The compensation of magnetic and electromagnetic interference generated by drones is one of the main problems related to drone-borne magnetometry. The simplest solution is to suspend the magnetometer at a certain distance from the drone. However, this choice may compromise the flight stability or introduce periodic data variations generated by the oscillations of the magnetometer. We studied this problem by conducting two drone-borne magnetic surveys using a prototype system based on a cesium-vapor magnetometer with a 1000 Hz sampling frequency. First, the magnetometer was fixed to the drone landing-sled (at 0.5 m from the rotors), and then it was suspended 3 m below the drone. These two configurations illustrate endmembers of the possible solutions, favoring the stability of the system during flight or the minimization of the mobile platform noise. Drone-generated noise was filtered according to a CWT analysis, and both the spectral characteristics and the modelled source parameters resulted analogously to that of a ground magnetic dataset in the same area, which were here taken as a control dataset. This study demonstrates that careful processing can return high quality drone-borne data using both flight configurations. The optimal flight solution can be chosen depending on the survey target and flight conditions.

A new method for inversion of potential fields is developed using a depth-weighting function specifically designed for fields related to complex source distributions. Such a weighting function is determined from an analysis of the field that precedes the inversion itself. The algorithm is self-consistent, meaning that the weighting used in the inversion is directly deduced from the scaling properties of the field. Hence, the algorithm is based on two steps: (1) estimation of the locally homogeneous degree of the field in a 3D domain of the harmonic region and (2) inversion of the data using a specific weighting function with a 3D variable exponent. A multiscale data set is first formed by upward continuation of the original data. Local homogeneity and a multihomogeneous model are then assumed, and a system built on the scaling function is solved at each point of the multiscale data set, yielding a multiscale set of local-homogeneity degrees of the field. Then, the estimated homogeneity degree is associated to the model weighting function in the source volume. Tests on synthetic data show that the generalization of the depth weighting to a 3D function and the proposed two-step algorithm has great potential to improve the quality of the solution. The gravity field of a polyhedron is inverted yielding a realistic reconstruction of the whole body, including the bottom surface. The inversion of the aeromagnetic real data set, from the Mt. Vulture area, also yields a good and geologically consistent reconstruction of the complex source distribution.

We have developed a deep-learning method based on the neural network of the feedforward type to estimate the depth to the basement from potential fields. The data used to train and test the network are related to the Bishop synthetic model. A trial-and-error approach was used to find the hyperparameters that have the best compromise between performance and computation time. The training was organized by associating the depth values of the basement to the data through a moving window, running along profiles in the north–south and east–west directions. In this way, we generated a set of approximately 296,980 examples. We verified the robustness of the trained net by carrying out a test related to another synthetic model, extracted from the Himalaya digital elevation model. The inherent ambiguity of the problem led us to test two hypotheses for the estimation of the basement depth, the first related to a priori information on the density contrast and the shallowest depth and the second assuming the knowledge of the depth at least at two points, but not that of the density contrast. In these cases, our data-driven approach yielded interesting results leading to estimate the maximum depth in the first case and the density contrast in the second one. We finally applied the method to the isostatic anomaly of the Yucca Flat sedimentary basin, Nevada. The results are consistent with previous interpretations of the area, which were based on gravity inversion methods.

The integration of miniaturized magnetometers with Unmanned Aerial Vehicles (UAVs) has revolutionized magnetic surveying, offering flexible, high-resolution, and cost-effective solutions for geophysical applications also in remote areas. This study presents a comparative analysis of two configurations using UAV-borne scalar magnetometers through several surveys conducted in the Altopiano di Verteglia (Southern Italy), chosen as a test-site since buried pipes are present. The two systems differ significantly in sensor–platform arrangement, noise sensitivity, and flight configuration. Specifically, the first employs the MagNimbus magnetometer with two sensors rigidly attached on two masts at fixed distances, respectively, above and below the UAV, enabling the vertical gradient estimation while increasing noise due to proximity to the platform. The second involves the use of the MagArrow magnetometer suspended at 3 m below the UAV through non-rigid ropes, which benefits from minimal electromagnetic interference but suffers from oscillation-related instability. The retrieved magnetic anomaly maps effectively indicate the location and orientation of buried pipes within the studied area. Our comparative analysis emphasizes the trade-offs between the two systems: the MagNimbus-based configuration offers greater stability and operational efficiency, whereas the MagArrow-based one provides cleaner signals, which deteriorate with the vertical gradient computation. This work underscores the need to optimize UAV-magnetometer configurations based on environmental, operational, and survey-specific constraints to maximize data quality in drone-borne magnetic investigations.

The potato (Solanum tuberosum) is one of the most widespread and cultivated Solanaceae in the world. To overcome the increasing problems of intensive cultivation and climate change, finding new strategies to guarantee the needs of today and future generations has become mandatory. The use of biostimulants based on Trichoderma spp. can be an excellent alternative to reduce the use of pesticides, as well as to mitigate the effects of biotic and abiotic stresses. In this study we evaluated the effects of a new bioformulation containing two Trichoderma strains on potato growth and yield. Trichoderma strains were characterised morphologically and molecularly. Application of the new bioformulate was able to promote potato plant growth and caused a significant increase in plant fresh (+107%) and dry weight (+74%), and potato tuber fresh weight (+37%) and number (+41%), and it also improved potato yield (+36%). These findings suggest that the bioformulation is a viable alternative to reduce pesticide use and mitigate biotic and abiotic stress in potato cultivation.

We have established a workflow for a multiorder sequential joint inversion (MOSJI) of gravity and gravity gradients, that aims at modeling vertically stacked sources in various geological scenarios. We consider the joint inversion of the gravity data and one of the $h$ th-order derivatives of the gravity data. The first step involves separate inversions, which are fundamental to fully exploit the different wavelength-content of the two quantities to invert. The joint inversion is warranted by using the scheme of a sequential joint inversion with a cross-gradient constraint. The algorithm is able to exploit different types of a priori information, such as compactness and inhomogeneous model-weighting function. First, we test this approach on a realistic synthetic model from the SEg Advanced Modeling (SEAM) Phase I model, involving salt and mother salt structures. Then, we consider a synthetic model containing either shallower or deeper karst cavities. These tests produced a better modeling of both shallower and deeper sources, when compared to the separate unconstrained inversions. Thanks to these good results, we apply our method to a real case for cavity detection in Southern Spain. The method shows an accurate modeling of the expected sources. In all the aforementioned tests, we obtain a strong decrease of the cross-gradient values and a meaningful linearization in the scatter plots of physical parameters, both indicating the good performance of the joint inversion.