A method for estimating glacier ice reserves based on flying ice radar

Abstract

The present invention relates to a method for estimating glacier ice reserves based on a flying ice radar, comprising: acquiring a large-scale glacier topography; transmitting microwaves to measure ice thickness within the glacier boundary; receiving echoes to measure ice thickness within the glacier boundary; real-time positioning of the collected microwave signals; ice thickness data processing; and glacier reserve estimation. To improve the accuracy of glacier detection, the core device of the present invention, the glacier thickness radar, has an ice layer resolution capability of the order of centimeters and an ice thickness penetration capability of the order of 500 meters. It is lightweight, compact, and highly reliable, adapting to high-altitude and cold conditions and suitable for installation on drone platforms. Ultra-wideband, high-stability, low-phase noise signal generation technology, weak echo signal reception technology, and ultra-wideband antenna technology make the thickness radar system lightweight, compact, and highly reliable. The use of drones is suitable for climatic conditions such as high glacier altitudes and thin air, overcoming the shortcomings of relying on manpower to reach or towed measurements, thereby improving test efficiency.

Description

Technical Field

[0001] The present invention relates to a glacier ice reserve estimation method based on a flying ice measuring radar, which is a new hydrological method for calculating glacier water storage. Background Art

[0002] There are three main types of glacier reserve estimation methods. The first is the empirical formula method, including the area-volume empirical formula. The second is the ice thickness model estimation method. A variety of physical models of varying complexity have emerged to simulate ice thickness distribution, such as those based on glacier mass conservation and ice flow mechanics, shallow ice approximate theory, glacier flow laws, glacier velocity methods, or minimum methods. These estimation models infer ice thickness from glacier surface characteristics, but whether the model parameters can be transplanted to glaciers in other regions has not been proven, and the uncertainty of the parameters is unknown, resulting in large uncertainties in the model estimation results. The third type is direct field measurement methods. For example, the early drilling method, flower pole and snow pit method are the most direct methods for measuring glacier thickness, but these methods are time-consuming and labor-intensive, and can only obtain very sparse point thickness information.

[0003] Each of these three methods has its own advantages and disadvantages. Generally speaking, the first method is only suitable for rough estimates of regional glacier reserves. When applied to a single glacier, its accuracy is only on the same order of magnitude as the actual results, and the parameters of the volume-area formula vary significantly between different glacier types. The second method is only suitable for estimating glacier reserves over a small area and lacks sufficient field data for model parameter calibration. The third method, field measurement, is almost impossible to implement. Glaciers are located on plateaus, where the harsh climate conditions, such as high altitude and thin air, make precise measurements difficult and inefficient. How to quickly, efficiently, and accurately measure and estimate glacier water reserves is a key issue that needs to be addressed. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this paper proposes a method for estimating glacier ice reserves based on an unmanned aerial vehicle (UAV) equipped with a glacier thickness radar. This method uses an unmanned aerial vehicle (UAV) equipped with a glacier thickness radar to accurately measure ice thickness over a large area of ​​glaciers. Based on the measured glacier distribution and thickness, the glacier water reserves can be accurately calculated, providing an accurate scientific basis for the utilization and development of glacier water resources.

[0005] The objective of the present invention is achieved by providing a method for estimating glacier ice reserves based on a flying ice-measuring radar. The system used in the method includes: an unmanned aerial vehicle (UAV), a glacier thickness-measuring radar mounted on the UAV, and a ground-based control computer. The glacier thickness-measuring radar includes an ultra-wideband antenna unit, a transmitter, a receiver, an RTK positioning device capable of achieving centimeter-level positioning accuracy, and a signal processing device. The ultra-wideband antenna unit is a linear dipole transmitting antenna and a receiving antenna. The radar pulse signal generated by the transmitter uses an avalanche transistor and a step recovery diode to form a differential structure and a Marx cascade structure to form a carrier-free pulse generation circuit, and adopts a multi-transistor joint working mode. The receiver is provided with a sensitivity time control circuit and a time-varying gain amplifier circuit to enhance the target's reflected signal and increase the radar's detection depth.

[0006] The steps of the method are as follows:

[0007] Step 1: Obtaining large-scale glacier topography: Using publicly available satellite remote sensing data or conducting on-site mapping with topographic mapping equipment, obtain the boundary and surface elevation data of the glacier being studied.

[0008] Step 2: emitting radar waves to measure ice thickness within the glacier boundary: planning a survey line within the glacier boundary and setting a flight path in the drone according to the survey line; starting the drone to cruise along the pre-set flight path, turning on the transmitter of the glacier thickness radar, using avalanche transistors and SRD devices to generate carrier-free pulses that meet the requirements of radar detection of glaciers, and adopting a multi-tube cascade working mode to generate an ultra-wideband, low-phase-noise pulse transmission signal, and transmitting the detection radar wave through a linear dipole antenna;

[0009] Step 3: Receive the radar echo reflected from the glacier: The receiver uses a sensitivity time control circuit and a time-varying gain amplifier circuit to increase the receiver amplifier gain in a nonlinear manner as the pulse propagation time increases, thereby enhancing the target's reflected signal and increasing the radar's detection depth. The received signal is processed using an equivalent sampling method to reconstruct the radar echo signal.

[0010] Step 4: Real-time positioning of the collected radar wave signal: During the echo reception process, synchronize RTK to obtain the corresponding position coordinates. After the RTK information is collected, the latitude and longitude information is used to track and dynamically display the measurement trajectory on the electronic map. At the same time, the latitude and longitude information is stored in the database table corresponding to the measurement data. Combined with GIS, the location information corresponding to the measurement data can be displayed;

[0011] Step 5, ice thickness data processing: The processing includes the following sub-steps:

[0012] Sub-step 5.1: filtering and gain adjustment to improve radar echo quality and measurement accuracy;

[0013] In sub-step 5.2, convert the two-way travel time of the radar signal into the ice thickness H to form the ice thickness layer on the survey line. The calculation formula is:

[0014]

[0015] Where: c is the propagation speed of electromagnetic waves in vacuum; t is the round-trip travel time of electromagnetic waves in the measurement medium; ε is the relative dielectric constant of the medium; v is the propagation speed of radar pulses in the medium; d is the distance between the radar transmitting antenna and the receiving antenna;

[0016] In sub-step 5.3, the obtained ice thickness data is marked on the survey line. The data includes ice thickness information and corresponding geographic location coordinates to form a point layer.

[0017] Sub-step 5.4, overlaying the ice thickness point layer onto the glacier boundary, overlaying the thickness point layer data onto the glacier map according to the geographic coordinates, so that the thickness information on the ice layer measurement line within the glacier boundary is added;

[0018] In substep 5.5, interpolate to obtain the ice thickness distribution map for all locations within the glacier boundary. Actual measurements only provide ice thickness at the measurement lines. Using linear interpolation, estimate ice thickness for the areas between the measurement lines based on the existing ice thickness data on the measurement lines, thus obtaining ice thickness distribution data for all locations within the glacier boundary.

[0019] Step 6, glacier reserve estimation: Ice reserve estimation includes the following sub-steps:

[0020] Sub-step 6.1: Based on the ice thickness distribution data along the glacier radar survey line, generate an ice thickness contour map; divide the map into 5m or 10m intervals along the elevation direction, and plot it in GIS;

[0021] Sub-step 6.2, estimate the bottom area occupied by the glacier at the same thickness in the GIS map;

[0022] In sub-step 6.3, the volume of the glacier block is obtained by multiplying the bottom area by the partition thickness; the volumes of the blocks at other ice thickness heights can be obtained in sequence; and the total volume of the measured glacier body is obtained by cumulative summation.

[0023] The advantages and beneficial effects of the present invention are as follows: the present invention utilizes a drone equipped with an ice-penetrating radar to directly measure the ice thickness distribution of a large glacier body at low altitude, and then obtains the glacier ice reserves in combination with the glacier bedrock topography. It adapts to the requirements of harsh environmental conditions such as low temperature and vibration, and accurately calculates the distribution and thickness of the glacier. It has the characteristics of high efficiency, high precision, high reliability, miniaturization, and wide adaptability. To improve the accuracy of glacier detection, the core equipment of the present invention, the glacier thickness radar, has an ice layer resolution capability of the order of centimeters and an ice thickness penetration capability of the order of 500 meters. The use of drones is suitable for climatic conditions such as high altitude and thin air at glaciers, overcoming the shortcomings of previous reliance on manpower or towing measurements. While reducing risks, it significantly improves test efficiency and has the characteristics of fast, large-scale, and high-precision measurement. The ice thickness data obtained by the present invention is accurate and can efficiently calculate ice reserves, which has great value for engineering promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present invention will be further described below with reference to the accompanying drawings and examples.

[0025] Figure 1 is a block diagram of the system principles used in the method according to an embodiment of the present invention;

[0026] Figure 2 is a flow chart of the method according to an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of the basic principle of glacier thickness radar measurement according to an embodiment of the present invention;

[0028] Figure 4 Schematic diagram of equivalent sampling of the improved glacier thickness radar according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] Example:

[0030] This embodiment is a method for estimating glacier ice reserves based on a flying ice radar. The system used in the method is as follows: Figure 1 As shown, it includes: a UAV 1, a glacier thickness measuring radar mounted on the UAV, and a control computer 2 located on the ground. Figure 1The glacier thickness radar includes an ultra-wideband antenna unit, a transmitter, a receiver, an RTK (Real Time Kinematic) positioning device capable of achieving centimeter-level positioning accuracy, and a signal processing device. The ultra-wideband antenna unit is a linear dipole transmitting antenna and a receiving antenna. The radar pulse signal of the transmitter is generated by using an avalanche transistor and a step recovery diode to form a differential structure and a Marx cascade structure to form a carrier-free pulse generation circuit, and adopts a multi-tube joint working mode. The receiver is provided with a sensitivity time control circuit and a time-varying gain amplifier circuit, that is, the gain of the receiver amplifier increases in a nonlinear form (mainly exponential form) as the pulse propagation time increases, which is used to enhance the target's reflected signal and increase the radar's detection depth. The receiver uses an equivalent sampling method to reconstruct the radar echo signal, and the receiver and the control computer are wirelessly connected.

[0031] The core equipment of the glacier thickness radar is carried by a drone. The drone can be a 4-axis, 8-propeller multi-rotor configuration with a maximum payload of 30 kg and a maximum range of 16 km when fully loaded. Conventional fixed-wing drones can also be used. The entire drone can operate in ambient temperatures ranging from -20°C to 40°C and has a maximum altitude of 8,000 meters. Combining a large payload, long range, strong signal strength, and high wind resistance, it is suitable for transportation in cold and high altitude environments and various emergency scenarios.

[0032] The technical indicators of the receiver include: sensitivity: 1mV, sampling rate is 2GHz (sampling interval 0.5ns).

[0033] Ultra-wideband antenna unit: To avoid the influence of clutter, glacier thickness radars in complex electromagnetic environments generally require shielded butterfly antennas. These antennas have the advantage of good radar echo signals, but are relatively large and heavy. Since the glacier is located in an uninhabited area with little clutter, this embodiment adopts the following measures to achieve lightweight and miniaturization:

[0034] Based on theoretical simulations and experimental measurements, the glacier thickness radar uses a broadband radar wave with a center frequency of 100 MHz. To reduce antenna weight, an impedance loading method based on the Wu-King theory was employed, using distributed resistance loading to broaden the antenna's bandwidth, resulting in superior time-domain and broadband characteristics. The glacier thickness radar uses the aforementioned unshielded antenna, with an antenna arm length of 75 cm, a total length of 150 cm, a width of 10 cm, and a weight of approximately 2 kg (excluding the transmitter and receiver).

[0035] The steps of the method are as follows: Figure 2 As shown:

[0036] Step 1: Obtaining the large-scale topography of the glacier: Using publicly available satellite remote sensing data or conducting on-site mapping with topographic mapping equipment, obtain the boundary and surface elevation data of the glacier being studied; obtaining this topographic data and surface elevation data is a prerequisite for calculating glacier thickness.

[0037] The terrain and elevation data of the earth's surface can be obtained from data or models publicly available on the Internet. For large-scale terrain of glaciers, remote sensing image data (such as Landsat 8, source: USGS, USA) and digital surface model (such as DSM, source: JAXA, Japan) terrain data can be used to obtain glacier boundaries. The above-mentioned drones can also be used to directly carry out terrain mapping to obtain glacier boundaries and glacier surface elevation data.

[0038] Step 2: emitting radar waves to measure ice thickness within the glacier boundary: planning a survey line within the glacier boundary and setting a flight path in the drone according to the survey line; starting the drone to cruise along the pre-set flight path, turning on the transmitter of the glacier thickness radar, using avalanche transistors and SRD devices to generate carrier-free pulses that meet the requirements of radar detection of glaciers, and adopting a multi-tube cascade working mode to generate an ultra-wideband, low-phase-noise pulse transmission signal, and transmitting the detection radar wave through a linear dipole antenna;

[0039] Generating high-quality ultra-wideband pulsed signals is the core technology of this embodiment's detection radar. The high voltage and wide bandwidth of the pulsed signal improve the radar's detection range and resolution, while the stability of its waveform impacts imaging quality. Existing tunnel diodes and step recovery diodes cannot meet the pulse source requirements of ground-penetrating radar. Therefore, this embodiment uses an avalanche diode as the high-speed switch for the pulse source, capable of generating very high-speed, narrow pulses.

[0040] Glacier-measuring radars place stringent demands on the upper repetition rate limit and pulse stability of their high-resolution ground-penetrating radar pulse transmitters. These performance indicators directly impact radar detection accuracy and range. Therefore, to meet the application requirements of high-resolution radar pulse transmitters, the traditional MARX pulse generation circuit was modified by switching the triggering mechanism from series to parallel. This significantly improves both the upper repetition rate limit and pulse stability, achieving a maximum repetition rate of 400 kHz with minimal waveform jitter. The improved nanosecond ground-penetrating radar transmitter circuit includes a power supply circuit, a trigger signal module, a driver amplifier module, and an avalanche transistor pulse generation circuit.

[0041] The basic principles of glacier thickness radar measurement are shown in Figure 3The transmitting antenna onboard the drone transmits electromagnetic waves in a direction toward the ice surface and penetrates the ice. The high-frequency electromagnetic waves are reflected by the subglacial strata or target objects with different electrical properties and then return to the receiving antenna. The two-way travel time t of the electromagnetic waves in the measurement medium can be calculated using the following formula:

[0042]

[0043] Where: H is the thickness of the measured medium; v is the propagation speed of the radar pulse in the medium; d is the distance between the radar transmitting antenna and the receiving antenna; the calculation formula of v is:

[0044]

[0045] Where: c is the propagation speed of electromagnetic waves in a vacuum (0.29979 m / ns), and ε is the relative dielectric constant of the medium.

[0046] Therefore, the thickness of the measured medium can be calculated using the following formula:

[0047]

[0048] Differences in dielectric properties between materials are a prerequisite for GPR detection of target media. The dielectric constant of air is 1, ice is between 3-5, and rock (silt) is between 5-30. The significant differences in dielectric properties between ice and the underlying strata provide the foundation for accurate GPR measurements. As high-frequency electromagnetic waves transmitted by GPR propagate, their field intensity and waveform vary with the dielectric properties and geometry of the medium. Radar waves received by the receiving antenna are analyzed for amplitude, waveform, and frequency to generate a radar image. Post-processing of this image allows the thickness of the target medium to be determined.

[0049] Step 3: Receive and measure radar echoes reflected from within the glacier. According to radar theory and practice, as a carrier-free pulse signal propagates through ice and water, its energy decays exponentially as the penetration depth increases. To better compensate for nonlinear attenuation, the receiver uses a sensitivity time control (STC) circuit and a time-varying gain (TVG) amplifier circuit to increase the receiver's amplifier gain nonlinearly (primarily exponentially) as the pulse propagation time increases. This increases the target's reflected signal and increases the radar's detection depth. The received signal is processed using equivalent sampling to reconstruct the radar echo signal.

[0050] Equivalent sampling is a type of transformed sampling that utilizes the periodic or quasi-periodic repetition characteristics of the received signal and performs sampling only once or several times in each period. After multiple repeated periods of the signal, all samples that can reconstruct the waveform within one period of the signal are obtained.

[0051] The traditional sequential equivalent sampling scheme for ground-penetrating radars collects only one point in each time period. If information on 512 points of an echo signal is to be collected, 512 time periods are required. For high-speed, cruise-type ice-measuring radars, this cannot meet the acquisition speed and lateral resolution requirements. Furthermore, if the antenna moves too quickly, it is difficult to ensure the similarity of the 512 echo signals, rendering the reconstructed signal unusable for actual measurement. This embodiment improves equivalent sampling to reduce the number of equivalent sampling periods. The principle is as follows:

[0052] Assume that the repetition period is T and the sampling time within the period is t p , collect K points, and the interval between sampling points in the cycle is τ Δ , a total of M cycles are collected to reconstruct the original signal. The equivalent sampling step time between each cycle is Δt. A total of N points are collected, and the relationship between them satisfies:

[0053] K = T / τ Δ (4)

[0054] M = τ Δ / Δt (5)

[0055] N = M × K (6)

[0056] Sampling time t mk It can be expressed as:

[0057] t mk =(T+Δt)+ k τ Δ = m T s + k τ Δ (7)

[0058] Where: k = 0, 1, 2, ..., K-1; m = 0, 1, 2, ..., M-1. The traditional equivalent only collects one point per step, while this embodiment collects K points each time. From formula (5), it can be seen that the number of equivalent sampling cycles M depends on the interval τ between sampling points within the cycle. Δ The step time Δt between cycles is determined by Δt. Since Δt also determines the accuracy of equivalent sampling, reducing Δt without sacrificing accuracy can reduce the number of cycles and improve the equivalent sampling speed. In this embodiment, Δt is the sampling interval of the sampling chip, so the system operating rate depends on the operating frequency of the AD chip. The improved equivalent sampling working process is as follows: Figure 4 shown.

[0059] Figure 4 The schematic diagram of hybrid equivalent sampling after sequential equivalent sampling is shown in Figure 1. Multiple information points are collected in one sampling cycle. In the figure, f(t) is the original signal to be collected, and f T(t) is the original signal that repeats with a period of T, f △ (t) is T s =T+△t is the periodic pulse trigger signal, △t is the delay step, t p is the effective signal time of the acquisition, τ △ is the real-time sampling interval, f s1 (t) is the real-time sampling reconstruction result with △t as the sampling interval, f s4 (t) is the result of fast equivalent sampling reconstruction. Figure 4 It can be seen from the figure that unlike the traditional equivalent sampling, the improved equivalent sampling collects K points each time, so the number of required cycles is reduced to M (=N / K), which greatly improves the information acquisition rate of the system.

[0060] In the process of equivalent sampling, the technology for generating accurate time steps is very critical. The accuracy of the time step directly affects the degree of consistency between the reconstructed waveform and the real waveform. In the glacier thickness radar, the time step signal is generated by the timing control module and implemented using a programmable delay chip. The minimum time delay step Δt of the chip is 2ps, and the maximum achievable delay is about 2ns. Δt varies with the operating temperature and operating voltage. For some key circuits of the timing control module, voltage stabilization and temperature control measures are adopted to monitor the working environment of the programmable delay chip. Based on the PID algorithm in control theory, the working environment parameters are dynamically adjusted using voltage stabilization and temperature control devices, thereby improving the stability and accuracy of the time step.

[0061] Step 4: Real-time positioning of the collected radar wave signals: During the echo reception process, RTK is used to obtain the corresponding position coordinates. After the RTK information is collected, the longitude and latitude information is used to track and dynamically display the measurement trajectory on an electronic map. The longitude and latitude information is stored in the database table corresponding to the measurement data. In combination with the GIS (Geographic Information System), the location information corresponding to the measurement data is displayed;

[0062] RTK positioning is a real-time, dynamic positioning technology based on carrier phase observations. It provides real-time, three-dimensional positioning results for a mobile station within a specified coordinate system, achieving centimeter-level accuracy. The RTK receiver connects to a ground-based control computer via an RS232 serial port or wireless communication interface to collect and store data and control and adjust flight attitude and corresponding positioning data.

[0063] Step 5, ice thickness data processing: The processing includes the following sub-steps:

[0064] Sub-step 5.1: filtering and gain adjustment to improve radar echo quality and measurement accuracy;

[0065] Actual detection environments present a variety of interference sources, such as electromagnetic interference from surrounding electrical equipment and surface clutter. Left unprocessed, these interference signals can mix with the target echo signal, severely impacting the identification and measurement of underground targets. Filtering removes interference signals outside the target signal's frequency band based on the signal's frequency characteristics. Bandpass filters precisely select the target signal's frequency band, enhancing the useful signal's clarity. Because ground-penetrating radar signals attenuate as they propagate beneath the ice, echo signal strength gradually weakens with increasing depth. Nonlinear gain adjustment is employed to compensate for echo signals at different depths, enhancing the strength of echo signals from deep targets and making them easier to compare and analyze with those from shallower targets. Filtering and gain adjustment ensure accurate signal processing across the entire detection depth range, thereby improving measurement accuracy.

[0066] In sub-step 5.2, convert the two-way travel time of the radar signal into the ice thickness H to form the ice thickness layer on the survey line. The calculation formula is:

[0067]

[0068] Where: c is the propagation speed of electromagnetic waves in a vacuum (0.29979 m / ns); t is the round-trip travel time of the electromagnetic wave in the measurement medium; ε is the relative dielectric constant of the medium; v is the propagation speed of the radar pulse in the medium; d is the distance between the radar transmitting antenna and the receiving antenna;

[0069] In sub-step 5.3, the obtained ice thickness data is marked on the survey line. The data includes ice thickness information and corresponding geographic location coordinates (such as longitude and latitude) to form a point layer.

[0070] Sub-step 5.4, overlaying the ice thickness point layer onto the glacier boundary, overlaying the thickness point layer data onto the glacier map according to the geographic coordinates, so that the thickness information on the ice layer measurement line within the glacier boundary is added;

[0071] In substep 5.5, interpolate to obtain the ice thickness distribution map for all locations within the glacier boundary. Actual measurements only provide ice thickness at the measurement lines. Using linear interpolation, estimate ice thickness for the areas between the measurement lines based on the existing ice thickness data on the measurement lines, thus obtaining ice thickness distribution data for all locations within the glacier boundary.

[0072] Step 6, glacier reserve estimation: Based on the obtained glacier body and ice thickness distribution map, the glacier ice reserve is estimated using the block volume method, thickness integral method, etc.

[0073] Based on the obtained glacier mass and ice thickness distribution maps, the block volume method is used to estimate the glacier ice reserves. The glacier area is divided into multiple area units according to thickness. The volume of each unit is approximately calculated by multiplying the unit area by the corresponding glacier thickness. The volumes of all units are then summed. The ice reserve estimation specifically includes the following sub-steps:

[0074] Sub-step 6.1: Based on the ice thickness distribution set along the glacier radar survey line, generate an ice thickness contour map; divide it into 5m or 10m intervals along the elevation direction, and plot it in GIS or other software;

[0075] Sub-step 6.2, estimate the bottom area occupied by the glacier at the same thickness in the GIS map;

[0076] In sub-step 6.3, the volume of the glacier block is obtained by multiplying the bottom area by the partition thickness; the volumes of the blocks at other ice thickness heights can be obtained in sequence; and the total volume of the measured glacier body is obtained by cumulative summation.

[0077] Application examples:

[0078] Dongkemadi Glacier is located in Tanggula Mountain. It is a continental glacier. It is a composite valley glacier formed by the confluence of a south-facing main glacier (Big Dongkemadi Glacier) and a southwest-facing branch glacier (Small Dongkemadi Glacier). The glacier area is 16 km 2 The Xiaodongkemadi Glacier (33°04'N, 92°04'E) is a typical continental glacier with a terminal elevation of 5420m and a highest point of 5919m. The glacier's surface is mainly distributed between 5550-5790m above sea level.

[0079] The drone glacier thickness measurement radar system described in this embodiment was used to measure the Xiaodongkemadi Glacier at an altitude of more than 5,000 meters. The drone flew along the survey line for measurement (for verification purposes only), with a total length of approximately 4 km, and obtained a relatively detailed distribution of glacier thickness. The measured data showed that the echo signal of the ice-rock interface of the glacier radar was clear, and the glacier thickness could be accurately obtained. The glacier thickness distribution of each survey line measured on the Xiaodongkemadi Glacier showed an overall trend of greater ice thickness in the middle, gradually decreasing on both sides, and less ice thickness at low altitudes. As the altitude increased, the ice thickness gradually increased. When the altitude exceeded 5,600 meters, the rate of increase in glacier thickness gradually decreased. The maximum measured glacier thickness was 125.54 meters (5,609 meters above sea level).

[0080] The three-dimensional distribution and thickness contour map of the Xiaodongkemadi Glacier were obtained by performing difference processing using the minimum curvature method. Based on this map, the glacier reserves within the measurement range were calculated using the block volume method, and the measured glacier reserves were found to be 69.467 million m 3 .

[0081] Finally, it should be noted that the above is only used to illustrate the technical solution of the present invention and is not a limitation. Although the present invention has been described in detail with reference to the preferred arrangement scheme, ordinary technicians in this field should understand that the technical solution of the present invention (such as the form of the drone, the form of the ground penetrating radar, the installation method on the drone, the sequence of steps, etc.) can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for estimating glacier ice reserves based on a flying ice radar, the system used in the method comprising: A 4-axis, 8-propeller, multi-rotor drone and a glacier thickness measurement radar mounted on the drone, as well as a ground-based control computer; the glacier thickness measurement radar includes an ultra-wideband antenna unit, a transmitter, a receiver, an RTK positioning device capable of achieving centimeter-level positioning accuracy, and a signal processing device; the ultra-wideband antenna unit is a linear dipole transmitting antenna and a receiving antenna; the transmitter generates radar pulse signals using a differential structure and a Marx cascade structure composed of an avalanche transistor and a step recovery diode to form a carrier-free pulse generation circuit, and adopts a multi-tube joint working mode; the receiver is provided with a sensitivity time control circuit and a time-varying gain amplifier circuit to enhance the target's reflected signal and increase the radar's detection depth; The method is characterized in that the steps are as follows: Step 1: Obtaining the large-scale topography of the glacier: Either using publicly available satellite remote sensing data or digital models, or conducting on-site mapping with topographic mapping equipment, obtain the boundary and surface elevation data of the glacier being studied; Step 2: Launch radar waves to measure ice thickness within the glacier boundary: plan a survey line within the glacier boundary and set a flight path in the drone according to the survey line; start the drone to cruise according to the pre-set flight path, turn on the transmitter of the glacier thickness radar, use avalanche transistors and SRD devices to generate carrier-free pulses that meet the requirements of radar detection of glaciers, and use a multi-tube cascade working mode to generate ultra-wideband low-phase noise pulse transmission signals, and transmit detection radar waves through a linear dipole antenna; the glacier thickness radar uses a 100MH based on theoretical simulation and experimental measurements. To reduce antenna weight by designing wide-band radar waves with a center frequency of 0.1, and considering the possibility of being carried by drones, an impedance loading method based on the Wu-King theory was adopted. Distributed resistance loading was used to broaden the antenna's bandwidth, giving it better time-domain and broadband characteristics. Furthermore, to meet the application requirements of high-resolution radar pulse transmitters, the traditional MARX pulse generation circuit was debugged and improved by changing the circuit triggering mode from series triggering to parallel triggering. This significantly improved the repetition rate upper limit and pulse stability, achieving a maximum repetition rate of 400 kHz and minimal waveform jitter. Step 3: Receive the radar echo reflected from the glacier. The receiver uses a sensitivity time control circuit and a time-varying gain amplifier circuit to increase the amplifier gain of the receiver in a nonlinear manner as the pulse propagation time increases, thereby enhancing the target's reflected signal and increasing the radar's detection depth. The received signal is processed using an improved equivalent sampling method to reconstruct the radar echo signal. The improved equivalent sampling method is as follows: Assume that the repetition period is T and the sampling time within the period is t p , collect K points, and the interval between sampling points in the cycle is τ Δ , a total of M cycles are collected to reconstruct the original signal, and the equivalent sampling step time between each cycle is Δ t , a total of N points are collected, and the relationship between them satisfies: K=T / τ Δ M=τ Δ / Δt N=M×K Sampling point time t mk It can be expressed as: t mk =(T+Δt)+kτ Δ =m T s +kτ Δ Where: k = 0, 1, 2, ..., K-1; m = 0, 1, 2, ..., M-1; Step 4: Real-time positioning of the collected radar wave signal: During the echo reception process, synchronize RTK to obtain the corresponding position coordinates. After the RTK information is collected, the latitude and longitude information is used to track and dynamically display the measurement trajectory on the electronic map. At the same time, the latitude and longitude information is stored in the database table corresponding to the measurement data. Combined with GIS, the location information corresponding to the measurement data can be displayed; Step 5, ice thickness data processing: The processing includes the following sub-steps: Sub-step 5.1: filtering and gain adjustment to improve radar echo quality and measurement accuracy; In sub-step 5.2, convert the two-way travel time of the radar signal into the ice thickness H to form the ice thickness layer on the survey line. The calculation formula is: Where: c is the propagation speed of electromagnetic waves in vacuum; t is the round-trip travel time of electromagnetic waves in the measurement medium; ε is the relative dielectric constant of the medium; v is the propagation speed of radar pulses in the medium; d is the distance between the radar transmitting antenna and the receiving antenna; In sub-step 5.3, the obtained ice thickness data is marked on the survey line. The data includes ice thickness information and corresponding geographic location coordinates to form a point layer. Sub-step 5.4, overlaying the ice thickness point layer onto the glacier boundary, overlaying the thickness point layer data onto the glacier map according to the geographic coordinates, so that the thickness information on the ice layer measurement line within the glacier boundary is added; In substep 5.5, interpolate to obtain the ice thickness distribution map for all locations within the glacier boundary. During actual measurement, only the ice thickness at the survey line location can be obtained. Using linear interpolation, based on the existing ice thickness data on the survey lines, estimate the ice thickness for the area between the survey lines, thereby obtaining ice thickness distribution data for all locations within the glacier boundary. Step 6, Glacier Reserve Estimation: Reserve estimation includes the following sub-steps: Sub-step 6.1: Based on the ice thickness distribution data along the glacier radar survey line, generate an ice thickness contour map; divide the map into 5m or 10m intervals along the elevation direction, and plot it in GIS; Sub-step 6.2, estimate the bottom area occupied by the glacier at the same thickness in the GIS map; In sub-step 6.3, the volume of the glacier block is obtained by multiplying the bottom area by the partition thickness; the volumes of the blocks at other ice thickness heights can be obtained in sequence; and the total volume of the measured glacier body is obtained by cumulative summation.

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