GPRPy is an open-source ground-penetrating radar software compatible with a range of ground-penetrating radar systems. Data processing and plotting can be performed by using graphical user interfaces or scripts that are generated automatically from the graphical user interfaces. This makes learning the software easy, and it enables researchers to share their scripts as part of a publication to ensure reproducible research. GPRPy enables profile data processing and visualization, velocity analysis, interpolation of 3D data cubes from profile data, and 3D interpolation for interfaces visible in multiple profiles. The software is written in Python and runs on all major operating systems.

When modelling satellite data to recover a global planetary magnetic or gravitational potential field, the method of choice remains their analysis in terms of spherical harmonics. When only regional data are available, or when data quality varies strongly with geographic location, the inversion problem becomes severely ill-posed. In those cases, adopting explicitly local methods is to be preferred over adapting global ones (e.g. by regularization). Here, we develop the theory behind a procedure to invert for planetary potential fields from vector observations collected within a spatially bounded region at varying satellite altitude. Our method relies on the construction of spatiospectrally localized bases of functions that mitigate the noise amplification caused by downward continuation (from the satellite altitude to the source) while balancing the conflicting demands for spatial concentration and spectral limitation. The ‘altitude-cognizant’ gradient vector Slepian functions (AC-GVSF) enjoy a noise tolerance under downward continuation that is much improved relative to the ‘classical’ gradient vector Slepian functions (CL-GVSF), which do not factor satellite altitude into their construction. Furthermore, venturing beyond the realm of their first application, published in a preceding paper, in the present article we extend the theory to being able to handle both internal and external potential-field estimation. Solving simultaneously for internal and external fields under the limitation of regional data availability reduces internal-field artefacts introduced by downward-continuing unmodelled external fields, as we show with numerical examples. We explain our solution strategies on the basis of analytic expressions for the behaviour of the estimation bias and variance of models for which signal and noise are uncorrelated, (essentially) space- and band-limited, and spectrally (almost) white. The AC-GVSF are optimal linear combinations of vector spherical harmonics. Their construction is not altogether very computationally demanding when the concentration domains (the regions of spatial concentration) have circular symmetry, for example, on spherical caps or rings—even when the spherical-harmonic bandwidth is large. Data inversion proceeds by solving for the expansion coefficients of truncated function sequences, by least-squares analysis in a reduced-dimensional space. Hence, our method brings high-resolution regional potential-field modelling from incomplete and noisy vector-valued satellite data within reach of contemporary desktop machines.

We use magnetic field data collected in orbit around Mercury by the MErcury Surface, Space ENvironment, GEochemistry and Ranging satellite, to detect a regional magnetic field anomaly that is spatially associated with Mercury's Northern Rise topographic signature. Regional spectral analysis indicates a source depth at or below the core‐mantle boundary, and hence the anomaly is of core, not crustal, origin. This observation supports previous studies linking the Northern Rise to a deep‐seated gravity anomaly and reveals connections among core, mantle, and crustal dynamics, likely enabled by Mercury's thin mantle.

Ganymede is the only moon in our solar system known to have a large-scale intrinsic magnetic field, likely generated in the moon’s metallic core. Initial analyses of Galileo spacecraft measurements concluded that Ganymede’s intrinsic magnetic field is dominated by a magnetic dipole and that quadrupolar contributions are exceptionally weak. These findings have influenced the development of models for Ganymede’s core dynamo over the past two decades, some concluding that Ganymede’s dynamo is limited to the innermost part of Ganymede’s core. Here, we reassess Ganymede’s internal field contributions based on the magnetic measurements from close Galileo flybys of Ganymede (G1, G2, G7, G8, G28, and G29), adding the recent Juno flyby. We find that presently available data cannot constrain Ganymede’s quadrupole moment, as we demonstrate by constructing models with a range of quadrupole moments, including relative values comparable to those at the Earth. As a consequence, global analysis of available data cannot constrain the spatial limits of Ganymede’s core dynamo. Incorporating ocean induction for a range of Ganymede ocean models indicates that ocean induction may be present, but that available magnetic data cannot discern between end-member cases for Ganymede ocean models.

SUMMARY: We present a method to calculate local spherical harmonic power spectra directly from spacecraft magnetic and gravity data with varying spacecraft altitude. Previously published applications of local spherical harmonic power spectra have been formulated for data that are available at a single collection altitude, such as data evaluated from a global spherical harmonic model. Our approach consists of first solving for local models from local data and then obtaining local multitaper spectra from the local models. We demonstrate with numerical tests that this approach can produce reliable results. Our method is particularly useful in situations where data coverage does not allow for calculating global magnetic or gravity field models, or where data quality or quantity varies regionally and where local models could yield superior resolution or quality over global models.

Ground Penetrating Radar (GPR) facilitates the detection and localisation of subsurface structural anomalies in critical transport infrastructure (e.g. tunnels), better informing targeted maintenance strategies. However, conventional fixed-directional systems suffer from limited coverage - especially of less-accessible structural aspects (e.g. crowns) - alongside unclear visual output of anomaly spatial profiles, both for physical and simulated datasets. To tackle these limitations, new hybrid-rotational GPR utilises novel 360& orientable air-launched antennas to increase subsurface coverage, principally in tunnels. Prototype systems currently lack a versatile workflow to generate practical visual output for surveyors. This study develops a versatile visualisation workflow based on entirely open access tools, returning 3D spatial profiles of subsurface anomalies in (i) simulated, (ii) fixed-directional and (iii) hybrid-rotational GPR datasets. Work includes development of two unique hybrid-rotational GPR systems, for laboratory and in-field data collection respectively. Following initial 3D grid alignment and smoothing, data undergoes 3D Stolt migration, normalisation and proximal clustering via Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). This returns segmented point subsets associated with suspected structural anomalies. Finally, 3D convex hulls are recovered using the QuickHull method. Detection and localisation performance is first appraised through numerical simulation in open-source software gprMax. Practical laboratory experimentation follows, with both commercial fixed-directional system and developed hybrid-rotational GPR, before in-field demonstration on a large-scale, tunnel subsurface analogue. In each experiment, all targets were successfully identified within returned 3D visualisations of hybrid-rotational GPR datasets. Moreover, the spatial profiles were consistently observed to be accurately localised to within decimetre length scales of known target locations. Overall, the advances presented in this work both facilitate and demonstrate the significant practical potential of new hybrid-rotational GPR technology as a basis for future subsurface surveying of critical transport infrastructure.