can present information about the temperature distribution. Its successful application greatly depends on the performance of the reconstruction algorithm. In this paper, a temperature distribution reconstruction method based on compressed sensing (CS) is proposed. Firstly, a measurement matrix of an AT system in a CS framework is established. Secondly, a sparse basis is selected based on the mutual coherence between the measurement matrix and sparse basis. Thirdly, an improvement of the orthogonal matching pursuit (OMP) algorithm, called the IMOMP algorithm, is proposed for pursuing efficiency in recovering sparse signals. Reconstruction experiments of Gaussian sparse signals showed that IMOMP was better than OMP in both success ratio and running time, and the selection method of sparse basis was effective. Finally, a temperature distribution reconstruction algorithm based on compressed sensing, that is, the CS-IMOMP algorithm, is proposed. Simulation and experiment results show that, compared with the least square algorithm and the Simultaneous Iterative Reconstruction Technique algorithm, the CS-IMOMP algorithm has smaller reconstruction errors and provides more accurate information about the temperature distribution.

Ionospheric Reconstruction Using GNSS Signals Around an Anomaly Crest Location in Indian Longitude Sector

AbstractThe present study aims to reconstruct the vertical ionospheric electron density (IED) profile using computerized ionospheric tomography (CIT) techniques, over the northern crest of the equatorial ionization anomaly (EIA) which is one of the most geo‐physically sensitive regions of Indian longitude sector. Total electron content (TEC) measurements obtained from GNSS signals from receivers located at Kolkata (22.57°N, 88.36°E), Purulia (23.33°N, 86.36°E), and Siliguri (26.73°N, 88.39°E) and International GNSS Service (IGS) stations of Port Blair (11.64°N, 92.71°E), Bangalore (13.02°N, 77.57°E), Hyderabad (17.42°N, 78.55°E), Lucknow (26.91°N, 80.96°E) and Lhasa (29.66°N, 91.10°E) have been incorporated into the tomography algorithm for ionospheric reconstructions. Among the different iterative techniques used for retrieving IED profiles on a 2‐dimensional altitude versus latitude plane fixed at a certain longitude, simultaneous iterative reconstruction technique (SIRT) was shown to be the optimum choice based on the representation of the EIA phenomenon and reconstruction accuracy. A comparative assessment between two ionospheric models IRI2016 and PIM1.4 was performed, with PIM based initial guess for inversions resulting in lower reconstruction errors. The 2D CIT algorithm was then used to reconstruct the hourly vertical IED profiles to study the impact of the severe G4‐class St. Patrick's Day storm on 17 March 2015 and reconstructed electron densities showed a large increase over the values obtained from the geomagnetically quiet day of 10 March 2015, indicating a positive ionospheric storm phase. Ionospheric response to the G1‐class geomagnetic disturbance on 5 December 2017 and the average F‐region IED values have also been compared with a geomagnetically quiet day of 3 December 2017.