Estimation of 3D electron density in the Ionosphere by using fusion of GPS satellite-receiver network measurements and IRI-Plas model

GPS systems can give a good approximation of the Slant Total Electron Content in a cylindrical path between the GPS satellite and the receiver. International Reference Ionosphere extended to Plasmasphere (IRI-Plas) model can also give an estimation of the vertical electron density profile in the ionosphere for any given location and time, in the altitude range from about 50 km to 20000 km. This information can be utilized to obtain total electron content between any given receiver and satellite locations based on the IRI-Plas model. This paper explains how the fusion of measurements obtained from a GPS satellite-receiver network can be utilized together with the IRI-Plas model in order to obtain a robust 3D electron density model of the ionosphere. © 2013 ISIF ( Intl Society of Information Fusi

Ionosphere Monitoring with Remote Sensing

This book focuses on the characterization of the physical properties of the Earth’s ionosphere, contributing to unveiling the nature of several processes responsible for a plethora of space weather-related phenomena taking place in a wide range of spatial and temporal scales. This is made possible by the exploitation of a huge amount of high-quality data derived from both remote sensing and in situ facilities such as ionosondes, radars, satellites and Global Navigation Satellite Systems receivers

Shape completion with a 3D Convolutional Neural Network for multi-domain O&M activities in offshore wind farms.

An autonomous vehicle needs to understand its surrounding environment to plan routes and avoid collisions. For that purpose, they are equipped with appropriate sensors which allow them to capture the necessary information. The maritime environment presents additional which make it hard to have a clear picture of the nearby structures. In this work, the goal is to use the available sensor information to infer the complete shape of nearby structures. The approach is divided into three main components: clustering, classification, and registration. The clustering is used to detect sizeable structures and remove irrelevant ones. The resulting data is voxelized, and classified, by a 3D CNN, as one of the studied structures. Finally, a hybrid PSO-ICP registration method is used to fit a complete CAD model on the observed data

Geosystemics View of Earthquakes

Earthquakes are the most energetic phenomena in the lithosphere: their study and comprehension are greatly worth doing because of the obvious importance for society. Geosystemics intends to study the Earth system as a whole, looking at the possible couplings among the different geo-layers, i.e., from the earth’s interior to the above atmosphere. It uses specific universal tools to integrate different methods that can be applied to multi-parameter data, often taken on different platforms (e.g., ground,marine or satellite observations). Itsmain objective is to understand the particular phenomenon of interest from a holistic point of view. Central is the use of entropy, together with other physical quantities that will be introduced case by case. In this paper, we will deal with earthquakes, as final part of a long-term chain of processes involving, not only the interaction between different components of the Earth’s interior but also the coupling of the solid earth with the above neutral or ionized atmosphere, and finally culminating with the main rupture along the fault of concern. Particular emphasis will be given to some Italian seismic sequences.Publishedid 4121A. Geomagnetismo e PaleomagnetismoJCR Journa

The 2019–2020 Khalili (Iran) Earthquake Sequence— Anthropogenic Seismicity in the Zagros Simply Folded Belt?

This work was supported by the International Training Course "Seismology and Seismic Hazard Assessment,'' which has been funded by the GeoForschungsZentrum Potsdam (GFZ) and the German Federal Foreign Office through the German Humanitarian Assistance program, grant S08‐60 321.50 ALL 03/19. Furthermore, M. Jamalreyhani acknowledges support by a grant from the Iran National Science Foundation (INSF) under a research project “97013349.” E. Nissen was supported by the Natural Sciences and Engineering Research Council of Canada through Discovery Grant 2017‐04029, the Canada Foundation for Innovation, the British Columbia Knowledge Development Fund, and a Tier 2 Canada Research Chair. J. A. López‐Comino has also received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska‐Curie grant agreement 754446 and UGR Research and Knowledge Transfer Found–Athenea3i and by project 407141557 of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). Most of the maps were prepared using the Pyrocko toolbox and GMT 5 software. We are grateful to all data research centers and networks for providing the data used in this study. InSAR interferograms were made using freely available Copernicus Sentinel data (2017; https://scihub.copernicus.eu/ ). We are thankful to Gudrun Richter, Christian Heberland, Sebastian Hainzl, Torsten Dahm, and Mir Ali Hassanzadeh for constructive comments on this work. M. Jamalreyhani and P. Büyükakpınar are very grateful to Claus Milkereit and Dorina Kroll for supporting them, during their visit to the GFZ Potsdam. Furthermore, we are grateful to Editor Rachel Abercrombie, the associate editor, and four anonymous reviewers for their valuable comments and suggestions that helped us to improve the manuscript.The seismic catalog and waveforms of the Iran network were downloaded from the Iranian Seismological Center (IRSC) available at http://irsc.ut.ac.ir/ . The IRSC waveforms are freely available for the events with magnitude larger than 4 and the IRSC catalog is available for 2.5+. The array data used for teleseismic modeling were downloaded from the Incorporated Research Institutions for Seismology (IRIS) Data Management Center (funded through the Seismological Facilities for the Advancement of Geoscience and EarthScope proposal of the National Science Foundation under Cooperative Agreement EAR‐1261681) and the Institute for Geosciences and Natural Resources, Germany (BGR). InSAR interferograms were made using Copernicus Sentinel data available at https://scihub.copernicus.eu/ . Information on the Shanul and Homa gas reservoirs obtained from the Iranian Central Oil Fields Company webpage (ICOFC, https://en.icofc.ir/ ), Southern Zagros Oil and Gas Production Company ( https://www.szogpc.com/ ), and National Iranian Oil Company (NIOC). The geological map of the region, which is published by the Geological Survey of Iran (GSI), is available at https://gsi.ir/en . M n M nWe investigate the origin of a long-lived earthquake cluster in the Fars arc of the Zagros Simply Folded Belt that is colocated with the major Shanul natural gas field. The cluster emerged in January 2019 and initially comprised small events of Mn ∼ 3–4. It culminated on 9 June 2020 with a pair of Mw 5.4 and 5.7 earthquakes, which was followed by >100 aftershocks. We assess the spatiotemporal evolution of the earthquake sequence using multiple event hypocenter relocations, waveform inversions, and Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) measurements and models. We find that the early part of the sequence is spatially distinct from the 9 June 2020 earthquakes and their aftershocks. Moment tensors, centroid depths, and source parameter uncertainties of 15 of the largest (Mn ≥ 4.0) events show that the sequence is dominated by reverse faulting at shallow depths (mostly ≤4 km) within the sedimentary cover. InSAR modeling shows that the Mw 5.7 mainshock occurred at depths of 2–8 km with a rupture length and maximum slip of ∼20 km and ∼0.5 m, respectively. Our results suggest that the 2019–2020 Khalili earthquake sequence was likely influenced by operation of the Shanul field, though elevated natural seismicity in the Zagros makes the association difficult to prove. Understanding how to distinguish man-made from natural seismicity is helpful for hazard and risk assessment, notably in the Zagros, which is both seismically active and rich in oil and gas reserves.Claus Milkereit and Dorina KrollGerman Federal Foreign OfficeGerman Humanitarian Assistance program S08‐60 321.50 ALL 03/19Institute for Geosciences and Natural ResourcesTier 2 Canada Research ChairNatural Sciences and Engineering Research Council of Canada 2017‐04029Canada Foundation for InnovationDeutsche ForschungsgemeinschaftIran National Science Foundation 97013349, EAR‐1261681Horizon 2020 407141557, 754446British Columbia Knowledge Development FundHelmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum GF

Space-time sampling strategies for electronically steerable incoherent scatter radar

Incoherent scatter radar (ISR) systems allow researchers to peer into the ionosphere via remote sensing of intrinsic plasma parameters. ISR sensors have been used since the 1950s and until the past decade were mainly equipped with a single mechanically steerable antenna. As such, the ability to develop a two or three dimensional picture of the plasma parameters in the ionosphere has been constrained by the relatively slow mechanical steering of the antennas. A newer class of systems using electronically steerable array (ESA) antennas have broken the chains of this constraint, allowing researchers to create 3-D reconstructions of plasma parameters. There have been many studies associated with reconstructing 3-D fields of plasma parameters, but there has not been a systematic analysis into the sampling issues that arise. Also, there has not been a systematic study as to how to reconstruct these plasma parameters in an optimum sense as opposed to just using different forms of interpolation. The research presented here forms a framework that scientists and engineers can use to plan experiments with ESA ISR capabilities and to better analyze the resulting data. This framework attacks the problem of space-time sampling by ESA ISR systems from the point of view of signal processing, simulation and inverse theoretic image reconstruction. We first describe a physics based model of incoherent scatter from the ionospheric plasma, along with processing methods needed to create the plasma parameter measurements. Our approach leads to development of the space-time ambiguity function, forming a theoretical foundation of the forward model for ISR. This forward model is novel in that it takes into account the shape of the antenna beam and scanning method along with integration time to develop the proper statistics for a desired measurement precision. Once the forward model is developed, we present the simulation method behind the Simulator for ISR (SimISR). SimISR uses input plasma parameters over space and time and creates complex voltage samples in a form similar to that produced by a real ISR system. SimISR allows researchers to evaluate different experiment configurations in order to efficiently and accurately sample specific phenomena. We present example simulations using input conditions derived from a multi-fluid ionosphere model and reconstructions using standard interpolation techniques. Lastly, methods are presented to invert the space-time ambiguity function using techniques from image reconstruction literature. These methods are tested using SimISR to quantify accurate plasma parameter reconstruction over a simulated ionospheric region

Ionospheric Multi-Spacecraft Analysis Tools

This open access book provides a comprehensive toolbox of analysis techniques for ionospheric multi-satellite missions. The immediate need for this volume was motivated by the ongoing ESA Swarm satellite mission, but the tools that are described are general and can be used for any future ionospheric multi-satellite mission with comparable instrumentation. In addition to researching the immediate plasma environment and its coupling to other regions, such a mission aims to study the Earth’s main magnetic field and its anomalies caused by core, mantle, or crustal sources. The parameters for carrying out this kind of work are examined in these chapters. Besides currents, electric fields, and plasma convection, these parameters include ionospheric conductance, Joule heating, neutral gas densities, and neutral winds.