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<jats:p> Scalar magnetic surveying using unmanned aerial vehicle (UAV) platforms is slowly gaining momentum within geophysical applications. So far, only a handful of studies have dealt with UAV-towed scalar field measurements, while even fewer have considered towed scalar difference measurements (or gradients). In this paper, we demonstrate the possibilities and benefits of deploying precisely positioned noise-minimized UAV-towed scalar transverse horizontal difference (THD) measurements for mineral exploration purposes. UAV-towed gradiometry bird data are presented from the Nautanen area in northern Sweden and compared with ground magnetic surveys. This area is known for its iron oxide copper-gold mineralizations. The UAV survey spans a total area of 2.5 km<jats:sup>2</jats:sup>. It was covered using an average line spacing of 30 m and a constant flight altitude above ground level of 30 m. High-quality scalar total-field and THD data were collected with a dynamic noise level of the raw scalar data of about ±0.05 nT. Comparison with the ground magnetic data shows a strong correlation between magnetic anomaly lows and highs across the survey areas. A map with new structural information is presented based on subtle magnetic structures identified in discrete derivatives of the total magnetic intensity anomaly and THD data. Such systems may replace high-quality heliborne systems and reduce costs of the geophysical exploration phase. However, mapping with UAV-towed systems is not straightforward. With typical UAV flight speeds of only 10–12 m/s, the wind often disturbs the 3D attitude of the bird during flights. Hence, advanced processing is required to obtain the intended gradients. Similar challenges are less important in surveying, where the survey speed often greatly exceeds the wind speed. </jats:p>

<jats:p> Since the 1950s, Tolles-Lawson-based aeromagnetic compensation methods have been used to separate an aircraft's magnetic signal from signal associated with ground geologic and cultural features. This is done by performing a high-altitude figure-of-merit (FOM) flight and fitting the band-pass-filtered magnetic data to determine compensation parameters. This paper describes a supervised hybrid recurrent neural network (HRNN) algorithm trained on low-altitude survey data to perform aeromagnetic compensation. The proposed HRNN attitude compensation method can be employed for aeromagnetic surveys where traditional FOM and compensation are not possible. It has particular relevance for surveying via uninhabited aircraft systems (UAS). Firstly, the HRNN was tested on data from a fixed-wing airplane survey, and the results were compared to hardware-based compensation results. The standard deviation of the difference between the two methods for magnetic attitude correction (MAC) was 0.1 nT for the training region and 0.4 nT for the application region, respectively. Secondly, a UAS FOM flight at the highest permitted altitude in Canada, 120 m above ground level, showed similar improvement ratios for software-based least squares (LS) and the proposed HRNN algorithm of 3.5 and 2.6, respectively. The percent change and deviation in differences in MACs from LS to HRNN was 0.0% and 0.9 nT across small-box loops and –2.7% and 0.4 nT across large-box loops. Finally, LS and the proposed HRNN algorithm were applied to a 50 m altitude UAS data set for which no FOM flight was possible. LS did not successfully model aircraft noise, whereas the HRNN demonstrated effective removal of the magnetic signal due to aircraft attitude variations. The modeled HRNN MAC had a standard deviation of 2.4 nT. </jats:p>

<jats:p> Full-waveform inversion (FWI) is firmly established within our industry as a powerful velocity model building tool. FWI carries significant theoretical advantages over conventional velocity model building methods such as refraction and reflection tomography. Specifically, by solving a nonlinear inverse problem through the wave equation, FWI is able to recover a broadband velocity model containing both high and low spatial wavenumbers, thus extending the approximation of residual moveout correction inherent in traditional velocity model building approaches. Moreover, FWI is capable of inverting information from the entire wavefield (i.e., early arrivals, reflections, refractions, and multiple energy) rather than from a subset as in conventional approaches (i.e., first break and primary reflections), thereby availing itself of more information to better constrain its model estimate. However, these theoretical benefits cannot be realized easily in practice because various complexities of real seismic data often conspire to violate algorithmic assumptions, leading to unsatisfactory results. Dynamic matching FWI (DMFWI) is a newly developed algorithm that solves an inversion problem that maximizes the cross correlation of two dynamically matched data sets — one recorded and the other synthetic. Dynamic matching of the two data sets de-emphasizes the amplitude impact, which allows the algorithm to focus on minimizing their kinematic differences rather than amplitude in the data-fitting process. The multichannel correlation makes the algorithm robust for data with low signal-to-noise ratio. Applications of DMFWI across different types of acquisition and geologic settings demonstrate that this novel FWI approach can resolve complex velocity errors and provide high-quality migrated images that exhibit a high degree of geologic plausibility. Additionally, reflectivity images can be obtained in a straightforward manner as natural byproducts through computation of the directional derivative of the inverted FWI velocity models. </jats:p>

<jats:p> Welcome to the latest installment of Geophysics Bright Spots. There are a number of interesting research articles in the last two issues of Geophysics. Here is a list of what piqued the editors' interests. </jats:p>

<jats:p> Giant Fields of the Decade: 2010 to 2020, by Charles Sternbach, Robert Merrill, and John Dolson, 2021. Planetary Geoscience, by Harry McSween, Jeffrey Moersch, Devon Burr, William Dunne, Joshua Emery, Linda Kah, and Molly McCanta, 2019. </jats:p>

<jats:p> SEG Board of Directors and Executive Committee actions in November and December 2022. </jats:p>

<jats:p> Applications for Active membership have been received from the candidates listed herein. </jats:p>

<jats:p> The geophysical community has lost one of its brightest lights with the passing of Norman Bleistein, university emeritus professor at the Colorado School of Mines (CSM). </jats:p>

<jats:p> Mining geophysicist Richard “Dutch” Van Blaricom passed away in December 2020 in Spokane, Washington, after a brief illness. </jats:p>

<jats:p> The Meetings Calendar chronologically lists professional events of interest to SEG members and means by which further information can be obtained. </jats:p>