When radio waves from our galaxy enter the Earth's atmosphere, they are absorbed by electrons in the upper atmosphere. It was thought that by measuring the amount of absorption, it would allow the height of these electrons in the atmosphere to be determined. If so, this would have significance for future instrument design. However, this paper demonstrates that it is not possible to do this, but it does explain how multiple-frequency measurements can nevertheless be useful.

The transient electromagnetic method (TEM) is widely used for mapping subsurface resistivity structures, but data are inevitably contaminated by noise from various sources including radio signals in the very low frequency (VLF) 3–30 kHz band. We present an approach where VLF noise is effectively suppressed with a new post-processing scheme where boxcar gates are combined into semi-tapered gates. The result is a 20 % increase in the depth of investigation for the presented test survey.

The Capon method serves as a powerful and robust data analysis tool when working on various kinds of ill-posed inverse problems. Besides the analysis of waves, the method can be used in a generalized way to compare actual measurements with theoretical models, such as Mercury's magnetic field analysis. In view to the BepiColombo mission this work establishes a mathematical basis for the application of Capon's method to analyze Mercury's internal magnetic field in a robust and manageable way.

The K index, characterizing local geomagnetic activity, is generally automatically calculated by algorithms such as KASM, one of the four software programs recommended by INTERMAGNET. KASM requires an appropriate L9 value. We analyze K values for Italian observatories and compare them with historical German observatories to establish the best L9 estimation for our stations. A comparison with results from a previous empirical method shows the consistency and reliability of our outcome.

We present a new versatile datalogger that can be used remotely for a wide range of applications in geosciences such as environmental and groundwater monitoring or in applied geophysics. The recorded signals will be processed using a new software approach, which will improve the data quality for very poor signal-to-noise ratios. We show this improvement by comparing it with traditional software-algorithm-processing synthetic data sets and real data collected in the field.