Ionosphere Waves Service (IWS) – a problem-oriented tool in ionosphere and Space Weather research produced by POPDAT project

Abstract

In the frame of the FP7 POPDAT project the Ionosphere Waves Service (IWS) has been developed and opened for public access by ionosphere experts. IWS is forming a database, derived from archived ionospheric wave records to assist the ionosphere and Space Weather research, and to answer the following questions: How can the data of earlier ionospheric missions be reprocessed with current algorithms to gain more profitable results? How could the scientific community be provided with a new insight on wave processes that take place in the ionosphere? The answer is a specific and unique data mining service accessing a collection of topical catalogs that characterize a huge number of recorded occurrences of Whistler-like Electromagnetic Wave Phenomena, Atmosphere Gravity Waves, and Traveling Ionosphere Disturbances. IWS online service (http://popdat.cbk.waw.pl) offers end users to query optional set of predefined wave phenomena, their detailed characteristics. These were collected by target specific event detection algorithms in selected satellite records during database buildup phase. Result of performed wave processing thus represents useful information on statistical or comparative investigations of wave types, listed in a detailed catalog of ionospheric wave phenomena. The IWS provides wave event characteristics, extracted by specific software systems from data records of the selected satellite missions. The end-user can access targets by making specific searches and use statistical modules within the service in their field of interest. Therefore the IWS opens a new way in ionosphere and Space Weather research. The scientific applications covered by IWS concern beyond Space Weather also other fields like earthquake precursors, ionosphere climatology, geomagnetic storms, troposphere-ionosphere energy transfer, and trans-ionosphere link perturbations.

Automated Large‐Scale Extraction of Whistlers Using Mask‐Scoring Regional Convolutional Neural Network

“…A general review of the various methods employed for ELF/VLF signal detection can be found in Ferencz et al. (2014). Algorithms such as those utilized by AWDA are extremely useful; however, such methods can be difficult to rely on in new noisy environments (as seen in ground‐based data) and do not easily generalize to new signal classes.…”

“…For example, the AWDA (Automated Whistler Detector and Analyzer) system, uses a canonical frequency-time spectrum of a mid-latitude whistler based on a dispersion relation to detect whistlers only after a lengthy noise-removal and amplitude thresholding process (Lichtenberger et al, 2008). A general review of the various methods employed for ELF/VLF signal detection can be found in Ferencz et al (2014). Algorithms such as those utilized by AWDA are extremely useful; however, such methods can be difficult to rely on in new noisy environments (as seen in ground-based data) and do not easily generalize to new signal classes.…”

VLF Waves Characterization: Wavelet Feature Extraction Method

“…Automation is possible by the machine learning that required a specific representation and characterization. In the past many scientists were working on automatic detection of VLF emission (Buzzi,2006;Linchtenbeger et al, 2008;Ferencz et al, 2009;Golden et al, 2011;Ferencz et al, 2014) by analysis of the spectrogram of VLF signals. As we know that the VLF signals are non-stationary signals and spectrogram is constructed by using Short Time Fourier transform (STFT) in which signal segment within the window function is assumed to be stationary (Akay,1997).…”