Aeromagnetic surveying has long been a cornerstone in geophysical exploration, but the advent of multi-rotor drone technology has revolutionized its application in challenging terrains. As a researcher deeply involved in this field, I have observed the limitations of traditional methods and the need for efficient quality assessment tools. Multi-rotor drones, with their ability to fly at low altitudes and adapt to complex landscapes, offer unprecedented opportunities for detailed magnetic surveys. However, their short endurance and susceptibility to environmental factors like wind necessitate frequent takeoff point changes and real-time data evaluation. In this paper, I present the development of a quality evaluation system for multi-rotor drone aeromagnetic surveys using scripts written for the Generic Mapping Tools (GMT) platform. This system addresses the critical need for rapid assessment of flight and magnetic data quality, enabling field personnel to make informed decisions and maintain survey continuity without the delays associated with manual processing.
The core of this system lies in its ability to automate the extraction of survey line data and compute key quality metrics. Multi-rotor drone aeromagnetic systems often collect data beyond the planned flight lines due to pre-flight calibration and post-flight maneuvers, making data segmentation essential. By leveraging GMT’s robust data processing modules, I designed scripts that filter raw aeromagnetic data based on planned flight paths, split it into individual lines, and calculate parameters such as deviation from planned routes (cross-track distance), flight altitude relative to ground surface, and dynamic noise of magnetic measurements. The integration of external data, like satellite imagery and digital elevation models (DEMs), further enhances the system’s utility by providing contextual visualizations. This approach not only streamlines the evaluation process but also outputs vectorized graphics that are easily interpretable in field conditions, significantly improving the efficiency of multi-rotor drone operations in aeromagnetic surveys.
In designing this system, I focused on two main components: preprocessing and graphical display. The preprocessing phase involves extracting survey line data from raw inputs, which typically include coordinates, altitude, magnetic total field values, and timestamps. Using GMT’s select and split modules, the scripts isolate data within specified flight lines based on azimuth angles and distance thresholds. For instance, the cross-track distance (deviation) is computed using the mapproject module, while flight altitude is derived by comparing drone altitude with DEM data through grdtrack. The magnetic data quality is assessed via a fourth-difference method for dynamic noise calculation, which helps identify anomalies caused by electromagnetic interference or instrument issues. This method involves computing the difference between sequential magnetic readings and applying a threshold to filter out spurious values. The formula for the fourth difference at point \(i\) is given by:
$$ D_i = T_{i-2} – 4T_{i-1} + 6T_i – 4T_{i+1} + T_{i+2} $$
where \(T_i\) represents the magnetic total field value at point \(i\). The dynamic noise level \(\delta\) for a survey line is then calculated as the average of the absolute values of \(D_i\) after excluding points where the magnetic gradient exceeds 600 nT/km:
$$ \delta = \frac{1}{N} \sum_{i=1}^{N} |D_i| $$
where \(N\) is the number of valid data points. This automated calculation ensures that field teams can quickly identify lines with excessive noise, prompting immediate re-survey if necessary.
The graphical display component transforms the statistical results into intuitive visualizations. I utilized GMT’s plotting capabilities to generate a composite output featuring three main plan views: a flight trajectory overlay on satellite imagery, a flight altitude profile map on a topographic base, and a magnetic total field profile map. Below these, statistical charts and tables for each survey line provide detailed quality metrics. This layout allows users to correlate spatial data with quantitative assessments, such as identifying areas where wind gusts might have caused navigation errors or where magnetic anomalies align with known interference sources. The use of subplot modules in GMT facilitates the dynamic arrangement of these elements, accommodating variable numbers of survey lines without sacrificing clarity. For example, the flight altitude profile map uses DEM data to display terrain contours, with wiggle plots showing altitude deviations, while the magnetic profile highlights total field variations against a reference baseline.
To illustrate the system’s functionality, consider the data file formats handled during processing. The table below summarizes the key files and their structures, which are integral to the preprocessing phase:
| Category | Filename | Longitude Column | Latitude Column | Altitude Column | Magnetic Field Column | Timestamp Column | Cross-Track Distance Column | Flight Height Column |
|---|---|---|---|---|---|---|---|---|
| Reference Data | plan.txt | 1 | 2 | – | – | – | – | – |
| Processed Files | profile.txt | 1 | 2 | 3 | 4 | 6 | 7 | 8 |
| Intermediate Files | mag.tmp | 1 | 2 | 3 | 4 | – | – | – |
This structured approach ensures that each step in the quality evaluation is traceable and repeatable. For instance, the plan.txt file defines the planned flight lines, while profile.txt contains the enriched data with computed metrics. The scripts automate the transition between these files using GMT commands and AWK scripting, reducing manual intervention and minimizing errors.
The development environment for this system centers on GMT 6.4, with scripting done in Bash through Git for Windows, though it is compatible with Linux and macOS. I chose GMT for its extensive mapping and data processing capabilities, which include modules like math for statistical calculations and grdimage for rendering spatial data. The use of AWK commands allows for efficient text processing, such as extracting specific data columns or computing averages. For example, to calculate the average cross-track distance for a survey line, the script uses:
$$ \text{xtk_mean} = \frac{1}{N} \sum_{j=1}^{N} |x_j| $$
where \(x_j\) is the cross-track distance at point \(j\), and \(N\) is the total number of points in the line. This is implemented in GMT with the math module, as shown in the script snippets. Similarly, flight height is derived by subtracting DEM-based ground elevation from the drone’s altitude, providing a true above-ground level (AGL) value that is critical for low-altitude surveys with multi-rotor drones.
In practice, the system processes raw aeromagnetic data through a series of steps. First, the raw data is simplified to retain only essential columns: longitude, latitude, altitude, magnetic total field, and timestamp. This is done using AWK commands to create an intermediate file (e.g., ini.tmp). Next, the select module filters data within the planned flight lines, and the split module divides it into individual survey lines based on azimuth changes. The cross-track distance is computed by projecting each data point onto the nearest segment of the planned route, and flight height is determined by querying a DEM grid. The resulting data, stored in files like profile.txt, includes all necessary metrics for quality evaluation. For each survey line, statistical summaries—such as minimum, maximum, and mean values for cross-track distance, flight height, and magnetic field—are generated using GMT’s math module. These summaries are then compiled into tables and histograms for graphical output.
The graphical output is designed for clarity and ease of use in field settings. The three plan views are arranged using GMT’s subplot functionality, with the flight trajectory map overlaid on satellite imagery to highlight navigation accuracy. The flight altitude map uses DEM-based contours and wiggle plots to visualize height variations, while the magnetic profile map displays total field anomalies. Additionally, known interference sources can be plotted as points or lines to aid in anomaly interpretation. Below these maps, each survey line’s quality metrics are presented in a table and histogram format. The table includes details like start and end coordinates, line length, duration, average speed, and dynamic noise level, while the histograms show distributions of cross-track distance, flight height, and magnetic values. This comprehensive presentation enables field personnel to quickly assess whether a multi-rotor drone survey meets quality standards and identify specific lines that require re-flying.
An application of this system in a real-world scenario demonstrates its effectiveness. In a mineral exploration project in a mountainous region, multi-rotor drones were deployed for high-resolution aeromagnetic surveys. The area’s complex topography and frequent wind gusts posed challenges to flight stability and data quality. Using the GMT-based evaluation system, field teams processed data from multiple flights immediately after each sortie. For instance, in one flight consisting of four survey lines, the scripts quickly identified one line with abnormal magnetic values due to electromagnetic interference. The dynamic noise level for that line exceeded 100 nT, far above the acceptable threshold, while other lines showed noise levels below 0.1 nT. The graphical output revealed that the issue occurred over a known power line, corroborating field observations. As a result, the team re-flew the affected line during the same field session, avoiding the need for a costly return trip. This case underscores how the system enhances the operational efficiency of multi-rotor drone aeromagnetic surveys by enabling rapid, data-driven decisions.
The advantages of this GMT script-based system are manifold. Firstly, it significantly reduces the time required for quality evaluation—typically completing within minutes—allowing for near-real-time assessment during field operations. Secondly, the integration of satellite imagery and DEM data provides contextual insights that are invaluable for interpreting flight quality issues, such as wind effects over varying terrain. Thirdly, the automated extraction of survey line data produces outputs that are directly usable for subsequent processing stages, streamlining the overall workflow. Moreover, the system’s reliance on open-source tools like GMT makes it accessible and cost-effective for widespread adoption. As multi-rotor drone technology continues to evolve, with improvements in battery life and payload capacity, such quality evaluation systems will become even more critical for maximizing the potential of low-altitude aeromagnetic surveys.
In conclusion, the development of this multi-rotor drone aeromagnetic quality evaluation system using GMT scripts represents a significant advancement in geophysical fieldwork. By automating data extraction, statistical analysis, and visualization, it addresses the unique challenges posed by multi-rotor drone operations, such as limited endurance and environmental sensitivity. The system’s ability to generate comprehensive, easy-to-interpret outputs empowers field teams to maintain high data quality and survey continuity. Future work could focus on integrating real-time data transmission and machine learning algorithms for anomaly detection, further enhancing the system’s capabilities. As I continue to refine this approach, I believe it will play a pivotal role in unlocking the full potential of multi-rotor drones in aeromagnetic exploration across diverse landscapes.