Characterisation of Fractures and Fracture Zones in a Carbonate Aquifer Using Electrical Resistivity Tomography and Pricking Probe Methodes

Position, width and fragmentation level of fracture zones and position, significance and characteristic distance of fractures were aimed to determine in a carbonate aquifer. These are fundamental parameters, e.g. in hydrogeological modelling of aquifers, due to their role in subsurface water movements. The description of small scale fracture systems is however a challenging task. In the test area (Kádárta, Bakony Mts, Hungary), two methods proved to be applicable to get reasonable information about the fractures: Electrical Resistivity Tomography (ERT) and Pricking-Probe (PriP). PriP is a simple mechanical tool which has been successfully applied in archaeological investigations. ERT results demonstrated its applicability in this small scale fracture study. PriP proved to be a good verification tool both for fracture zone mapping and detecting fractures, but in certain areas, it produced different results than the ERT. The applicability of this method has therefore to be tested yet, although its problems most probably origin from human activity which reorganises the near-surface debris distribution. In the test site, both methods displayed fracture zones including a very characteristic one and a number of individual fractures and determined their characteristic distance and significance. Both methods prove to be able to produce hydrogeologically important parameters even individually, but their simultaneous application is recommended to decrease the possible discrepancies

An alternate representation of the geomagnetic core field obtained using machine learning

Machine learning (ML) as a tool is rapidly emerging in various branches of contemporary geophysical research. To date, however, rarely has it been applied specifically for the study of Earth’s internal magnetic field and the geodynamo. Prevailing methods currently used in inferring the characteristic properties and the probable time evolution of the geodynamo are mostly based on reduced representations of magnetohydrodynamics (MHD). This study introduces a new inference method, referred to as Current Loop-based UNet Model Segmentation Inference (CLUMSI). Its long-term goal focuses on uncovering concentrations of electric current densities inside the core as the direct sources of the magnetic field itself, rather than computing the fluid motion using MHD. CLUMSI relies on simplified models in which equivalent current loops represent electric current systems emerging in turbulent geodynamo simulations. Various configurations of such loop models are utilized to produce synthetic magnetic field and secular variation (SV) maps computed at the core–mantle boundary (CMB). The resulting maps are then presented as training samples to an image-processing neural network designed specifically for solving image segmentation problems. This network essentially learns to infer the parameters and configuration of the loops in each model based on the corresponding CMB maps. In addition, with the help of the Domain Adversarial Training of Neural Networks (DANN) method during training, historical geomagnetic field data could also be considered alongside the synthetic samples. This implementation can increase the likelihood that a network trained primarily on synthetic data will appropriately handle real inputs. Our results focus mainly on the method's feasibility when applied to synthetic data and the quality of these inferences. A single evaluation of the trained network can recover the overall distribution of loop parameters with reasonable accuracy. To better represent conditions in the outer core, the study also proposes a computationally feasible process to account for magnetic diffusion and the corresponding induced currents in the loop models. However, the quality of the reconstruction of magnetic field properties is compromised by occasional poor inferences, and an inability to recover realistic SV

Atmospheric electric potential gradient data at the Széchenyi István Geophysical Observatory, Hungary, digitized from photographical records from the years 1999-2009

The negative of the vertical component of the atmospheric direct current (DC) electric field is referred to as the atmospheric electric potential gradient (PG). The PG depends on the actual ionospheric potential, local electric fields, and the electrical conductivity of the air at the place of the measurement. These factors are more or less directly connected to meteorological conditions. The overall state of the global network of large-scale electrical currents in the Earth-ionosphere system can be inferred from the PG when the weather is locally calm. This state is traditionally referred to as “fair weather” and is characterized by allowed ranges of specified meteorological parameters (Harrison and Nicoll, 2018). This dataset contains PG data recorded in the Széchenyi István Geophysical Observatory (NCK, 47.632°N, 16.718°E), Hungary from 1999 up to 2009. Throughout this time period, data were collected using two instruments (with occasional small upgrades in the electronics). The older instrument has been measuring the PG quasi-continuously since 1962 and the newer one has been recording the PG since 1998. The two instruments are practically at the same place installed 5 m from each other. Basically, this dataset contains the data measured by the older instrument, however, at times when there were no data available from the older instrument, data from the newer apparatus were used. The operation principle is the same for both devices. Radioactive material is used which equalizes the atmospheric potential over the lowest 1 m thick air layer so that the potential difference between the sensing and grounded electrodes at ground level is the PG itself. Zero signal offset was determined daily and the instrument was calibrated in the ±250 V/m range weekly whenever it was possible. The instrument has a measuring range of −300 V/m to 300 V/m. The data were recorded photographically by a sensitive galvanometer. Detailed characteristics of the instruments and the applied calibration technique as well as links to original data publications can be found in Bór et al., 2020 and the references therein. Originally, hourly PG averages were obtained from the photographical records via manual evaluation with an uncertainty of ±10 V/m (Buzás et al., 2022). However, by means of digital image processing techniques, PG values measured at NCK were determined with a temporal resolution of 42 seconds and a ±3 V/m uncertainty (Magos et al., 2022). During the data digitization, archive PG data from 1999 to 2009 recorded on photo papers were used. First of all, the photo paper rolls were scanned into a raster image. After that, a locally-developed image processing algorithm extracted the digital PG value from the scanned images. This dataset contains these data, which have higher temporal resolution compared to the originally extracted PG data in hourly resolution. Please note, however, that owing to the characteristics of the algorithm used during the digitization, PG values with a large absolute magnitude are often missed by the algorithm as the it was optimized for fair-weather PG values that have a smaller absolute magnitude. On-site measurements of temperature, total rainfall, relative humidity, resultant wind direction and speed, and global solar radiation after the year of 2000 with a temporal resolution of 10 minutes can be found in the Buzás et al., 2022 dataset in Pangaea. This dataset also contains PG values which have been corrected for the time-dependent bias caused by the electrostatic shielding effect of trees that were growing up not far from the measuring instrument over the decades. Note that this shielding effect largely dominates the long-term trends in the uncorrected data, so the original PG data must be interpreted with care. The uncertainty of the conversion is also provided. This uncertainty arises from unexact information on the age and growth rate of different trees near the measuring instrument and from the ±3 V/m uncertainty of the image digitization. Detailed explanation of the correction can be found in Buzás et al., 2021