Magnetic Field Data Research Articles

How Magnetic Reconnection May Affect the Coherence of Interplanetary Coronal Mass Ejections

On 2020 April 19–20, a solar ejection was seen by spacecraft in a radial alignment that included Solar Orbiter and Wind. The ejection contained a magnetic flux rope where magnetic field and plasma parameters were well correlated between spacecraft. This structure is called an “unperturbed magnetic flux rope” (UMFR). Ahead of the UMFR is a portion of the ejection (not sheath) that is referred to as “upstream” (US). We focus on the US and inquire why the correlation is so much weaker there. Specifically, we analyze data collected by Solar Orbiter at 0.81 au and Wind at L1. We show that a plausible cause for the lack of coherence in the US is a combination of front erosion and internal reconnection occurring there. Front erosion is inferred from an analysis of azimuthal magnetic flux balance in the UMFR. In the present case, we contend that the US, rather than the UMFR, is the source of the eroded field lines. The presence of erosion is supported further by a direct comparison of the magnetic field data at both spacecraft that shows, in particular, a massive shrinkage of the front portion of the US. Internal reconnection is also happening at thin current sheets inside the US. Strong nonradial flows are reconfiguring the structure. As a result of these reconnection processes, a whole section of the US is disrupted and field lines move down the flanks of the ejection and out of view of Wind.

Copper(II) halide complexes of aminopyridines: Synthesis, structure, and magnetic behavior of neutral compounds of 5-IAP: (5-IAP)2CuCl2·H2O, [(5-IAP)2CuBr2]2, (5-IAP)2CuBr2 and (5-IAP)3CuCl2·nH2O (5IAP = 2-amino-5-iodopyridine)

Reaction of 2-amino-5-iodopyridine (5IAP) with copper(II) bromide or chloride in alcoholic solution led to four coordination complexes: [(5-IAP)2CuCl2](H2O) (1), (5-IAP)2CuBr2 (2), [(5-IAP)2CuBr2]2 (3) and [(5-IAP)3CuCl2](H2O)n (4). The compounds were characterized by single crystal X-ray diffraction and variable temperature magnetic susceptibility measurements. 1 crystallizes as monomeric units with the 5IAP ligands in the syn-conformation and one lattice water molecule. 2 also exhibits syn-conformation 5IAP ligands, but forms a common bromide bibridged dimer structure. 3 is a conformational polymorph of 2 with anti-oriented 5IAP ligands. Long Cu⋯Br contacts link the molecules into chains. 4 crystallizes with 1.67 lattice water molecules, disordered over three positions. All four compounds exhibit strong halogen bonding. Variable field and temperature magnetic data were obtained on 1–3. 1 is paramagnetic, with no indication of magnetic exchange down to 1.8 K. 2 is well modeled as an antiferromagnetic dimer (J/kB = −22 K) with significant antiferromagnetic interactions between the dimers. 3 is well modeled as a uniform antiferromagnetic chain (J/kB = −6 K) with weak interchain interactions.

Forcing of slow density waves in the C ring by Saturn's quasi-toroidal normal modes

We calculate Saturn's quasi-toroidal normal modes of azimuthal order three for several candidate models for the interior stratification. We propose that these oscillations are responsible for exciting a class of slowly propagating spiral density waves observed in the C ring, possessing the same azimuthal symmetry and pattern speeds, that cluster near the rotation rate of the planet. Both prograde and retrograde propagation relative to the planetary rotation are observed. We find that we can construct interior models for which the pattern speeds of the normal modes align with those of a majority of the slow density waves. Excitation of the retrograde density waves requires retrograde toroidal modes with matching pattern speeds. We find that in order to support such modes, the interior model must include a layer of positive static stability in the molecular hydrogen envelope. The model that performs best places the peak of the static stability at a fractional radius of 0.85, near the radius of the semi-conductive layer where Saturn's zonal jets are inferred to decay. The properties of the toroidal modes are less sensitive to the stratification in the deep interior, hence our results neither confirm nor contradict the presence of a dilute core. When differential rotation is included in the form of zonal winds based on analysis of Cassini gravity and magnetic field data, we find that several toroidal modes become inertially unstable, raising the possibility that this instability is the principal mechanism for their excitation. However, while we are able to construct interior models that align the pattern speeds of a few unstable retrograde modes with those of prominent density waves in the ring, the same models fail with respect to the unstable prograde modes. Consequently, the significance of the instability mechanism remains an open question. Our results suggest that the interaction of Saturn's quasi-toroidal modes with the C ring provides an important source of new, complementary constraints on the planet's internal stratification and that further study of these modes is warranted.

Machinery Value Estimation Method Based on IIoT System Utilizing 1D-CNN Model for Low Sampling Rate Vibration Signals From MEMS

Accurately estimating the value of an equipment is a significant challenge in the industrial environment. Conventional methods mainly considered discounting the value over time, but they are limited in that they do not consider the status of individual equipment. Recent developments in Industrial Internet of Things (IIoT) and AI technologies have opened up the possibility of real-time remote monitoring on the status of machinery, thus providing an opportunity to more accurately estimate the value of machine equipments. In this study, we designed a sensor that can acquire the vibration and magnetic field data of an equipment, with which we proposed a one-dimensional convolutional neural network that can classify the status of machinery based on the data obtained by the designed sensor. In addition, based on the results of the classification model, the cumulative fatigue of equipment was predicted using the Pålmgren-Miner’s linear damage rule, with which we proposed a model for estimating the value of movable property based on the cumulative fatigue.

A geophysical investigation of the urban-expanding area over the seismologically active Dahshour region, Egypt

Western Desert region between Cairo and Fayoum in Egypt is a geologically interesting area due to its active seismicity status and the continuous bidirectional urban expansions ongoing northward and southward growth. This work aims to develop a geological model of the region by combining magnetic intensity data (aeromagnetic) and potential field data (airborne gravity) with magnetotelluric (MT) data to offer decision-makers an additional evaluation tool for long-term future planning. Applying a number of filters to aerial gravity and magnetic data allowed us to calculate the subsurface density distribution and structure of the area between Cairo and Fayoum. In addition, the surface of the crust was imaged down to a depth of 6 km using a 3-D inversion of gravity data. Magnetotelluric data were gathered at eight locations over the NS-extended Cairo-Fayoum Road profile. MT data were inverted in 2-D to generate a resistivity model that defines the subsurface structure of the researched region. The subsurface geometry in the MT-derived resistivity model agrees with the results of airborne potential measurements, and no consideration was given to any faults that may have been undetected from the potential maps. Long-term seismological observations indicated that the likely active sources of earthquakes are limited to the recognized main faults and that the ongoing “1185 buildings” construction project did not and will not cause unanticipated seismicity. The paper concludes that urban growth is secure so long as the earthquake codes are rigorously considered while planning civil projects. The northern study region must be regularly monitored for induced seismicity. On the Cairo-Fayoum Road's western side, the middle sector of the study area is the safest place for future civil developments.

Jets and Mirror Mode Waves in Earth's Magnetosheath.

Magnetosheath jets are localized plasma structures with high dynamic pressure which are frequently observed downstream of the Earth's bow shock. In this work we analyze Magnetospheric MultiScale magnetic field and plasma data and show that jets can be found in the quasi-perpendicular magnetosheath in regions permeated by Mirror mode waves (MMWs). We show that structures identified as jets by their enhanced dynamic pressure can have very different internal structure, with variable signatures in magnetic field magnitude and components, velocity, and density and can be associated to ion distribution functions of various types. This suggests that jets observed in the quasi-perpendicular magnetosheath are generated by different mechanisms. We find that jets can be related to traveling foreshocks, flux transfer events, and some have MMWs inside them. Our results suggest that some jets have a local source and their formation does not depend on upstream structures. We find that different types of ion distributions can exist inside the jets, while in some cases anisotropic distributions are present, in others counterstreaming distributions exist. We also show that for jets with MMWs inside them, ion distributions can be modulated. This highlights the importance of using ion distributions to identify and classify different types of jets.

A dataset of field-aligned Poynting flux in the dayside polar cap of the northern hemisphere during 2014-2016

The transport of electromagnetic energy in the polar upper atmosphere and its spatial and temporal distribution are current hot issues and important topics for studying the polar upper atmospheric environment and space weather forecasting models. Researchers usually adopted the observations from low-altitude polar-orbiting satellites to estimate the magnitude and direction of electromagnetic energy. The continuous accumulation of electromagnetic energy data will be beneficial to the study on the transmission and variation characteristics of electromagnetic energy in the polar ionosphere in China. In this paper, we estimated the distribution of field-aligned Poynting flux, horizontal convection electric field and magnetic field perturbation in the dayside polar cap based on the plasma velocity and magnetic field data of DMSP F17 satellite in the northern hemisphere during 2014-2016. The initial DMSP F17 satellite data included in this dataset are of high quality. The estimation process is rigorous and reasonable, and the data records are complete. The dataset can provide important data support for the study on energy transport and dissipation in the ionosphere/thermosphere in the polar region.

Statistical Decomposition and Machine Learning to Clean In Situ Spaceflight Magnetic Field Measurements

AbstractRobust in situ magnetic field measurements are critical to understanding the various mechanisms that couple mass, momentum, and energy throughout our solar system. However, the spacecraft on which magnetometers are often deployed contaminate the magnetic field measurements via onboard subsystems including reaction wheels and magnetorquers. Two magnetometers can be deployed at different distances from the spacecraft to determine an approximation of the interfering field for subsequent removal, but constant data streams from both magnetometers can be impractical due to power and telemetry limitations. Here we propose a method to identify and remove time‐varying magnetic interference from sources such as reaction wheels using statistical decomposition and convolutional neural networks, providing high‐fidelity magnetic field data even in cases where dual‐sensor measurements are not constantly available. For example, a measurement interval from the Parker Solar Probe outboard magnetometer experienced a 95.1% reduction in reaction wheel interference following application of the proposed technique.

Data-constrained Solar Modeling with GX Simulator

To facilitate the study of solar flares and active regions, we have created a modeling framework, the freely distributed GX Simulator IDL package, that combines 3D magnetic and plasma structures with thermal and nonthermal models of the chromosphere, transition region, and corona. Its object-based modular architecture, which runs on Windows, Mac, and Unix/Linux platforms, offers the ability to either import 3D density and temperature distribution models, or to assign numerically defined coronal or chromospheric temperatures and densities, or their distributions, to each individual voxel. GX Simulator can apply parametric heating models involving average properties of the magnetic field lines crossing a given voxel, as well as compute and investigate the spatial and spectral properties of radio, (sub)millimeter, EUV, and X-ray emissions calculated from the model, and quantitatively compare them with observations. The package includes a fully automatic model production pipeline that, based on minimal users input, downloads the required SDO/HMI vector magnetic field data, performs potential or nonlinear force-free field extrapolations, populates the magnetic field skeleton with parameterized heated plasma coronal models that assume either steady-state or impulsive plasma heating, and generates non-LTE density and temperature distribution models of the chromosphere that are constrained by photospheric measurements. The standardized models produced by this pipeline may be further customized through specialized IDL scripts, or a set of interactive tools provided by the graphical user interface. Here, we describe the GX Simulator framework and its applications.

Estimating The Location of a Buried Body from Magnetic Anomaly Through Normalized Full Gradient: A Case Study from The Sapinuwa Ancient City, Turkey

In geophysics, whether the structure is close to the surface or deeper, information about the depth and location of the structure can be obtained by using magnetic data. The importance of this study is an example of the application of the Normalized Full Gradient (NFG) method to an archaeological site to find the depth and location of structures that cause magnetic anomalies collected at the archaeological site. One of the parameters affecting the shape and size of the magnetic anomaly is the depth of the source causing the anomaly. For this reason, it is significant to determine the location of the source correctly. One of the methods used to determine the depth of structure using magnetic field data is NFG technique. In the application of downward analytical continuation, distortions due to the passage of mass depth occur and this method invalidates this. The NFG technique was tested on anomalies caused by prism-shaped synthetic models. Test studies on synthetic models with the NFG technique have yielded satisfactory findings. Based on the findings, the NFG technique was carried out to the real magnetic anomaly collected in the ancient city of Sapinuwa. The findings were compared with the building remains unearthed as a result of the proposed trench excavations. The obtained results have shown satisfactory results.

Distributed Poloidal Magnetic Field Measurement in Tokamaks Using Polarization-Sensitive Reflectometric Fiber Optic Sensor.

Determination of the poloidal magnetic field distribution in tokamaks is of prime importance for the successful operation of tokamaks. In this paper, we propose a polarization-sensitive reflectometry-based optical fiber sensor for measuring the spatial distribution of the poloidal magnetic field in tokamaks. The measurement method exploits the Rayleigh backscattering and Faraday magneto-optic effect in optical fibers. The former is an intrinsic property of optical fibers and enables distributed polarization measurements, while the latter arises in the presence of a magnetic field parallel to the optical fiber axis and rotates the polarization state of the light. When an optical fiber is looped around a toroidal section of the vacuum vessel, the local polarization rotation of the light is proportional to the local poloidal magnetic field in the tokamak. The proposed method is discussed theoretically and experimentally using the results from JET. The obtained magnetic field measurement shows a good agreement with that of the internal discrete coils. A potential solution to recover the magnetic field data from the noise-affected region of the optical measurement is proposed and is demonstrated through simulations using the JET magnetic field configuration.

Banana Current in the Inner Magnetosphere Observed by Van Allen Probes

AbstractBy using the magnetic field data from Van Allen Probes, we analyzed the distribution characteristics of the electromagnetic environment in the inner magnetosphere on different Dst* index and magnetic local time. Our results show that for the response of different current systems, the dawn‐dusk and noon‐midnight asymmetry distribution of the residual magnetic field δB increases with Dst* index. When Dst* < −60 nT, a “banana”‐shaped geomagnetic field negative disturbance peak region appears in the sector from midnight to dusk. Then, we obtained the azimuthal current density and found the asymmetric internal eastward and external westward ring current. Through the vector analysis of three‐dimensional current density, the current density vector distribution in the magnetic equatorial plane is completely displayed for the first time, which directly proves the existence of banana current near r = 3.0–4.0 RE during strong geomagnetic storms.

Estimating intermittency significance by means of surrogate data: implications for solar wind turbulence

Introduction: Intermittency is a property of turbulent astrophysical plasmas, such as the solar wind, that implies irregularity and fragmentation, leading to non-uniformity in the transfer rate of energy carried by nonlinear structures from large to small scales. We evaluated the intermittency level of the turbulent magnetic field measured by the Parker Solar Probe (PSP) in the slow solar wind in the proximity of the Sun during the probe’s first close encounter.Methods: A quantitative measure of intermittency could be deduced from the normalized fourth-order moment of the probability distribution functions, the flatness parameter. We calculated the flatness of the magnetic field data collected by the PSP between 1 and 9 November 2018. We observed that when dividing the data into contiguous time intervals of various lengths, ranging from 3 to 24 hours, the flatness computed for individual intervals differed significantly, suggesting a variation in intermittency from “quieter” to more intermittent intervals. In order to quantify this variability, we applied an elaborate statistical test tailored to identify nonlinear dynamics in a time series. Our approach is based on evaluating the flatness of a set of surrogate data built from the original PSP data in such a way that all nonlinear correlations contained in the dynamics of the signal are eliminated. Nevertheless, the surrogate data are otherwise consistent with the “underlying” linear process, i.e., the null hypothesis that we want to falsify. If a discriminating statistic for the original signal, such as the flatness parameter, is found to be significantly different from that of the ensemble of surrogates, then the null hypothesis is not valid, and we can conclude that the computed flatness reliably reflects the intermittency level of the underlying nonlinear processes.Results and discussion: We determined that the non-stationarity of the time series strongly influences the flatness of both the data and the surrogates and that the null hypothesis cannot be falsified. A global fit of the structure functions revealed a decrease in flatness at scales smaller than a few seconds: intermittency is reduced in this scale range. This behavior was mirrored by the spectral analysis, which was suggestive of an acceleration of the energy cascade at the high-frequency end of the inertial regime.

On-Orbit Magnetometer Data Calibration Using Genetic Algorithm and Interchangeability of the Calibration Parameters

Magnetometers are important sensors with applications in the attitude determination and control systems of satellites. CubeSats have certain limitations related to power, mass, and volume. Due to this, CubeSat magnetometers are not separated from other electrical circuits inside the satellite. Thus, it is important to calibrate the magnetometer, simulating operating conditions while the satellite is running before the launch. However, due to the limited facilities, not every CubeSat is able to calibrate its magnetometers properly on the ground. This study focuses on the calibration of on-orbit magnetometer data observed by BIRDS-3 CubeSats with a genetic algorithm. High oscillations in the total magnetic field were found in the on-orbit magnetic field data measured by magnetometers inside BIRDS-3 CubeSats. Nine unknowns, scaling factors, non-orthogonal angles, and offsets are identified with the genetic algorithm. This paper discusses the factors that affect the high oscillations in the measured total magnetic field data. For the calibration, we used magnetic field data similar to those of a model magnetic field, as the deviation is smaller. This paper presents the accuracy of determining unknowns using the genetic algorithm, as well as the interchangeability of the answers with additional orbit data from the same satellite. This method can be used in the future to calibrate magnetometers inside CubeSats before or after launch.

Small-scale EUV features as the drivers of coronal upflows in the quiet Sun

Context. Coronal upflows in the quiet Sun are seen in a wide range of features, including jets and filament eruptions. The in situ measurements from Parker Solar Probe within ≈0.2 au have demonstrated that the solar wind is highly structured, showing abrupt and near-ubiquitous magnetic field reversals (i.e., switchbacks) on different timescales. The source of these structures has been associated with supergranular structures on the solar disc. This raises the question of whether there are additional small coronal features that contribute energy to the corona and produce plasma that potentially feeds into the solar wind. Aims. During the Solar Orbiter first science perihelion, high-resolution images of the solar corona were recorded using the Extreme Ultraviolet High Resolution Imager (HRIEUV) from the Extreme Ultraviolet Imager (EUI). The Hinode spacecraft was also observing at the same location providing coronal spectroscopic measurements. Combining the two datasets allows us to determine the cause of the weak upflows observed in the quiet Sun and the associated activity. Methods. We used a multi-spacecraft approach to characterise regions of upflows. The upflows were identified in the Fe XII emission line by the Hinode EUV Imaging Spectrometer (EIS). We then used imaging data from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory (SDO/AIA) and the High Resolution Imagers (HRI) from EUI on board the Solar Orbiter to identify coronal features and magnetic field data from the SDO Helioseismic and Magnetic Imager (HMI). Interface Region Imaging Spectrograph (IRIS) observations were also used to understand the photospheric and chromospheric driving mechanisms. Results. We have identified two regions of coronal upflows in the quiet Sun, with respective sizes and lifetimes of (20 Mm2, 20 min) and (180 Mm2, several hours), which are contrasting dynamic events. Both examples show weak flux cancellation, indicating that the source of the upflows and enhancements is related to the magnetic field changes. The first event, a larger upflow region, shows velocities of up to −8.6 km s−1 at the footpoint of a complex loop structure. We observe several distinct extreme ultraviolet (EUV) features including frequent loop brightenings and plasma blobs travelling along closed coronal loops. The second upflow region has velocities of up to −7.2 km s−1. Within it, a complex EUV feature that lasts for about 20 min can be seen. This main feature has several substructures. During its appearance, a clear mini-filament eruption takes place at its location, before the EUV feature disappears. Conclusions. Two features, with contrasting properties, show upflows with comparable magnitudes. The first event, a complex loop structure, shares several similarities with active region upflows. The second one, a complex small-scale feature that could not have been well resolved with previous instruments, triggered a cascade of events, including a mini-filament that lead to a measurable upflow. This is remarkable for an EUV feature that many instruments can barely resolve. The complexity of the two events, including small loop brightenings and travelling plasma blobs for the first and EUV small-scale loops and mini-filament for the second one would not have been identifiable as the sources of upflow without an instrument with the spatial resolution of HRIEUV at this distance to the Sun. These results reinforce the importance of the smallest-scale features in the Sun and their potential relevance for and impact on the solar corona and the solar wind.

Magnetic Field Simulation of Reactor Based on Deep Neural Networks

In the context of constructing digital power grid, there has been significant attention on the methods to accurately and promptly obtain the physical field information of power equipment. The numerical methods, such as finite element analysis (FEA), are limited by offline computation and cannot meet the safety and timeliness requirements of the power grid. In this letter, a method based on deep neural networks (DNN) is proposed for rapidly predicting the magnetic field distribution of reactors. After training on magnetic field data generated by FEA simulation, the DNN takes the reactor current value as input and gets the simulation results processed using principal component analysis (PCA) within 1 s. The trained DNN has a mean absolute percentage error (MAPE) of 0.012% in predicting the magnetic field distribution of reactor. This method demonstrates the viability of replacing traditional simulation methods with DNN to expand the applications of digital twin in the power grid.

Adaptive backsliding control method of permanent magnet synchronous motor based on RBF

The adaptive backstepping control method of permanent magnet motor has the problems of complicated coordinate transformation process and high position tracking error. Based on this, an adaptive backstepping control method of permanent magnet synchronous motor based on RBF is proposed. According to the principle of electrical machinery, the electromagnetic wave and magnetic field data are obtained, and the mathematical model of permanent magnet synchronous motor is constructed. Under the condition of keeping the resultant magnetomotive force after coordinate transformation unchanged, the structure of motor torque neural network is established by RBF method, and the coordinate transformation process is optimized. Through the compensation control strategy, the adaptive backstepping control mode is designed to realize the adaptive backstepping control of permanent magnet synchronous motor. The simulation results show that the position tracking error of the proposed method is 4.549 mm when the running time is 7 s and 43.699 mm when the running time is 14 s, which proves that the adaptive backstepping control effect of the proposed method is better.

A Machine Learning–Based Approach to Time-series Wave Identification in the Solar Wind

The Wind spacecraft has yielded several decades of high-resolution magnetic field data, a large fraction of which displays small-scale structures. In particular, the solar wind is full of wavelike fluctuations that appear in both the field magnitude and its components. The nature of these fluctuations can be tied to the properties of other structures in the solar wind, such as shocks, that have implications for the time evolution of the solar wind. As such, having a large collection of wave events would facilitate further study of the effects that these fluctuations have on solar wind evolution. Given the large volume of magnetic field data available, machine learning is the most practical approach to classifying the myriad small-scale structures observed. To this end, a subset of Wind data is labeled and used as a training set for a multibranch 1D convolutional neural network aimed at classifying circularly polarized wave modes. Using this algorithm, a preliminary statistical study of 1 yr of data is performed, yielding about 300,000 wave intervals out of about 5,000,000 solar wind intervals. The wave intervals come about more often in the fast solar wind and at higher temperatures, and the number of waves per day is highly periodic. This machine learning–based approach to wave detection has the potential to be a powerful, inexpensive way to catalog waves throughout decades of spacecraft data.

Application of toroidal magnet excitation and hall sensor array in magnetic leakage field detection of rail surface defect

To detect rail top surface defects, NdFeB toroidal permanent magnets were firstly used to excite the rail detection area, and the magnetic excitation direction was perpendicular to the rail direction. So that, the magnetic circuit can be analyzed, and the model was verified by finite element simulation. Secondly, the artificial rectangular defect samples were machined on the top surface of the rail, and the magnetic field data acquisition system was developed and equipped on the inspection vehicle. With that, a linear array which consisted of three-dimensional hall sensors was designed to detect the magnetic leakage field signal from the defect samples. Finally, the features of the leakage field signal in each dimension were extracted. And then, the length, width, and depth of the defects were predicted using BP neural network algorithm. The results indicate that the method can improve the excitation efficiency, and the average absolute errors of prediction for defect length, width, and depth were 1.43 mm, 1.01 mm, and 0.63 mm respectively.

Integration of magnetic and geological field data into geological mapping and rutile mineralization targets in the Minta locality (Haute-Sanaga, Cameroon)

This study combines the processing and interpretation of magnetic data with geological fieldwork. The purpose is to establish the geological map and rutile mineralization targets map of Minta area (Haute-Sanaga, Cameroon). To this, a strategic geological field survey was first conducted. It highlighted some geological structures, especially magnetite quartzites, which is considered as the potential sources of primary rutile mineralization. Several methods were applied to the magnetic data, including upward continuation (UC), vertical derivative (DZ), analytical signal (AS) and horizontal gradient magnitude (HGM). The regional-residual separation method based on the UC was used to develop the residual map which was very helpful to understand the distribution of magnetic anomalies related to rutile mineralization in this area. The structural model of the study area was established by combining the analysis of the AS and HGM maxima with the analysis of the topographic model. Since magnetic relief variation characterizes the lithological information changes according to the filtered map considered, a set of categorization processes based on the anomaly signals was applied to each of filtered maps according to the geological information sought. It is based on the interpretation of structural models, combined with previous works and the spatial distribution of geological data collected. This process produced a set of partial lithological models, which were then combined with the interpreted structural model to produce the geological map of the study area. This map also shows the spatial distribution of the various targets of potential rutile mineralization interpreted on the DZ model as rectilinear anomalies, with a signal range of 0.78–0.88 nT/m.