Systems, methods, and computer-readable media for generating accurate magnetic field and a map of a surveyed area

Abstract

A method for generating a magnetic map image of a surveyed area, comprising performing a line splitting on magnetometer data collected by a drone along drone flight lines of the surveyed area, the splitting creating spilt magnetometer data by dividing the magnetometer data into manageable segments. Creating dipped magnetometer data by removing extraneous data points from the magnetometer data, Creating: adjusted magnetometer data by adjusting the clipped magnetometer data for known geomagnetic variations and to align the clipped magnetometer data with a geographic orientation of the surveyed area. Setting difference measurements as a difference in the adjusted magnetometer data collected along intersections of the flight lines. The difference measurements being independent of base station measurements. Organizing lire adjusted magnetometer data into grid survey lines to create structured magnetometer data. Separating a signal and a noise in the structured magnetometer data, and generating the magnetic map image of the surveyed area.

Description

>s.<Our Ref No. 438230.000134SYSTEMS, METHODS, AND COMPUTER-READABLE MEDIA FOR GENERATING ACCURATE MAGNETIC FIELD AND A MAP OF A SURVEYED AREAFIELD|0001| The present disclosure generally relates to systems, methods, and computer-readable media for generating accurate magnetic field and a map of a surveyed area.BACKGROUNDJ0002| Magnetic mapping is a geophysical method used to measure variations in the Earth's magnetic field caused by the presence of different geological features. This technique is commonly used in mineral exploration, archaeology, and other purposes. The process involves the use of magnetometers which are devices that measure the strength and direction of the magnetic field at a specific location. These measurements are typically collected along a predefined flight path by an airborne system equipped with a magnetometer. The collected data is then processed and analyzed to generate a magnetic map of the surveyed area. The resulting magnetic map provides a visual representation of the magnetic anomalies in the surveyed area, which can be used to infer the underlying geological structures.(0003] Despite the advancements in magnetic mapping technologies, several challenges persist in the field. Traditional methods often require the establishment of a base station positioned along the surveyed area to record diurnal variations, which can be impractical or unfeasible in geographically challenging areas such as high mountains, swamps, deserts, polar regions, and deep ocean areas. This limitation restricts the applicability of these methods and increases the time and cost investments associated with magnetic surveying. Additionally, current technologies may not provide comprehensive correction techniques that account for al l potential sources of error. As a result, the accuracy and precision of the resulting magnetic maps may be compromised. Therefore, there Is a pressing demand for an improved method for generating magnetic maps that addresses these challenges and limitations in the prior art.SUMMARY|0004| This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identifyOur Ref No. 438230.000134 key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.[0OO5j In one aspect the present disclosure relates to method for generating a magnetic map image of a surveyed area, comprising performing a line splitting on magnetometer data collected by a drone along drone flight lines of the surveyed area, the splitting creating split magnetometer data by dividing the magnetometer data into manageable segments, performing a clipping on the split magnetometer data, the clipping creating clipped magnetometer data by removing extraneous data points from the magnetometer data, applying an adjustment to create adjusted magnetometer data by adjusting the clipped magnetometer data for known geomagnetic variations and to align the clipped magnetometer data with a geographic orientation of the surveyed area, setting difference measurements as a difference in the adjusted magnetometer data collected along intersections of the flight lines, the difference measurements being independent of base station measurements, organizing the adjusted magnetometer data into grid survey lines to create structured magnetometer data as a structured representation of the surveyed area, separating a signa) and a noise in the structured magnetometer data, and generating the magnetic map image of the surveyed area based on the separated signal in the structured magnetometer data, the magnetic map image indicating magnetic strength across the surveyed area,|()006] In embodiments of this aspect, the disclosed method according to any of the above example embodiments, wherein the clipping includes discarding portions of the magnetometer data that fall outside predetermined thresholds of magnetic field strength.[0(107] In embodiments of this aspect, the disclosed method according to any of the above example embodiments, wherein setting the difference measurements includes using data from at least two intersecting flight lines to calculate a:local anomaly in the clipped magnetometerdata.{OOOSJ In embodiments ofihis aspect, the disclosed method according to any of the above example embodiments, wherein the adjustment for the known geomagnetic variations includes applying an International Geomagnetic Reference Field (IGRI') model to the clipped magnetometer data.[0009 | In embodiments of this aspect, the disclosed method according to any of the above example embodiments, wherein perform ing the adjustment inel tides using inertial measurement unit (IM U) data to adjust the clipped magnetometer data to adjust for heading error.

[0010] In embodiments of this aspect, the disclosed method according to any of the above example embodiments, further comprising applying a polynomial fit and a directional filler to the adjusted161 .? t()3W, IOur Ref No. 438230.000134 magnetometer data to reduce an influence of temporal magnetic field variations and systematic noises.

[0011] in embodiments of this aspect, the disclosed method according to any of the above example embodiments, wherein organizing the adjusted magnetometer data into the grid survey lines includes interpolating the magnetometer data to fill gaps between the (light lines,

[0012] In embodiments of this aspect, the disclosed method according to any of the above example embodiments, wherein separating a signal and a noise in the structured magnetometer data includes applying a transform to distinguish between periodic and non-periodic components.

[0013] In embodiments of this aspect, the disclosed method according to any of the above example embodiments, wherein generating the magnetic map image includes color-coding the map to represent different ranges of magnetic field strength.

[0014] In embodiments of this aspect, the disclosed method according to any of the above example embodiments, further comprising using the generated magnetic map image to identify geological features such as mineral deposits, fault lines, or underground structures. (0015] In one aspect, the present disclosure relates to a system for generating a magnetic map image of a surveyed area, comprising a receiver configured to receive magnetometer data collected by a drone along drone Hight lines of the surveyed area, and a processor configured to perform line splitting on the received magnetometer data, the splitting creating split magnetometer data by dividing the magnetometer data into manageable segments, perform clipping on the split magnetometer data, the clipping creating clipped magnetometer data by removing extraneous data points from the magnetometer data, apply an adjustment to create adjusted magnetometer data by adjusting the clipped magnetometer data for known geomagnetic variations arid to align the clipped magnetometer data with a geographic orientation of the surveyed area, set difference measurements as a difference in the adjusted magnetometer data collected along intersections of the flight lines, the difference measurements being independent of base station measurements. organize the adjusted magnetometer data into grid survey lines to create structured magnetometer data as a structured representation of the surveyed area, separate a signal and a noise in the structured magnetometer data, and generate the magnetic map image of the surveyed area based on the separated signal in the structured magnetometer data, the magnetic map image indicating magnetic strength across the surveyed area.Our Ref No. 438230.000134

[0016] In embodiments of this aspect, the disclosed system according to any of the above example embodiments, wherein the processor is further configured to perform the clipping by discarding portions of the magnetometer data that lull outside predetermined thresholds of magnetic field strength.10017] In embodiments of this aspect, the disclosed system according to any of the above example embodiments, wherein the processor is further configured to set the difference measurements by using data from at least two intersecting flight lines to calculate a local anomaly in the clipped magnetometer data.(0018] In embodiments of this aspect, the disclosed system according to any of the above example embodiments, wherein the processor is further configured to apply an International Geomagnetic Reference Field (1GRF) model to the clipped magnetometer data for the adjustment of the known geomagnetic variations.(0019] In embodiments of this aspect, the disclosed system according to any of the above example embodiments, wherein the processor is further configured to use inertial measurement unit (IMU) data to adjust the clipped magnetometer for heading error.(0020] hi embodiments of this aspect, the disclosed system according to any of the above example embodiments, wherein the processor is further configured to apply a polynomial fit and a directional filter to the adjusted magnetometer data to reduce an influence of temporal magnetic field variations: and systematic noises.(0021 J In embodiments of this aspect, the disclosed system according to any of the above example embodiments, wherein the processor is further configured to interpolate the magnetometer data to fill gaps between the flight lines when organizing the adjusted magnetometer data into the grid survey lines.

[0022] In embodiments of this aspect, the disclosed system according to any of the above example embodiments, wherein the processor is further configured to apply a transform to the structured magnetometer data io distinguish between periodic and non-periodic components when separating a signa! and a noise.

[0023] In embodiments of this aspect, the disclosed system according to any of the above example embodiments, wherein the processor is further configured to color-code the magnetic map to represent different ranges of magnetic field strength when generating the magnetic map image.Our Rel No. 438230.000134

[0024] I n embodiments of this aspect, the d isciosed system according to any of the above example embodiments, further comprising utilizing the generated magnetic map image to identify geological features such as mineral deposits, fault lines, or underground structures.

[0025] The foregoing general description of the illustrative embodiments and the following detai led description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive.BRIEF DESCRIPTION OF THE DRAWINGS

[0026] So that the way the above-recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, brielly summarized above, may be made by reference to example embodiments, some of which are illustrated in the appended drawings, it is to be noted, however, (hat the appended drawings illustrate only example embodiments of this disclosure and are therefore not to be considered limiting of i ts scope, for the disclosure may admit to other equally effective example embodiments.

[0027] FIG. 1 illustrates a view of a drone assembly equipped with a magnetometer, according to aspects of the present disclosure.

[0028] FIG, 2 illustrates a view of the drone assembly showing the magnetometer’s sensing field of view while performing a survey, according to aspects of the present disclosure.

[0029] FIG, 3A illustrates a view of survey lines, according to aspects of the present disclosure,

[0030] FIG. 3B illustrate a view of magnetic data superimposed on the survey lines, according to aspects of the present disclosure.

[0031] FIG. 3C illustrate a view of resultant magnetic data map, according to aspects of the present d isclosure,

[0032] FIG, 4 illustrates a flowchart outlining the steps involved in collecting and processing magnetometer data for the creation of a geological survey, according to aspects of the present disclosure.

[0033] F IG, 5 illustrates a flowchart outlining a method for generating a magnetic map of a surveyed area, according to aspects of the present disclosure.

[0034] F IG. 6 illustrates a block diagram of' a system for data communication and storage, according to aspects of the present disclosure.! 61 J f iHJSi) 1Our Ref No. 438230.000134

[0035] FIG. 7 illustrates a block diagram of a computing system architecture, according to aspects of the present disclosure.DETAILED DESCRIPTIONJ0036| Various example embodiments of the present disclosure will now be described in detail with reference to the drawings. It should be noted that the relative arrangement of die components and steps, the numerical expressions, and the numerical values set forth in these example embodiments do not limit the scope of the present disclosure unless it is specifically slated otherwise. The following description of at least one example embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or its uses. Techniques, methods, and apparatus as known by one of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate, hi the examples illustrated: and discussed herein, any specific values should be interpreted to be illustrative and non-limiting. Thus, other example embodiments may have different values. Notice that similar reference numerals and letters refer to similar items in the following figures, and thus once an item is defined in one figure, it is possible that it need not be further discussed for the? following figures. Below, the example embodiments will be described with reference to the accompanying figures.[00371 The present disclosure relates to systems, methods, and computer-readable media for generating accurate magnetic field maps of surveyed: areas. In particular, the present disclosure may provide systems and methods for creating these maps using an automated an airborne system (e.g.. drone(s)) equipped with a magnetometer. The system may follow a predefined flight path over the surveyed area, collecting magnetic data that is later analyzed and post-processed to generate a high-precision magnetic map.|0038| In one example, the system may include a drone integrated with a magnetometer, a computer (including software) for analyzing and post-processing the collected magnetic data. The drone may follow a designed trajectory, collecting magnetic data along the flight path. The collected magnetic data can be processed using a dedicated software to generate the magnetic map of the surveyed area. This processing may be performed by the computer, a backend server or a combination of both.10039] The method of generating the magnetic map may involve several steps, including line splitting and clipping, international Geomagnetic Reference Field (IGR.F) correction, headingOur Re I No. 438230.000134 error correction, diurnal correction (i.e,. base station-free corrections), directional filtering, gridding the survey lines and lie lines, removal of boudinage artifacts, and signal and noise separation. These steps, when combined, can result in a comprehensive and efficient process for generating high-precision magnetic maps, particularly beneficial for magnetic navigation and surveying in geographical ly challenging areas.

[0040] The benefits of the disclosed systems and methods include the ability to perform magnetic mapping by way of an application of a comprehensive set of corrections (without die reliance on a base station positioned along the surveyed area to record diurnal variations). These benefits contribute to the generation of accurate and high-resolution magnetic maps, addressing the limitations of existing solutions and providing a novel approach to magnetic mapping.

[0041] T he disclosed technology presents a novel and efficient method for generating accurate magnetic maps of surveyed areas, offering several benefits over existing solut ions.

[0042] In one example, the technology introduces a base-station-free diurnal correction, a feature that allows for magnetic data correction in environments where seting up a base station within the surveyed area is impractical or unfeasible. This innovation can be particularly beneficial in inaccessible areas such as high mountains, swamps, deserts, polar regions, and deep ocean areas, where traditional methods are limited. By eliminating the reliance on base station measurements, the technology can reduce the time and cost investments typically associated with magnetic surveying, making the process more efficient and accessible. The technology can ensure the accuracy and precision of the resulting magnetic maps, providing a more reliable and detailed representation of the magnetic anomalies in the surveyed area.

[0043] Furthermore, the technology can include an equivalent source technique for signal and noise separation. This innovative approach can offer superior noise reduction capabilities compared to current methods, enhancing the clarity and usability of the magnetic' maps for various applications. By distinguishing between meaningful magnetic signals and noise in the data, the technology can provide a more accurate and informative depiction of the magnetic field variations in the surveyed area.

[0044] Overall, the technology offers a more efficient, cost-effective, and precise method for generating magnetic maps, particularly in challenging environments where traditional methods are limited. The integration of advanced correction techniques, along with the novel base-station-fi-eeO u r R e f N o . 438230.000134 approach, represents a substantial improvement over the suite of the art, providing a novel and inventive contribution to the Held of magnetic surveying.

[0045] The disclosed technology has potential applications across a variety of sectors. In the Held of geophysics, the technology can be used to generate high-precision magnetic maps of surveyed areas, aiding in the identification and exploration of mineral deposits, fault lines, and other geological features. The technology's ability to operate without, the reliance on base station measurements makes it particularly advantageous for surveying geographically challenging areas such as high mountains, swamps, deserts, polar regions, and deep ocean areas.]0046] In the robotics sector, the technology can be integrated into drone systems to enhance their navigation capabilities. The generated magnetic maps can serve as a reference for drone navigation, particularly in environments where GPS signals are weak or unavailable. This can be beneficial for various drone applications, including aerial photography, environmental monitoring, and disaster response.(0047] In the aviation industry, the technology can be used for aircraft navigation, especially in situations where traditional navigation systems are not reliable or available. T he generated magnetic maps can provide pilots with valuable information about the Earth’s magnetic field variations, aiding in navigation and flight planning.

[0048] In the mapping sector, the technology can be used to create detailed magnetic maps of surveyed areas. These maps can be used for various purposes, including land use planning, resource management, and scientific research. The technology's ability to generate high-precision magnetic, maps can contribute to the accuracy and reliability of these applications,|G049] For example, consider a scenario where a geological survey company is tasked with mapping the magnetic anomalies of a remote, mountainous region with the. goal of identifying potential mineral deposits. Traditional methods require the establishment of a base station positioned along the survey path to record diurnal variations, which is not feasible due to the terrain. Using the technology described, the company can deploy drones equipped with magnetometers that follow a pre-defined flight path, collecting magnetic data without the reliance on base station measurements. The data can undergo post-processing using the innovative methods outlined, correcting, for diurnal drift, IGRF. and heading errors resulting in high-precision magnetic maps. The final high-precision magnetic map can reveal detailed magnetic anomalies, enabling the company to pinpoint areas of interest for mineral exploration. T his technology not just saves16 i 311)2380 tOur Ref No. 438230.000134 time and resources but also opens up new possibilities lor surveying in previously inaccessible areas.

[0050] The methods and systems of the present disclosure will now be described with respect to the figures. Each figure provides a visual representation and schematic layout of the various components, processes, and systems involved in the disclosed technology. These figures serve to illustrate the embodiments and functionalities of the systems and methods for generating accurate magnetic field maps of surveyed areas, as well as the data communication and storage systems that support the processing and analysis of the collected magnetic data. The detailed description that follows will reference these figures to provide a clearer understanding of how the disclosed technology operates and the advantages it offers over existing solutions.[005.11 Referring to FIG. I , a perspective view of a drone assembly 1(h) is depicted. The drone assembly 100 can include a central body 102, which serves as the main structural component. Multiple drone arms 104 can be atached to the central body 102, each equipped with a propeller motor 106 and a propeller blade 108 for propulsion. Landing gear 1 10 can be connected to the central body 102, providing stability during takeoff and landing. An antenna 1 12 can extend from the central body 102 to facilitate communications with, for example, computer systems and other drones,

[0052] Suspended below the central body 102 can be a magnetometer 1 14, connected by a cable 1 18, The magnetometer 1 14 can be configured to delect magnetic fields within a magnetometer sensing field of view 120. In some cases, the magnetometer 1 14 may be a scalar magnetometer, capable of recording magnetic data while the drone assembly 100 executes a predefined Hight path. In other cases, the magnetometer 1 14 may be a different type of magnetometer suitable for collecting magnetic data.

[0053] Additionally, a sensor 1 16 can be mounted on a drone assembly, such as on the landing gear 1 10. The sensor 1 16 may assist in navigation and data collection tasks. In some aspects, the sensor 1 16 may be an inertial measurement unit (1MU) used to adiiist the magnetometer data for heading error. In other aspects, the sensor 1 16 may be a different type of sensor suitable for assistin Vs in navi Wgation and data collection,J0054] The arrangement of these components enables the drone assembly 100 to perform aerial surveys and collect magnetic data for mapping purposes. In operation, the drone assembly 100 may follow a designed trajectory, collecting magnetic data along the flight path. If the batteries ofOur Ref No. 438230.000134 the drone assembly 100 are low, and if the trajectory is too large to finish with the current batteries, the drone assembly 100 may return to a computer system (not shown) for batery replacement before continuing the trajectory. Once the trajectory is completed, the drone assembly 100 may return to the computer system , and the magnetometer 114 may save the collected datasets and stop recording.|0O55] It is noted that in embodiments, the drone assembly 100 may' include additional or different components, such as additional sensors, different types of magnetometers, or different types of antennas. For example, the drone assembly 100 may incl ude a vector magnetometer instead of or in addition to the scalar magnetometer. The drone assembly 100 may also include different types of sensors, such as a barometric pressure sensor, a temperature sensor, or a humidity sensor, to collect additional data during the survey.[00561 The computer system re ferenced herein can serve as a central hub for the drone operations rather than a measurement point within the surveyed area, It is the location where the drone user can collect the drone after its survey mission and upload the collected drone data to a computer for further processing. This computer system does not have to be positioned along the survey area, as it does not participate in taking measurements. Instead, it functions more as a ’home base’ for the drones, providing a site for maintenance, data transfer, and storage. This flexibility in the location of the computer system expands the operational capabilities of the drones, especially in remote or difficult-to-access areas where establishing a traditional base station in or along the survey area for measurement purposes is challenging or impractical, hi one example, the computer system may be a vehicle for transporting the drones to the desired survey area, and for giving the user a secure location from which to launch the drones.[0057| 'Phe collected magnetic data is then post-processed to generate a high-precision magnetic map of the surveyed area. This post-processing may invol ve several steps that when combined, result in a comprehensive and efficient process for generating high-precision magnetic maps,particularly beneficial for magnetic navigation and surveying in geographically challenging areas. Details of these steps are described with respect to the flowcharts.(0058] Referring now to FIG. 2. a side perspective view 200 of a drone assembly 100 equipped with a magnetometer 1 14 is depicted. The magnetometer 1 14 can have a sensing field of view 120, which is represented by dashed lines extending downwards to the terrain 206. Two field of view lines, 202 and 204, illustrate the extent of the magnetometer's sensing field of view 120 as itOur Ref No. 438230.000134 interacts with the terrain 206 and trees 208. This view 200 demonstrates how the drone assembly 100 can survey an area by collecting magnetic data across various features of the landscape, such as tiie terrain 206 and trees 208, to generate a magnetic map of the surveyed area.|0059| In some aspects, the drone assembly 100 may be configured to follow a predefined flight path over the surveyed area (e.g., grid-like pattern with intersections between survey lines), collecting magnetic data along the flight path. The magnetometer 1 14, which may be a scalar magnetometer in some cases, is capable of recording magnetic data while the drone assembly 100 executes the Hight path. The magnetometer 1 14 can be suspended below the central body 102 of the drone assembly 100 and is connected by a cable 1 18. Other magnetometer mounting configurations are of course possible. Magnetometer 114 is generally configured to detect magnetic fields within its sensing field of view 120, which extends downwards to the terrain 206. [0060| In some cases, the drone assembly 100 may be configured to collect magnetic data across various features of the landscape, such as the terrain 206 and trees 208. The collected magnetic data may include measurements of the magnetic field strength at different points along the flight path. The collected magnetic data may then be processed to generate a magnetic map of the surveyed area, which provides a visual representation of the magnetic field variations across the surveyed area.[0061.) In embodiments, the sensing field of view 120 may be larger or smaller, depending on the specifications of the magnetometer 1 14 and the altitude of the drone assembly 100. The sensing field of view 120 may also be adjustable, allowing the drone assembly 100 to adapt to different survey conditions or requirements. For example, the drone assembly 100 may be configured to increase the sensing field of view 120 when surveying a large, open area, and to decrease the sensing field of view 120 when surveying a smaller, more confined area.

[0062] In scenarios where the surveyed area is extensive or the terrain is particularly challenging, multiple drones may be deployed in a coordinated effort to cover the area more efficiently. Each drone, equipped with its own magnetometer, can be assigned a specific segment of the survey grid, allowing for simultaneous data collection across different parts of the area. This coordinated approach not just expedites the survey process but also ensures comprehensive coverage without leaving any gaps in the data. The drones can communicate with each other and with the computer system to synchronize their Hight paths and data collection, ensuring that the magnetic data from each drone can be seamlessly integrated during post-processing to create a unified and accurateI I wnfiossit iOur Ref No. 438230.000134 magnetic map of the large surveyed area, litis multi-drone strategy is particularly advantageous in time-sensitive or resource^con strained situations, maximizing the area surveyed in the shortest amount of lime while maintaining the high precision of the magnetic data.|0063} FIGs. 3 A, 313 and 3C show views of survey lines, magnetic data superimposed on the survey lines, and a resultant magnetic data map demonstrating the transition from raw flight path data to refined magnetic data. The survey lines map 302 A in FIG. 3A includes vertical trajectory5survey lines 304A and horizontal trajectory survey lines 306A, representing the trajectory of a drone during a magnetic survey. The survey lines map 302 A also includes non-syrvey data lines 308, which represent extraneous paths of the drone that do not contribute to the mapping effort. In some cases, these non-survey data lines 308 may be discarded during the clipping process to enhance the accuracy and precision of the resulting magnetic map. The grid-like survey pattern is chosen to ensure intersections between vertical and horizontal survey lines. The benefits of these intersections include creating points of comparison that act as substitutes Cor conventional base station measurements. Further details wifi be provided below,100641 The superimposed map 30213 in FIG. 3B includes vertical magnetic data lines 304B and horizontal magnetic data lines 30613 superimposed on the survey lines, indicating the processed magnetic data collected from the survey lines 304 A and 306A along the flight path map 302 A. The vertical magnetic data lines 30413 and horizontal magnetic data lines 306B may be generated by organizing the adjusted magnetometer data into grid survey lines, creating a structured representation of the surveyed area. In some aspects, this organization process may include interpolating the magnetometer data to fill gaps between the flight lines to generate a full resultant magnetic data map 302C of plotted magnetic data 310, ensuring a comprehensive and uniform representation of the surveyed area as shown in FIG. 3C.|0065| In some cases, the difference measurements may be set as a difference In the clipped magnetometer data collected along intersections of the flight lines. As mentioned above, these difference measurements may be Independent of base station measurements, allowing for magnetic data corrections in environments where setting up a base station is impractical or unfeasible. This base-station-free approach offers a more efficient and accessible solution for magnetic surveying. |0066} In some aspects, the processor may be further configured to set the difference measurements by using data from at least two intersecting flight lines io calculate a local anomaly12 J6J 5 W23&) !Our RefNo. 438230.000 ! 34 in the clipped magnetometer data. This process may enhance the accuracy of the magnetic map by accounting for local variations in the magnetic field.

[0067] In other words, an innovative approach of setting difference measurements at the intersections of the night lines may serve as a substitute for the conventional systems that typically rely on comparison to base station measurements. This comparison between measurements at the intersection points provides a robust mechanism for correcting magnetic data, leveraging the characteristics that diurnal variation is relatively smooth over a period of lime. By comparing the magnetic data collected at these intersecting points, the system can effectively account for and correct local magnetic anomalies (i.e., differences between the intersecting measurements indicate the diurnal variations and the potential systematic error which can be reduced or eliminated by the system). This method not just circumvents the logistical challenges of positioning base stations in difficult terrains but also streamlines the data correction process, thereby enhancing the overall efficiency and practicality of magnetic survey ing operations.

[0068] In embodiments, it is noted the flight path map 302 A and the magnetic map 302B may include additional or different elements, such as contour lines, grid lines, or symbols, to provide additional information or to enhance the readability of the maps, for example, the Hight path map 302A may include contour lines to indicate changes in altitude, and the magnetic map 302B may include grid lines to facilitate the interpretation of the magnetic data.[0()69| The flowcharts in FIG. 4 and FIG. 5 are now described with respect to the systematic process for collecting and processing the magnetic data to produce a comprehensive magnetic map of the surveyed area.

[0070] Referring now to FIG. 4. a flowchart 400 is depicted, outlining the steps involved in collecting and processing magnetometer data for the creation of a geological survey. The first step in the process is the data collection step 402, where one or more drones, such as drones characterized by drone assembly 100, follow a survey pattern to collect magnetometer data. The survey pattern may be a predefined flight path over the surveyed area, and the magnetometer data may be collected by magnetometer 1 14, integrated with the drone assembly 100. As mentioned above, the drone assembly 100 may follow a designed grid-like trajectory, collecting magnetic data along the flight path.(00711 Fol lowing the data collection step 402, the computer system retrieves the magnetometer data from the droue(s) in the data retrieval step 404. The computer system may be a computer or13 mis loassto iOur Rel No. 438230.000! 34 a similar device capable of receiving and processing the collected magnetometer data. The magnetometer data may be transmitted from the drone assembly 100 to the computer system via a wireless communication link using appropriate radio frequency (RE) communication equipment and protocols for communication distances, power and terrain. In some cases, the magnetometer data may be stored on a storage device, such as a memory card or a hard drive, on the drone assembly 100, and the computer system may retrieve the data by accessing the storage device.

[0072] The:collected magnetometer data is then processed by the computer system in the data processing step 406, where various corrections and analyses are applied to reline the data. The data processing step 406 may involve several sub-steps which are described in detail with respect to FIG. 5.[0073 | The computer system may display the geological survey based on the processed magnetometer data in the survey display step 408, resulting in a visual representation of the magnetic anomalies across the surveyed area. For example, the geological survey may be displayed on a display device al the computer system or a remote device, such as a computer monitor or a projector. The displayed geological survey may be a color-coded magnetic map, with different colors representing different ranges of magnetic field strength. The magnetic map may provide a detailed and accurate representation of the magnetic anomalies in the surveyed area, aiding in various applications such as mineral exploration, fault line detection, and underground structure mapping.

[0074] In embodiments, the process illustrated in flowchart 400 may include additional or different steps, depending on the specific requirements of the survey or the capabilities of the drone assembly 100 and the processing system. For example, the process may include a data validation step, where the collected magnetometer data is checked for errors or inconsistencies before being processed. The process illustrated in flowchart 400 may also include a data visualization step, where the processed magnetometer data is visualized in a three-dimensional format to provide a more detailed view of the magnetic anomalies in the surveyed area.|0075| In embodiments, the drones may also be equipped with long-range communication modules that enable real-time data transmission to the computer system during the survey. 'This capability allows for immediate analysis and potential adjustment of the survey parameters based on the incoming data, enhancing the efficiency and adaptability of the survey process. The real- time transmission can be facilitated through various wireless communication technologies, suchOur Ref bh . 438230.000134 as Wi-Fi, cellular networks, or satellite communications. depending on the operational environment and the range requirements.|0076| Furthermore, the computer system may possess the functionality to upload the raw or processed magnetometer data to a server. This server can be accessed remotely, enabling users to view and analyze the magnetic survey data from distant locations. The ability to upload data to a server expands the accessibility of the data, a! lowing for collaborative efforts in data analysis and decision-making processes. It also provides a secure and centralized repository for the survey data, which can be beneficial for long-term storage, backup, and retrieval purposes. This server-based approach can be particularly advantageous for large-scale projects Involving multiple stakeholders or tor organizations that operate across different geographical locations.

[0077] Referring now to FIG. 5, a flowchart 500 is depicted, outlining a detailed method for generating a magnetic map of a surveyed area based on the magnetometer data collected by the drone(s). The steps outlined in FIG. 5 can be executed at the computer system such as a laptop, by a server once the raw data has been uploaded, or a combination of both the computer system and the server.|()078] The method of flowchart 500 initiates with a line splitting and clipping step 502, a stage in the process where the raw magnetometer data, which has been collected by drone assembly 100, undergoes an initial refinement. This step is designed to streamline the data and prepare it for the subsequent processing stages. |0079| As described, the drone collects data along irajeciory.'survey lines with tie lines also referred to as “T-lines” (e.g., 1 tie line for every 4-8 flight lines). The line splitting aspect of this step involves dividing die collected magnetometer data into smaller, more manageable segments. This division is typically based on the flight lines followed by the drone during the survey, with each flight line corresponding to a separate segment of data (e.g., lines may be split and labelled as flight lines and tie lines). By splitting the data in this manner, the process can handle and analyze each segment individually, enhancing the efficiency and accuracy of the subsequent processing stages,[IIO8O| The clipping aspect of this step involves removing extraneous data points from the magnetometer data. These extraneous data points may include measurements that are not directly related to the magnetic field of the surveyed area, such as noise introduced by the drone's motion or environmental factors. By removing these extraneous data points, the process ensures that the15PC7 / GR2024 / 000028Our Ref No. 438230.000134 remaining data is relevant and contributes to the accuracy of the magnetic map. In some cases, the clipping may also involve discarding portions of the magnetometer data that fall outside predetermined thresholds of magnetic field strength. These thresholds are typically set based on the expected range of magnetic field values in the surveyed area, and any data points that tall outside this range are considered outliers and are discarded. This step further refines the data, ensuring that the remaining data points are within a reasonable and expected range, thereby enhancing the reliability of the resulting magnetic map.10081] Overall, the line splitting and clipping step 502 serves as a preliminary data cleaning stage, preparing the raw magnetometer data for the more complex correction and processing steps that follow. By dividing the data into manageable segments and removing extraneous and outlier data points, this step can ensure that the data is clean, relevant, and ready tor further processing.

[0082] Fol lowing the initial line splitting and clipping step 502, which prepares the raw magnetometer data for further processing, the next step is the IGRF correction step 504. The IGRF correction step 504 can be a beneficial stage in the process, designed to adjust the clipped magnetometer data based on the IGRF. The IGRF is a mathematical description of the Earth’s main magnetic field, which is used widely in studies of the Earth’s deep interior, crust, ionosphere, and magnetosphere.

[0083] In this step, the IGRF model is applied to the clipped magnetometer data. This model provides a reference for the expected magnetic field at the survey location based on global coordinates (latitude, longitude) of each data point. By applying the IGRF model, the process adjusts the clipped magnetometer data for known geomagnetic variations. This adjustment aligns the clipped magnetometer data with the geographic orientation of the surveyed area, ensuring that the Earth's main magnetic field is accounted for in the magnetic map.(00841 Overall, the IGRF correction step 504 ensures that the magnetic data collected by the drone is adjusted to account for the Earth’s main magnetic field. This step enhances the accuracy and reliability of the resulting magnetic map, providing a more accurate and informative depiction of the magnetic field variations in the surveyed area.[0085| Subsequently, the process involves a heading and error correction step 506. It is noted that heading errors can occur due to factors such as the drone’s orientation, wind conditions, or mechanical issues, and can skew the magnetometer readings. This step is designed to align the collected magnetic data with the geographic orientation of the surveyed area, ensuring that the dataOur Ref No. 438230.000134 accurately reflects the magnetic field variations in the context of the area’s geographical layout. This alignment is particularly beneficial for application? such as geological exploration or navigation, where the accurate representation of the magnetic field in relation to the geographic features of the area is of utmost relevance.|l.M)86] The heading and error correction step 506 may involve using data from an 1MU to adjust the clipped magnetometer data for heading error. The IMU is a device that measures and reports the specific force, angular rate, and sometimes the orientation of the drone. By integrating these measurements, the IM1J can provide data on the drone's velocity, orientation, and gravitational forces, which can be used to correct the magnetometer data for any errors introduced by the drone's heading,|00<87| The I MU data may be collected by a sensor, such as sensor 116, mounted on the drone assembly 100. This sensor is strategically positioned to accurately capture the drone’s motion and orientation during the survey. The sensor 1 16 collects IMU data in real-time as the drone follows its flight path, ensuring that the data is current and accurately reflects the drone’s state at each point along the path.[00881 In one example, lite drone may perform an experimental flight to understand how the drone's heading affects the scalar magnetometer’s measurements. This experimental flight may involve the drone performing different orientations, for example, every 90astarting from absolute North at the desired altitude of the survey, it is beneficial that the area where the experimental fight is performed is magnetically stable. The azimuth angle of the survey lines and the lie lines of the survey performed by the drone may then be corrected based on determined heading error (e.g.. the heading error may be subtracted from drone survey data). [00891 The process in flowchart 500 then proceeds to a step that involves the removal of temporal or diurnal variations in the Earth's magnetic field from its geographical variations 508. Th is step is a distinctive feature of the disclosed technology, as it is independent of base station measurements. In traditional magnetic surveying methods, as mentioned above, a base station is typically established along the survey path to record diurnal variations in the Earth's magnetic field. These recordings are then used to correct the magnetic data collected by the airborne system. However, this approach can be impractical or unfeasible in geographically challenging areas, such as high mountains, swamps, deserts, polar regions, and deep ocean areas, where selling up a base station is difficult or even not possible.17 iMjum ssaiOur Ref No. 438230.000134[009(J[ In contrast, the disclosed technology introduces an approach that allows for magnetic data correction without the reliance on base station measurements. This is achieved by settingdifference measurements based on the clipped magnetometer data collected along intersections of the flight lines. These intersections are points where the flight lines, which are the main lines along which the drone collects data, intersect with the tie lines, which are perpendicular or oblique lines used for calibration and quality control purposes. By comparing the magnetic data collected at these intersecting points, the system can effectively account for and correct local magnetic anomalies (i,e., diurnal variations), which might otherwise require the presence of a base station along the survey path for calibration purposes. Moreover, any potential systematic error in the survey lines and tie lines can also be corrected at the same time. Therefore, levelling is not necessary after this processing. This base-station-free approach not just circumvents the logistical challenges of establishing base stations in difficult terrains but also streamlines the data correction process, thereby enhancing the overall efficiency and practicality of magnetic surveying operations. In one example, a drift function for each flight line may be determined where polynomial coefficients for each function are chosen to minimize the root-mean-square of the tie- line / flight-line intersection residuals. The flight lines may then be corrected using these low-order polynomials.[0091 | The subsequent step in the process, which is optional but can enhance the quality of the final magnetic map, is the directional filtering step 510. This step can involve the application of a directional filter to the adjusted magnetometer data. The purpose of this filter is to enhance 'die clarity of the magnetic anomalies present in the data by reducing noise along the survey lines.[0092j Directional noise can be introduced by various factors, such as the drone's movement, environmental conditions, or sensor misalignments. This noise can distort the magnetic data, making it difficult to accurately interpret the underlying magnetic anomalies. By applying a directional filter, the process can effectively reduce this noise, resulting in cleaner and more accurate magnetic data.[00931 Directional filtering is particularly useful in distinguishing geological features that have a specific orientation, such as fault lines or mineral veins, from the surrounding magnetic data. These geological features can create distinct magnetic anomalies that align with their orientation. However, these anomalies can be obscured by directional noise or other magnetic signals in the data.Our Ref No. 43'8230.000 i 34[00941 By applying a directional filter, the process can focus on the magnetic signals that align with the expected geological structures, effectively separating these signals from the surrounding noise and other signals. 1'his can improve the interpretability of the magnetic map for geologists and surveyors, allowing them to more accurately identify and analyze the geological features of interest.[0095J Following the directional filtering step 510, the process continues with the step of organizing the adjusted magnetometer data into grid survey lines, as indicated in step 512. This step lays the groundwork for creating a structured representation of the surveyed area, a prerequisite for the generation of the magnetic map. |0096} In this step, the adjusted magnetometer data, which has been refined through the previous steps, can be systematically organized into a grid pattern. This grid pattern is designed io correspond to the predefined flight path of the drone, ensuring that the data is arranged in a manner that accurately reflects the drone's trajectory during the survey. This systematic organization of data is a process that involves alignment and arrangement of the data points to form a coherent and structured grid.

[0097] One of the challenges in this step is dealing with gaps in the data, which can occur between the flight lines due to factors such as the drone's speed, altitude, or the magnetometer's sampling rate. To address this issue, the process may involve interpolating the magnetometer data to fill any gaps between the flight lines. Interpolation is a mathematical technique that estimates the value of a variable at a point based on the known values at other points. By applying this technique, the process can generate a comprehensi ve and uniform representation of the surveyed area, ensuring that no areas are left unaccounted for in the magnetic map.(0098] Once the magnetometer data has been organized into grid survey lines, it forms what is known as structured magnetometer data. T his structured data serves as the foundation for the next step in the process, which is the generation of a visual representation of the magnetic field variations across the surveyed area. This visual representation, also known as the magnetic map, may be a color-coded image that depicts the variations in the magnetic field strength across the surveyed area. The magnetic map provides a clear and intuitive way for the user to interpret the magnetometer data, making it a valuable tool for various applications such as geological exploration, navigation, and resource management.19Our Ref No. 438230.000134

[0099] The process of generating an accurate magnetic map of a surveyed area, as described herein, may optionally include a step known as boydinage artifacts removal, denoted as step 514 in the process of flowchart 500. Boudinage artifacts are distortions in the magnetic data that can mimic the appearance of geological structures. However, these distortions are not indicative of actual geological features but are instead artifacts resulting from the data processing or collection procedures.[0100 J T ’hese artifacts can potentially mislead the interpretation of the magnetic map, as they can create false signals that may be mistaken for genuine magnetic .anomalies. This can lead io inaccurate conclusions about the geological features of the surveyed area, which can have implications for applications such as mineral exploration, fault line detection, or underground structure mapping. Therefore, the removal of these artifacts may enhance the accuracy and reliability of the resulting magnetic map.[0101 | The boudinage artifacts removal step 514 may involve applying advanced filtering techniques to the structured magnetometer data. These filtering techniques are designed to identify and eliminate the distortions associated with boudinage artifacts. The techniques may involve mathematical algorithms or statistical methods that can distinguish between true magnetic signals and artifacts based on their characteristics. For example, the filtering techniques may look for patterns or features in the data that are indicative of boudinage artifacts, such as unusual spikes or dips in the magnetic field strength, or patterns that do not align with the expected geological structures.|0I02| Once the boudinage artifacts are identified, they are removed from the structured magnetometer data. This removal process may involve replacing the artifact data points with interpolated values based on the surrounding data, or simply discarding the artifact data points. The result is a set of magnetometer data that is free of boudinage artifacts, ensuring that the data accurately reflects the true geological features of the surveyed area.

[0103] The next step in the process, as depicted in step 516, is the signal and noise separation.This step is designed to distinguish between meaningful magnetic signals, which represent the geological features of interest, and noise, which can obscure these signals. Noise in the context of magnetic survey data can arise from various sources, including environmental factors, instrument errors, the drone's motion and geological noises. These noise components can distort the magnetic data, making it challenging to accurately interpret the underlying magnetic anomalies.PCT / GR2O24 / OOOO28Our Ref No. 438230.000134|0104| To address this challenge, the process may involve applying equivalent source technology, which may be based on a fast inversion method, to the structured magnetometer data. This technology is adept at modeling the observed magnetic data as if it weregenerated by a distribution of sources, such as magnetic poles, located at a depth below the surveyed area. The inversion process iteratively adjusts the parameters of these sources to minimize the difference between the observed data and the data predicted by the model, effectively separating the magnetic signal from the noise. The equivalent source technique effectively removes noise and also separates the magnetic field signals generated by magnetic sources of interest.|0I05) Additionally, common methods for magnetic data processing, such as the Fast Fourier Transform (FFT) and wavelet transform, can also be applied. The ITT method decomposes a signal into its constituent sinusoidal components, each representing a specific frequency, which allows for the identification and isolation of periodic components typically associated with noise. The wavelet transform, on the other hand, decomposes a signal into wavelets localized wave functions that provide information about the signal at different scales and positions. This method may be effective at identifying and isolating non-periodic components in the data, which are likely to represent true magnetic anomalies.[01061 By employing these methods and isolating the signal from the noise, the process ensures that the magnetic map image generated is a clear and accurate representation of the magnetic field variations in the surveyed area. This step enhances the interpretability of the magnetic map, allowing for a more precise identification of geological features such as mineral deposits, fault lines, or underground structures. 'The resulting magnetic map. free from noise distortions, can then be used with greater confidence in various applications, from mineral exploration to navigation, providing a reliable tool for understanding the geological characteristics of the surveyed area.(01.07) 'The equivalent source technique may be employed to refine the magnetic map data by filtering out noise and distinguislting between regional and local magnetic signals, or any specific signals of interest. T his method may utilize a kernel that represents the relationship between the observed magnetic field and the equivalent sources, which are simple sources, such as magnetic poles or dipoles, posited to exist beneath the surveyed area. The separation of these components is beneficial for in-depth analysis and interpretation, providing valuable insights into the geological characteristics and potential resources within the surveyed area. In an example,21Our Ref No. 438230.000134 the method can help to separate the regional and local magnetic signals for further analysis and interpretation using Equation I below:where G is a kernel function; S' is an equivalent source; and doi)Sis the mag en tie anomaly data

[0108] Solving Equation I for S’ allows for the separation of the noise source Sk local source S) and regional source S', as shown in Equations 2, 3 and 4 below:Gf)* $„ ~ dnQiw(Eq. 2)G, * S(= cf (Eq, 3)Gr* 5'r— dr(Eq. 4) where Gii is the kerne! o f the noise source, Co is the kernel of the local source, (f is the kernel of the regional source, df!f,!Se is rhe magnetic field anomaly of 'the noise source, ch is the magnetic field anomaly of the local source, and <b is the magnetic field anomaly of the regional source.

[0109] Referring now to FIG. 6, a block diagram 600 is depicted, illustrating a system for data communication and storage. The system includes a personal computer (PC) 602, a network server 606, and a database storage 604. interconnected via a network 612. The personal computer 602 may be a standard computing device, such as a desktop computer, a laptop, or a workstation, equipped with a processor, memory, and other standard components. The personal computer 602 may be located at the computer system and used to control the operation of the drone assembly 100, to receive and process the collected magnetometer data, and to process the magnetic map of the surveyed area or upload the magnetic map data to a server.[OHO] T he network server 606 may be a dedicated server or a cloud-based server that faci litates the exchange of data between the personal computer 602 and the database storage 604. The network server 606 may be configured to receive the magnetometer data from the personal computer 602, to store the data in the database storage 604, and to retrieve the data from the database storage 604 for further processing or analysis. In some cases, the personal computer 602Our Ref No. 438230.000134 and network server 606 may also perform some data processing tasks, such as data compression, encryption, or error checking, to enhance the efficiency and reliability of the data communication. [01 1 1 ] 'I he database storage 604 may be a storage device or a storage system that stores the collected magnetometer data and other related data. The database storage 604 may be a local storage device, such as a hard drive or a solid-state drive, or a remote storage system, such as acloud storage service. The database storage 604 may be configured to store the magnetometer data in a structured format, facilitating efficient data retrieval and analysis. In some cases, the database storage 604 may also store other related data, such as flight path data, drone configuration data, or magnetic map data,

[0112] The network 612 may be a local area network (LAN), a wide area network (WAN), or a cloud-based network that facilitates data communication between the personal computer 602, the network server 606, and the database storage 604. ’The network 612 may support various data communication protocols, such as Ethernet, Wi-Fi, or Bluetooth, and may provide secure and reliable data communication.

[0113] T he arrows between the persona! computer 602, network server 606, and database storage604 indicate the flow of information across the network 612. highlighting the interconnected nature of the system components. The flow of information may involve the transmission of magnetometer data from the personal computer 602 to the network server 606. the storage of the data in the database storage 604, and the retrieval of the data from the database storage 604 for further processing or analysis.

[0114] In embodiments, the system may include additional or different components, such as additional servers, different types of storage devices, or different types of networks. For example, the system may include a cloud server in addition to or instead of the network server 606. allowing for remote access and storage of the magnetometer data. The system may also include a solid-state drive or a flash memory device in addition to or instead of the database storage 604, providing faster data access and retrieval.

[0115] Referring now to FIG. 7, a block diagram 700 of a computing system architecture is depicted. The computing system architecture may be part of a personal computer, such as personal computer 602, or a network server, such as network server 606. and may be used to process the collected magnetometer data and generate the magnetic map of the surveyed area.Our Ret No. 438230.000134|0116] The block diagram 700 includes a central processing unit 710, which is connected to a cache memory 712 and a main memory block 715 via a system bus 705. The central processing unit 710 may be a microprocessor or a similar processing device that performs the data processing tasks, such as data compression, encryption, error checking, and other operations involved in the generation of the magnetic map.|0117| The cache memory 712 may be a small, high-speed memory component that stores frequently accessed data to speed up the data processing tasks. The cache memory 712 may be integrated into the central processing unit 710 or may be a separate component connected to the central processing unit 710 via the system bus 705.|0118] The main memory block 715 includes read-only memory (ROM) 720 and random-access memory 725. The read-only memory 720 may store firmware or other permanent data that is not intended to be modified. The random-access memory 725 may store temporary data or instructions that are currently being processed by the central processing unit 710.{0119] 'The central processing unit 710 is also connected to a storage device block 730, which comprises first server storage 732, second server storage 734, and third server storage 736. The storage device block 730 may be a local storage device, such as a hard drive or a solid-state drive, or a remote storage system, such as a cloud storage service, 'The storage device block 730 may store the collected magnetometer data, the processed data, and other related data.

[0120] A communication interface 740 facilitates data exchange with external devices and is connected to the system bus 705. The communication Interface 740 may support various data communication protocols, such as Ethernet, Wi-Fi, or Bluetooth, and may provide secure and rel i ab I e data com m un icat i on ,

[0121] Additionally, an output device 735 and an input device 745 are interfaced with the communication interface 740, allowing for user interaction with the computing system. The output device 735 may be a display device, such as a computer monitor or a projector, that displays the geological survey based on the processed magnetometer data. The input device 745 may be a keyboard, a mouse, a touchscreen, or any other device that allows the user to input commands or data into the computing system.

[0122] In embodiments, the computing system architecture may include additional or different components, depending on the specific requirements of the survey or the capabilities of the drone assembly 100 and the processing system. For example, the computing system architecture may24 s 102 >*0 1Our Ref No. 438230.000134 include a graphics processing unit (GPU) for performing complex calculations or rendering high- quality images. The computing system architecture may also include additional input devices, such as a joystick or a touchscreen, to facilitate user interaction with the system.

[0123] In a specific use case, the user operates the drone assembly 100 via the personal computer 602, which: serves as the eoinmand-and-control center for the survey operation. The user initiates the survey by sending commands to the drone assembly I ()() to follow a predetermined Hight path over the area of interest. As the drone assembly 100 traverses the landscape, the magnetometer 1 14 collects magnetic data, which is then wirelessly transmitted in real-time or uploaded to the personal computer 602.

[0124] Upon receipt of the data, the personal computer 602 collaborates with the network server606 to initiate the storage and processing of the collected data. The network server 606 performs the heavy lifting of data processing. It executes complex algorithms, such as the Fourier or wavelet transforms, to separate meaningful geological signals from noise. The processed data is then stored in the database storage 604. which is structured to allow for efficient retrieval and further analysis.

[0125] The next step involves the visualization of the processed data. Personal computer 602, displays a detailed magnetic map of the surveyed area. This map is color-coded to represent different ranges of magnetic field strength, providing the user with a clear and intuitive visual representation of the magnetic anomalies. The map may highlight areas of high magnetic intensity, potentially indicating the presence of mineral deposits or geological structures such as fault lines.

[0126] For the user, this displayed data enables the identification of areas of interest for further exploration or study. For instance, in mineral exploration, the user can pinpoint specific locations where the magnetic intensity suggests the presence of valuable resources. In environmental studies, the map can help in understanding the distribution of magnetic materials in the soil, which can be indicative of various soil properties. In archaeology, subtle variations in the magnetic map might reveal buried structures or artifacts. The high-precision magnetic maps generated by this system thus provide a reliable tool for users across various fields, enhancing their ability to complete tasks with greater accuracy and efficiency.

[0127] While the foregoing is directed to example embodiments described herein, other and further example embodiments may be devised without departing from the basic scope thereof. For example, aspects of the present disclosure may be implemented in hardware or software or a combination of hardware and software. One example embodiment described herein may beOur Ref No. 438230.000134 implemented as a program product tor use with a computer system. The program(s) of the program product defines functions of the example embodiments (including the methods described herein) and may be contained on a variety of computer-readable storage media. Illustrative computer- readable storage media include, but are not limited to: (i) non-writable storage media (e.g.. read- only memory (ROM) devices within a computer, such as CD-ROM disks readably by a CD-ROM drive, Hash memory, ROM chips, or any type of solid-state non- volatile memory) on which information is permanently stored: and (ii) writable storage media (e,g.< floppy disks within a diskette drive or hard-disk drive or any type of solid-state random-access memory) on which alterable information is stored. Such computer-readable storage media, when carrying computer- readable instructions that direct the functions of the presented example embodiments, are example embodiments of the present disclosure.[0128| h will be appreciated by those skilled in the art that the preceding examples are exemplary and not limiting. It is intended that all permutations, enhancements, equivalents, and improvements thereto are apparent to those skilled in the art upon a reading of the specification and a study of the drawings are included within the true spirit and scope of the present disclosure.It is therefore intended that the following; appended claims include all such modifications, permutations, and equivalents as fall within the true spirit and scope of these teachings.

Claims

Our Ref No. 438230.000134What is claimed is:I . A method for generating a magnetic map image of a surveyed area, comprising: performing a line splining on magnetometer data collected by a drone along drone flight lines of the surveyed area, the splitting creating split magnetometer data by dividing the magnetometer data into manageable segments; performing a clipping on the split magnetometer data, the clipping creating clipped 'magnetometer data by removing extraneous data points from the magnetometer data; applying an adjustment to create adjusted magnetometer data by adjusting the clippedmagnetometer data for known geomagnetic variations and to align the clipped magnetometer data with a geographic orientation of the surveyed area; setting: difference measurements as a difference in the adjusted magnetometer data collected along intersections of the flight lines, the difference measurements being independent of base station measurements; organizing the adjusted magnetometer data into grid survey lines to create structured magnetometer data as a structured representation of the surveyed area; separating a signal and a noise in the structured magnetometer data; and generating the magnetic map image of the surveyed area based on the separated signal in the structured magnetometer data, the magnetic map image indicating magnetic strength across the surveyed area.

2. The method of claim I , wherein the clipping includes discarding portions of the magnetometer data that fal l outside predetermined thresholds of magnetic field strength.

3. The method of claim I , wherein setting the di fference measurements incl udes using data from at least two intersecting flight lines to calculate a local anomaly In the clipped magnetometer data.

4. The method of claim 1, wherein the adjustment for the known geomagnetic variations includes applying an International Geomagnetic Reference Field (IGRF) model to the dipped m agnetometer data .

5. The method of claim 1 , wherein performing the adjustment includes using inertial measurement unit (IMU) data to adjust the clipped magnetometer data to adjust for heading error.161 lUCly&lOur Ref No. 438230.0001346. 1'he method of claim 1 , further comprising applying a polynomial fit and a directional filter to the adjusted magnetometer data to reduce an influence of temporal magnetic field variations and systematic noises.

7. The method of claim I. wherein organizing the adjusted magnetometer data into the grid survey lines includes interpolating the magnetometer data to fill gaps between the Hight lines.

8. fhe method of claim I, wherein separating a signal and a noise in the structured magnetometer data includes applying a transform to distinguish between periodic and non-periodic components.

9. The method of claim I . wherein generating the magnetic map image includes color-coding the map to represent different ranges of magnetic field strength.

10. The method of claim I , further comprising using the generated magnetic map image to identify geological features such as mineral deposits, fault lines, or underground structures.1 1 . A system for generating a magnetic map image of a surveyed area, comprising: a receiver configured to receive magnetometer data collected by a drone along drone flight lines of the surveyed area; and a processor configured to: perform line splitting on the received magnetometer data, the splitting creating split magnetometer data by dividing the magnetometer data into manageable segments; perform clipping on the split magnetometer data, the clipping creating clipped magnetometer data by removing extraneous data points from the magnetometer data; apply an adjustment to create adjusted magnetometer data by adjusting the clipped magnetometer data for known geomagnetic variations and to align the clipped magnetometer data with a geographic orientation of the surveyed area; set difference measurements as a difference in the adjusted magnetometer data collected along intersections of the flight fines, the difference measurements being independent of base station measurements; organize the adjusted magnetometer data into grid survey lines to create structured magnetometer data as a structured representation of the surveyed area;Our Ref No, 438230.000134 separate a signa) and a noise in the structured magnetometer data; and generate the magnetic map image of the surveyed area based on the separated signal in the structured magnetometer data, the magnetic map image indicating magnetic strength across the surveyed area.

12. The system of claim 1 1, wherein the processor is further configured to perform the clipping by discarding portions of the magnetometer data that fall outside predetermined thresholds of magnetic field strength.

13. The system of claim 1 1 , wherein the processor is further configured to set the difference measurements by using data from at least two intersecting flight lines to calculate a local anomaly in the clipped magnetometer data.1-4. The system of claim I f , wherein the processor is further configured io apply an International Geomagnetic Reference Field (IGRF) model to the clipped magnetometer data for the adjustment of the known geomagnetic variations.

15. The system of claim I I , wherein the processor is further configured to use inertia) measurement unit (1MU) data to adjust the clipped magnetometer for heading error.

16. The system of claim 1 1, wherein the processor is further configured to apply a polynomial fit and a directional filter to the adjusted magnetometer data to reduce an influence of temporal magnetic field variations and systematic noises.

17. The system of claim 1 1, wherein the processor is further configured to interpolate the magnetometer data to fill gaps between the flight lines when organizing the adjusted magnetometer data into the grid survey lines,18. The system of claim 1 1 , wherein the processor is further configured to apply a transform to the structured magnetometer data to distinguish between periodic and non-periodic components when separating a signal and a noise.

19. The system of claim I I , wherein the processor is further configured to color-code the magnetic map to represent different ranges of magnetic field strength when generating the magnetic map image.Our Ref No. 438230.00013420. The system of claim 1 1, further comprising utilizing the generated magnetic map image to identify geological features such as mineral deposits, fault lines, or underground structures.

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