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Altitude refers to Meters Above Mean Sea Level. For both MagArrow I and II, the altitude is ellipsoidal and the earth model is WGS84. For additional information: Overview Reported elevations from MagArrow (and G-864 and MagEx) are the unedited values from the elevation field in the GNSS's GGA NMEA string. That value is the GNSS's calculated height above geoid; height above geoid is the standard meaning of the elevation field in the GGA NMEA string. But what does calculated height above geoid mean? Definitions GNSS - Global Navigation Satellite System GPS - The GNSS operated by the United States. Other systems include GLONASS(Russia), Galileo (Europe), BeiDou (China), QZSS (Japan), IRNSS (India). Ellipsoid - A comparatively simple or abstract geometric model of Earth's surface. Geoid - A more complex model of Earth's surface that takes the place of what was previously called Mean Sea Level. At any particular latitude or longitude, the geoid's surface may be above or below the ellipsoid's by as much as a few hundred meters, depending on regional and local geography and geology. Reference datum - A specific model of Earth's shape (such as WGS84, EGM96...), including references to specific landmarks. Calculations A particular GNSS, for example the GPS system run by the United States, provides timing data to a receiver to calculate the receiver's position above or below a particular latitude and longitude on the surface of the ellipsoid. The GNSS receiver first uses that timing data to calculate its height over the ellipsoid, and then subtracts from it the local height of the geoid over the ellipsoid (or HAE), to arrive at the local height of the receiver over the geoid (or in old-fashioned terms, elevation over mean Sea level): h - calculated height above geoid. This is the value reported in the GGA elevation field. H - height of the receiver over the ellipsoid (calculated from GNSS timing signals) N - local height of geoid over the ellipsoid, or HAE, per a lookup table or other local reference. h = H - N      Because the local height of the geoid over the ellipsoid is not provided by the GNSS, it must be provided locally, i.e. by the GNSS receiver, which may contain an internal database from which the local geoid height over ellipsoid (or HAE) can be found, based on the receiver's latitude and longitude. Small GNSS receivers contain small HAE databases, so the HAE value will not be exact. Some small receivers contain no HAE table at all; in this case HAE is deemed to be zero, so that the reported elevation is the uncorrected height over ellipsoid.​​​​​​​ A user of elevation data from Geometrics' MagArrow, G-864, and MagEx magnetometers may evaluate or adjust the reported values of the GGA elevation field and the GGA HAE field, by comparing the GGA HAE values to another source of local HAE data; this may particularly be useful for GNSSes that report a HAE equal to zero. Geometrics magnetometers do not currently record the values of a VDOP calculation, which offers an additional statistical estimate of the accuracy of the GNSS elevation measurement.

There are many things that don’t work as well at high altitude. Many of these don’t apply to the MagArrow such as LCDs, can type electrolytic capacitors, hard drives, Sealed keyboards, and High Voltage flashover points. What could possibly be an issue are the LiPo batteries, thermal cooling reduction at lower pressures, and the Sealed MagArrow case. MagArrow Case: While the body of the MagArrow is Sealed tight, it is not Sealed enough to puff out or get crushed with altitude changes. This hasn’t been tested. LiPo Batteries: These are not altitude rated. The failure mode is shorted cells and fire. Thermal Cooling: This could be measured in a pressure chamber using the internal temperature monitor diodes. We would have to rent time in the chamber to do the measurement. We might also make a stab at calculating the temperature increase. 30,000 feet is about 3 PSIA compared to 14 PSIA at Sea level. There is no altitude restriction on the MagArrow, but flying at high altitudes is taken at the users risk.

Hi MuhammaUnofficially, the MagArrow was designed to be very non-magnetic, so hard* *But your drone, with its big motors and the magnets in it are much more magnetic But I would suggest flying higher (due to the last drone hitting something) and with a quality mounted camera, looking down at 45 degrees, record raw (for clarity) video footage, and review it. Ideally, following the same survey lines and flight direction. If nothing is visible, try a different height and angle to gain a different View through the foliage. Ideally, with the sun at an angle from behind you to assist in lighting up the View. That's my suggestion based on what you have described. Wishing you the very best "luck"

The 3 Meter accuracy of the GPS in the MagArrow I is 50% Circular Error Probable (50% CEP). That means that if accumulating location data at a fixed point for a long period of time 50 percent of the readings will be within a circle with a 3 meter radius of the actual location, and 50% will be outside that circle. It also requires the GPS antenna have a clear View of the sky with no multipath interference. A good measure of GPS accuracy is to look at the HDOP number. It should be less than 1. Altitude values as a rule of thumb will be half as accurate due to the geometry of satellites. It will be much less accurate if the satellites towards the horizon are blocked, which is often the case. In other words the best accuracy for altitude requires a wide View of the sky. MagArrow II has much improved GPS specs: 50% CEP 1.5m; less than 1m with SBAS.

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What Affects Geode Trigger Cycle Times? If you're trying to optimize your Geode system for faster trigger cycles—especially in high-repeat environments—there are a few key factors to consider. The goal is to ensure that the system completes its entire cycle (trigger → recording → data transfer → re-arming) before the next expected trigger. Here’s what influences that cycle: 🧠 Core Factors That Affect Cycle Times 1. File Size (Sampling Parameters) Your sample interval and record length directly affect the size of each data file.You can View the resulting file size in the Acquisition Parameters menu.Larger files take longer to transfer, which delays the re-arm process. 2. Data Transfer Rate The Geode typically transfers data at around 450–465 kb/sec.Reducing file size is the best way to reduce transfer time and speed up the cycle. 3. Calibration Frequency By default, the system may attempt to calibrate every N shots, which takes additional time.Go to Options > Calibration and set "calibrate every N shots" to a large number (e.g., 100000) to prevent unnecessary delays. 4. Recording Delay and Record Length If you're operating in a region with a consistently deep Seafloor, you can add a recording delay and reduce record length accordingly.Example: If the water column is always >0.3s, you can apply a delay of 0.2s and reduce record length by the same amount.This trims your file size and speeds up the transfer/re-arm process. ⚙️ Best Practices Use the Auto-Trigger function or set trigger sensitivity to the maximum value for testing.Monitor the cycle timing and adjust acquisition parameters to stay within your trigger window.It's often an iterative process to find the ideal configuration for your environment.

Here are a few additional details relating to the measurement of elevation: GNSS AccuracyAccuracy depends on multiple aspects of the GNSS system: among them are clock accuracy, atmospheric effects, and satellite geometry. Satellite geometry"Satellite geometry" refers to how the currently visible satellites are distributed in the sky - close to each other or scattered around. The best satellite geometry includes satellites that are near the axes on which you hope to locate your receiver; for example, to locate your receiver on the East/West axis, it's helpful to have good reception from satellites low in the sky in the East and in the West. If you also have satellites that are low in the sky near the South and North horizons, you will have good accuracy on the horizontal (latitude/longitude) plane. It's best to have satellites scattered around the sky, overhead as well as near the horizon all around. HDOPMagArrow records HDOP, a standard measure of satellite geometry's effect on horizontal (or latitude/longitude) accuracy. A smaller number (less than 1.0 is very good) indicates that the visible satellites are in good positions to contribute to accuracy. Vertical AccuracyThe reason that GNSS systems aren't as accurate on the vertical axis as on the horizontal axes is that no satellites are visible in a full half of that axis: the half that is below the horizon. Consequently, vertical accuracy is on average about half that of horizontal accuracy; calculated offset from true elevation is on average about twice that as on the horizontal axes. While on average HDOP can therefore be used to estimate VDOP (the similar measurement of the effect of satellite geometry on the vertical axis), that estimate is only a rule of thumb; it is possible to have an excellent HDOP, reflecting very good horizontal satellite geometry, while having poor vertical satellite geometry. In those cases, good HDOP does not indicate good VDOP. Keeping in mind that possibility, a combination of good HDOP and many satellites in View usually indicates good VDOP. Practical effects Some data processing techniques (upward continuation, for example) can include elevation as an input. Customers who are considering using GNSS elevation in those techniques should conduct a careful analysis of their data and develop test routines to verify that all their data meet the requirements of the technique and its application to a particular survey. Some customers who require very accurate elevation data incorporate LIDAR data and drone elevation data into their analyses.

Dead-Zones The MagArrow is a dual sensor magnetometer powered by MFAM sensors, but it is configured for use so it only has a single data output. The reason Geometrics has done this is so we could ensure the MagArrow encounters no "dead-zones". A dead-zone occurs when the orientation of a magnetometer results in the magnetometer producing poor or no measurements. The dead-zone angle depends on the location of survey. Since we have the two MagArrow MFAM sensors in orthogonal orientations, the MagArrow Magnetometer has operability worldwide without affecting survey orientation, making it much easier to use for the customer. Heading Errors Heading errors are a type of noise magnetometers can experience. They come from three sources: Sensor Console Operator Magnetic materials in the sensor itself are the primary cause of heading errors. The physics of Cesium and Potassium magnetometers can contribute small amounts to the total heading error. Magnetic contamination near the sensors, operating electronics, or operator can all contribute to heading error. Heading errors look like herringbone patterns in survey images. Alternate lines can also be corrugated. Dead-Zones vs Heading Errors while these two sources of error in magnetic data are different, there is overlap between them when operating a magnetometer like the MagArrow. Heading errors can be fixed relatively easily in software, where dead zones can be much harder to manage. If a line is completely ruined because of a dead zone then they will need to re-fly the line/mission which is time consuming. Even with advanced users, these sorts of mishaps can happen. Additionally, the closer a mag sensor operates to a dead-zone, the larger a heading error will be measured. With compensation software and a pre-survey heading error flight, heading error can be reduced dramatically to around 1 nT for the MagArrow. Click to View the difference between Raw and Processed MagArrow Data The MagArrow is only outputting a single value as a means to create a “no-dead-zone” system. Obviously each sensor has a dead zone themselves, but with the sensors orientated orthogonally at least one sensor at all times will have a magnetic measurement. By combining the measurements from both sensors it is possible to generate a constant magnetic field measurement independent of orientation and location in the world. If the data from each MFAM sensor in the MagArrow was individually reported there would be gaps in the mag fields observed by either sensor as you fly, rotate, and swing.

The Geometry GUI provides a graphical representation of your survey, along with a wide range of control capability. It is particularly useful when conducting reflection surveys, but can be useful in a wide range of applications. It summarizes, in one simple View, the physical positions and other attributes of the hardware on the ground, and allows graphical control of these. Below is a typical display of a 96-channel, four-Geode layout. We will first describe the display itself, and follow with a description of its control capabilities. Example Geometry GUI

The MFAM Magnetometer samples at 1000 Hz, which in turns captures a lot of unique waveforms. When Viewing the data raw, it can therefore appear to be a bit noisy. But a closer examination of the data will reveal a real variation of the magnetic field which is caused caused by the power distribution network. Proper filtering is required to reduce the power line caused variations and reveal the strong signal of interest. It is not obvious that 60 or 50 hertz electromagnetic radiation is real, since in ordinary experience any power line “noise” is electrostatically coupled into a system (think 60 hertz hum on a stereo system) and is a fault that needs to be fixed. In this case however the variation in the magnetic field is induced by the power grid and is real. The magnetometer is simply and dutifully reporting the variation. These power line variations are to some extent present everywhere – even miles from the nearest power line. But obviously being close to power lines will increase the amplitude of the variations a lot. Often on a MagArrow survey the power line variations will be larger at one end of the survey area than the other. Poking in the GPS coordinates at the survey area nearest the larger variations into Google Earth will usually reveal the power lines from an aerial View – even if they are not visible on the ground. After applying a Fourier Frequency Transform on the MFAM data to identify the noise sources, 50 and 60 Hz noise amplitudes are easily observed. Also observable is the likely to be 20.8 Hz Schumann resonance of the third node and some other ultra-low frequency electro magnetic radiation produced naturally by the Earth. Harmonics of 60 Hz are also present. Another common question is “Why is the power line variations not a sine wave like the power line voltage?” Remember that voltages do not make magnetic fields. Only current generates magnetic fields, and the current being drawn is not a sine wave at all. Many loads, for example, only draw current at the voltage peaks. This makes for a non-sinusoidal magnetic field that is rich in harmonics. Also note that most power distribution system use a 3 phase topology. The ripple current in such a system will be 150 or 180 Hz. Thus you will often see large peaks in the power spectrum at these frequencies and their harmonics.

The MFAM Magnetometer samples at 1000 Hz, which in turns captures a lot of unique waveforms. When Viewing the data raw, it can therefore appear to be a bit noisy. But a closer examination of the data will reveal a real variation of the magnetic field which is caused caused by the power distribution network. Proper filtering is required to reduce the power line caused variations and reveal the strong signal of interest. It is not obvious that 60 or 50 hertz electromagnetic radiation is real, since in ordinary experience any power line “noise” is electrostatically coupled into a system (think 60 hertz hum on a stereo system) and is a fault that needs to be fixed. In this case however the variation in the magnetic field is induced by the power grid and is real. The magnetometer is simply and dutifully reporting the variation. These power line variations are to some extent present everywhere – even miles from the nearest power line. But obviously being close to power lines will increase the amplitude of the variations a lot. Often on a MagArrow survey the power line variations will be larger at one end of the survey area than the other. Poking in the GPS coordinates at the survey area nearest the larger variations into Google Earth will usually reveal the power lines from an aerial View – even if they are not visible on the ground. After applying a Fourier Frequency Transform on the MFAM data to identify the noise sources, 50 and 60 Hz noise amplitudes are easily observed. Also observable is the likely to be 20.8 Hz Schumann resonance of the third node and some other ultra-low frequency electro magnetic radiation produced naturally by the Earth. Harmonics of 60 Hz are also present. Another common question is “Why is the power line variations not a sine wave like the power line voltage?” Remember that voltages do not make magnetic fields. Only current generates magnetic fields, and the current being drawn is not a sine wave at all. Many loads, for example, only draw current at the voltage peaks. This makes for a non-sinusoidal magnetic field that is rich in harmonics. Also note that most power distribution system use a 3 phase topology. The ripple current in such a system will be 150 or 180 Hz. Thus you will often see large peaks in the power spectrum at these frequencies and their harmonics.