Ultra-low frequency magnetotelluric data sampling method and device for deep mineral resource exploration

Abstract

This invention relates to a method and apparatus for sampling ultra-low frequency magnetotelluric data for deep mineral resource exploration. The method includes: converting the collected ultra-low frequency magnetotelluric data for deep mineral resource exploration into digital magnetotelluric data through analog-to-digital conversion, and organizing the data to be downsampled; establishing a filter based on a downsampling factor M, and setting the number of filter coefficients; dividing all continuous sample points in the data to be downsampled into groups of M, sequentially moving them into the filter for filtering and sampling, and moving them out of the filter, calculating the filter output during this process, and sorting all filter outputs to obtain the downsampled data. This invention uses a downsampling factor to buffer the sample points before performing multi-point calculation, shifting, and output, reducing the number of shifting steps and calculations, thereby reducing the number of operations and the load on the microcontroller (MCU), and improving computational efficiency.

Description

Technical Field

[0001] This invention belongs to the field of mineral resource exploration filtering and sampling technology, specifically relating to an ultra-low frequency magnetotelluric data sampling method and device for deep mineral resource exploration. Background Technology

[0002] Electromagnetic exploration is one of the effective means of resource exploration. It has many types and strong adaptability, and is widely used in fields such as deep structural exploration, mineral resource exploration, and hydrological and engineering surveys.

[0003] Magnetotelluric (MT) is currently the electromagnetic exploration method with the greatest depth, covering a frequency range of approximately 0.001 Hz to 320 Hz. By employing MT for detailed data processing and inversion, we can gain a deeper understanding of the Earth's deep-seated material composition, thermal state, and dynamic processes, providing crucial information for Earth science research from a profound perspective.

[0004] To implement magnetotelluric and long-period (ultra-low frequency) magnetotelluric methods, broadband signal acquisition is required. Broadband signal acquisition employs various analog-to-digital converters (ADCs) with different sampling rates. Synchronizing these ADCs at different sampling rates is challenging; improper synchronization can introduce errors. Furthermore, the data stitching and processing between different sampling rates are complex, increasing the difficulty of algorithm design and implementation. This negatively impacts the quality and accuracy of the final acquired signal, significantly increasing costs and power consumption. Summary of the Invention

[0005] In order to overcome the problems existing in the prior art, the present invention provides an ultra-low frequency magnetotelluric data sampling method and device for deep mineral resource exploration, which is used to overcome the current defects.

[0006] A sampling method for ultra-low frequency magnetotelluric data for deep mineral resource exploration includes: S1. Converting the collected ultra-low frequency magnetotelluric data for deep mineral resource exploration from analog to digital to obtain digitized magnetotelluric data, and organizing the digitized magnetotelluric data to form sampling data to be sampled;

[0007] S2. Based on the required downsampling factor M, establish a filter, and set the number of filter coefficients to be an integer multiple of M, where M is a positive integer greater than or equal to 1;

[0008] S3. Divide all continuous sample points in the data to be downsampled into groups of M continuous sample points. Send the group sample points into the filter for filtering and sampling in sequence. Specifically, move the continuous group sample points into the filter in sequence and move them in the filter in sequence. Each group sample point moves from its current position to the next position until it is removed from the filter. Each time the group sample points in the filter are moved, filtering and sampling are performed, and the output of the filter is calculated. Sort the outputs of all filters to obtain the downsampled data.

[0009] In addition to the aspects described above and any possible implementations, a further implementation is provided in which the filter is a finite impulse response (FIR) anti-spoofing low-pass filter.

[0010] In addition to the aspects described above and any possible implementation, a further implementation is provided in which the output is calculated as the sum of the products of each sample in all group samples of the filter and the filter coefficients corresponding to the shift-in position.

[0011] As described above, and in accordance with any possible implementation, an implementation is further provided, wherein S3 specifically includes:

[0012] S31. When the first set of samples is input into the filter, the output of the filter is the sum of the products of each sample in the first set of samples and the filter coefficients corresponding to the shifted-in positions;

[0013] S32. The second set of samples continues to be input into the filter. It is determined whether the sum of the number of the first set of samples and the second set of samples is greater than the number of filter coefficients. If so, the first set of samples is removed from the filter. The filter performs filtering and sampling on the second set of samples. At this time, the output of the filter is the sum of the products of each sample in the second set of samples and the filter coefficients corresponding to the moved-in positions. The subsequent sets of samples after the second set of samples are input into the filter, and the above operation is performed. The outputs of all filters are sorted to obtain downsampled data.

[0014] In addition to the aspects and any possible implementations described above, an implementation is further provided in which, if the sum of the number of the first set of samples and the second set of samples is less than or equal to the number of filter coefficients, step S34 is executed to calculate the output of the filter, which is the sum of the products of each sample in the first set of samples and the coefficients of the filter at the corresponding positions.

[0015] S35. Subsequent sample groups after the second set of sample points are moved into the filter in sequence. The sample groups that were moved in beforehand move in the filter in sequence. Each set of sample points moves from its current position to the next position until it is moved out of the filter. Each time the filter moves, it filters and samples all the sample groups in it. Its output is the sum of the products of each sample point in each set of sample points in the filter and the coefficients of the filter that has moved to the corresponding position. The results of all the sums are sorted to obtain the downsampled data.

[0016] In addition to the aspects described above and any possible implementation, a further implementation is provided in which downsampled data is used as the data to be sampled, and other downsampling factors are used to filter and downsample again in steps S2 and S3 to obtain other downsampled data, wherein the number of other downsampling factors is at least one.

[0017] The present invention also provides an ultra-low frequency magnetotelluric data sampling device for deep mineral resource exploration, the device comprising: a connected acquisition station and an ultra-low frequency magnetotelluric detection and acquisition component;

[0018] The data acquisition component includes several different sensors installed on-site;

[0019] The data acquisition station includes a control unit, which communicates with each sensor and performs filtering and extraction processing on the information collected by each sensor using the method described above.

[0020] In addition to the aspects and any possible implementations described above, an implementation is further provided in which the different sensors are superconducting magnetic sensors, digital fluxgate sensors, inductive magnetic field sensors, and electric field sensors.

[0021] In addition to the aspects described above and any possible implementations, a further implementation is provided in which the acquisition station further includes a switch, a data acquisition channel, and a power supply module.

[0022] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the control unit is implemented using a microcontroller (MCU).

[0023] Beneficial effects of the present invention

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention discloses an ultra-low frequency magnetotelluric data sampling method for deep mineral resource exploration. The method involves converting the collected ultra-low frequency magnetotelluric data for deep mineral resource exploration into digital magnetotelluric data via analog-to-digital conversion. The digitized magnetotelluric data is then organized to form downsampled data. A filter is established based on the required downsampling factor M, and the number of filter coefficients is set to be an integer multiple of M. All continuous sample points in the downsampled data are divided into groups of M consecutive sample points. These groups are then sequentially fed into the filter for filtering and sampling. Specifically, the consecutive sample points are sequentially moved into the filter and moved sequentially within the filter, with each group moving from its current position to the next position until it exits the filter. Each time a group of sample points moves within the filter, filtering and sampling are performed, and the filter output is calculated. All filter outputs are then sorted to obtain the downsampled data. This invention analyzes the calculation and extraction (downsampling) steps corresponding to filtering and downsampling, and realizes the buffering of sample points based on the downsampling factor, followed by the shifting and calculation of various sample points for output. This reduces the number of shifting steps and operations, lowers the number of operations and load on the microcontroller MCU, and improves computational efficiency. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0027] Figure 2 The filtering and extraction flowchart of this invention;

[0028] Figure 3 This is a flowchart of the optimized filtering and extraction process of the present invention;

[0029] Figure 4 This is a schematic diagram of the buffer sample point shifting of the present invention;

[0030] Figure 5 This is a schematic diagram of the downsampling process according to an embodiment of the present invention;

[0031] Figure 6 This is a schematic diagram of the extraction and downsampling process according to an embodiment of the present invention;

[0032] Figure 7 This is a schematic diagram of the connection of the device of the present invention;

[0033] Figure 8 This is a schematic diagram of the internal structure of the data acquisition station of the present invention. Detailed Implementation

[0034] To better understand the technical solution of this invention, the content of this invention includes, but is not limited to, the specific embodiments described below. Similar technologies and methods should be considered within the scope of protection of this invention. To make the technical problems to be solved, the technical solutions, and advantages of this invention clearer, a detailed description will be provided below in conjunction with the accompanying drawings and specific embodiments.

[0035] It should be understood that the embodiments described in this invention are merely some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0036] like Figure 1 As shown, the ultra-low frequency magnetotelluric data sampling method for deep mineral resource exploration of the present invention includes: S1. Converting the collected ultra-low frequency magnetotelluric data for deep mineral resource exploration into digital magnetotelluric data using a single analog-to-digital converter (ADC), and processing the digital magnetotelluric data to form sampling data to be reduced; the digital magnetotelluric data is the original high sampling rate data, which is processed and used as the sampling data to be reduced, with a corresponding sampling rate of (The sampling rate is usually a positive integer), 1 second has The method converts analog signals into corresponding digital signals using a single analog-to-digital converter (ADC). The advantage of this method is that it allows for unified and continuous acquisition of signals across the entire wideband, avoiding errors caused by different sampling rates and ensuring signal integrity and accuracy. Data processing is relatively simple, enabling rapid subsequent analysis of the acquired data. However, using a single high-sampling-rate ADC to cover the entire frequency band can result in excessively large amounts of sampled data when the target frequency is low. Furthermore, it can lead to excessive redundant data acquisition in low-frequency bands, wasting resources. Therefore, in this invention, when using a single ADC, the method employed here simultaneously performs filtering and sampling when processing the data acquired by the single ADC.

[0037] S2. Based on the required downsampling factor M, where M is a positive integer greater than or equal to 1, establish a filter and set the number of filter coefficients to be a multiple of M;

[0038] S3. Divide all continuous samples in the data to be downsampled into groups of M. Sequentially feed these groups of samples into a filter for filtering and sampling. Specifically, move the continuous groups of samples sequentially into the filter and then sequentially within it, moving each group from its current position to the next position until it exits the filter. Each time a group of samples moves, filtering and sampling are performed, and the filter output is calculated. Sort all the filter outputs to obtain the downsampled data. This result is the sum of the products of each sample in all groups of samples in the filter and the filter coefficients corresponding to the moved-in positions.

[0039] Preferably, the filtering is implemented using a finite impulse response (FIR) inverse spoofing low-pass filter, which describes the input-output relationship as a rational transfer function, the form of which is shown in the formula. (1)

[0040] Among them, b(1), b(2), ..., b(n) b +1) represents the filter coefficients, n b The filter order is the filter coefficient, which can be preset as required and stored in the buffer of the microcontroller MCU.

[0041] Preferably, step S2 is performed in the buffer of the microcontroller (MCU), the size of which is the order of the anti-spoofing low-pass filter plus 1, such as... Figure 3 As shown, a buffer is established in the microcontroller (MCU), and the data to be sampled is input as the acquisition data into the filter established in the buffer. The size of the buffer is set according to the number of coefficients of the filter.

[0042] Preferably, step S3 can be implemented using the following process:

[0043] S31. When the first set of samples is input into the filter, the output of the filter is the sum of the products of each sample in the first set of samples and the filter coefficients corresponding to the shifted-in positions;

[0044] S32. The second set of samples continues to be input into the filter. It is determined whether the sum of the number of the first set of samples and the second set of samples is greater than the number of filters. If so, it means that the number of filter coefficients is the same as the number of samples in each set of samples, i.e., both are M. Then the first set of samples is moved out of the filter and the second set of samples is moved into the filter. The filter performs filtering and sampling on the second set of samples. At this time, the output of the filter is the sum of the products of each sample in the second set of samples and the filter coefficients corresponding to the moved-in position. The subsequent sets of samples after the second set of samples are input into the filter, and the previous set of samples is moved out of the filter. The above operation is performed on all of them. Finally, the outputs of all filters are sorted to obtain downsampled data.

[0045] If the sum of the number of samples in the first group and the second group is less than or equal to the number of filters, then proceed to step S34. Calculate the output of the filter, which is the sum of the products of each sample in the first group and the second group and the coefficients of the filter at the corresponding position.

[0046] S35. Subsequent sample points after the second set of sample points are moved into the filter in sequence. The sample points that were moved in beforehand move in the filter in sequence. Each sample point moves from its current position to the next position until it is moved out of the filter. Each time the filter moves, it filters and samples all sample points in it. Its output is the sum of the products of each sample point in each set of sample points in the filter and the coefficients of the filter that has moved to the corresponding position. The results of all the sums are sorted to obtain the downsampled data.

[0047] In the above processing, the present invention does not compare the number of samples in the moved-in group with the number of filter coefficients in advance. The samples are moved first, and the judgment is made after the first group of samples is moved. This operation speeds up the moving speed and improves the calculation efficiency.

[0048] Taking M=3 as an example, when the first set of samples is input into the filter, the output of the filter is the sum of the products of each sample in the first set and the filter coefficients corresponding to the shifted-in positions, that is... (2);

[0049] Then, the second set of samples is input into the filter. At this point, it is determined whether the sum of the number of samples in the first and second sets is greater than the number of filters. If so, it indicates that there are only three coefficients b(1), b(2), and b(3) in the filter. If the first set of samples is not moved out, the second set of samples cannot be moved in. Therefore, the first set of samples is moved out of the filter, and the second set of samples is moved into the filter. At this point, the output of the filter is the sum of the products of each sample in the second set and the filter coefficient corresponding to the moved-in position, i.e. (3) When inputting subsequent sample points after the second set of sample points into the filter, the above operation process is performed, and the calculation is performed using formula (3) until the last output is obtained: (4), where n takes the value of 3, ..., ∞, sort all the sums to obtain the downsampled data. .

[0050] If the sum of the number of samples in the first group and the second group is less than or equal to the number of filters (i.e., the sum of the number of samples in the first group and the second group is 6, but the number of filter coefficients is 9), then proceed to step S34. First, calculate the filter output, which is the sum of the products of each sample in the first group and the second group and the corresponding filter coefficients at the moved positions. Figure 4 As shown in process (3), the output at this time is (5);

[0051] S35. Subsequent sample points after the second group of samples are sequentially moved into the filter. The previously moved sample points move sequentially within the filter, each sample point moving from its current position to the next position, until it is removed from the filter. During each movement, the filter filters and samples all sample points within that group. After this movement, the filter output is the sum of the products of each sample point in each group of samples and the coefficients of the filter at the corresponding position. All these sums are sorted to obtain the downsampled data, such as... Figure 4 The processes (4) and (5) are shown in the middle.

[0052] Preferably, S3 can also be implemented using the following specific process:

[0053] Corresponding to the consecutive sample points of the data to be downsampled, P positions can be set in the filter, each with M coefficients, where P is a positive integer greater than or equal to 1. Based on the value of P and its relationship to the sequence number of each sample point to be shifted into the filter, the filter output corresponding to each shift in the filter is calculated. All filter outputs are then sorted to obtain the downsampled data corresponding to the downsampling factor. The specific process is as follows:

[0054] S311. If P=1, that is, the number of filter coefficients is equal to the number of samples M in each group of samples, and there are N groups of continuous samples to be downsampled, where N takes the values ​​1, 2, ..., ∞, then each group of samples is input into the filter in sequence, and each sample in each group of samples is multiplied by each coefficient of the corresponding filter. The sum of the products is used as the output of the filter, resulting in a total of N filter outputs. The N filter outputs are sorted to obtain the downsampled data. In this case, when each group of samples is input into the filter, the previous group of samples is removed from the filter. After the filter performs filtering and sampling on the group of samples, the group of samples is removed from the filter thereafter.

[0055] S312. If P≠1, that is, the number of coefficients in the filter is at least 2M or more, and there are N consecutive samples of the data to be downsampled, where N takes the values ​​1, 2, ..., ∞, then the following steps are included:

[0056] S3121. When P is greater than or equal to the N-1th group of sample points to be moved in, the first group of sample points to the N-1th group of sample points are moved into the filter in sequence. Each time a group of sample points moves in the filter, the M sample points in each group moved into the filter are multiplied by the M coefficients of each position of the corresponding filter. The sum of the products obtained after each move is used as the output of the filter for that move. Thus, a total of P filter outputs corresponding to the N-1th group of sample points are obtained.

[0057] S3122. When the Nth group of samples is to be moved in, if P is less than the sequence number N of the Nth group of samples to be moved in, then when the Nth group of samples to be moved and each subsequent group of samples are moved into the filter in sequence, the NP-1th group of samples is moved out of the filter. Then the NPth group of samples is moved in sequence, and the M samples in each group of samples in the filter after each move are multiplied by the M coefficients of the corresponding Pth position after the move, and the sum of the products is used as the output of the filter for that move. After the last group of samples is moved, a total of NP filter outputs corresponding to the NPth group of samples are obtained.

[0058] S3123. Sort the P filter outputs obtained in S3121 and the NP filter outputs obtained in S3122 to obtain N outputs, and use them as downsampled data.

[0059] Specifically, the case where P≠1 is handled as follows: Figure 4 The process shown is illustrated in the example. The filter has PM coefficients, grouped into groups of M, for a total of P groups. Here, we still use M=3 as an example, and take P=3. Then, as shown... Figure 4 As shown in (1), the filter has 9 coefficients, and the coefficients are... As a group, it is positioned as the first position. As a group, it is the second position. As the third position, there are N consecutive sample points to be sampled, totaling NM sample points, ordered as the first group, second group, third group, ..., ∞th group; the following process is used during the movement:

[0060] (1) First, the first set of samples is shifted into the filter. At this time, the filter only contains the first set of samples. Therefore, the sum of the products of this set of samples and the filter coefficients corresponding to the first shifted position is calculated as the output of all shifted samples in the filter. The calculation formula is the above formula (2), as follows. Figure 4 The process is shown in step (2);

[0061] Next, the second set of samples moves into the filter, while the first set of samples continues to move within the filter, from position 1 to the next position, position 2. At this point, the second set of samples is in position 1. The output of the filter after this movement is the sum of the product of the coefficients of the first set of samples at position 2 and the product of the coefficients of the second set of samples at position 1. Figure 4 As shown in (3) above, the calculation formula is (5) above.

[0062] (3) Next, the third set of samples is moved to position 1 of the filter. The first set of samples moves from position 2 to position 3, and the second set moves from position 1 to position 2. The output of the filter corresponding to this movement is the sum of the products of the first, second, and third sets of samples moved to the filter and their corresponding filter coefficients. That is, the sum of the products of each sample in the first set with the coefficient corresponding to position 3, the products of each sample in the second set with the coefficient corresponding to position 2, and the products of each sample in the third set with the coefficient corresponding to position 1. The calculation formula is as follows:

[0063] (6), like Figure 4 As shown in (4), after the above steps (1)-(3), three outputs are obtained, namely P outputs.

[0064] During the movement of the above three sets of sample points, the number of positions or groups P (P=3) in the filter is greater than or equal to the sequence number of the sample group that was moved in. When the subsequent sample groups continue to move, the number of positions in the filter becomes less than the sequence number of the sample group that was moved in. Therefore, when the sequence number of the subsequent sample group is greater than the number of positions P in the filter, the following operation is performed: (4) After the third set of sample points is moved in, the fourth set of sample points continues to move into the filter. When the fourth set of sample points moves into the filter, since the filter only has 3 positions, the filter cannot accommodate the newly moved fourth set of sample points. Therefore, the sample points that were moved in at the beginning are moved out of the filter, i.e., as shown in the figure. Figure 4 As shown in (5), the first set of samples that were moved in first is moved out of the filter, the second set of samples is moved from the previous 2nd position to the 3rd position, the third set of samples is moved from the previous 1st position to the 2nd position, and the fourth set of samples is moved into the 1st position. Therefore, the output of the filter at this time is the sum of the products of the coefficients of each sample in the second, third, and fourth sets of samples and their corresponding positions. The calculation formula is: (7).

[0065] Each set of samples after the fourth set is moved into the filter in the same manner as step (4) above, until the last set of samples is moved into the filter. In the filter, the samples are moved sequentially from position 1 to position 2, from position 2 to position 3, until they are moved out of the filter from position 3. During the above movement process, the output of the filter is calculated each time it is moved. The calculation formula is the aforementioned formula (7). The sample corresponding to the filter coefficient in the formula is replaced. After the above steps, a total of NP outputs are calculated.

[0066] Finally, sort the P outputs and NP outputs to obtain N outputs. These N outputs are the downsampled data obtained by downsampling the data to be sampled with 3N sample points by a factor of 3.

[0067] by Figure 4 The above process described for example is the case where M=3. M can also take other values ​​as needed, and the present invention does not impose any restrictions on the selection based on actual needs.

[0068] The microcontroller (MCU) of this invention uses this calculation method. Since there is a large amount of data to be sampled, the accuracy of the final downsampled data obtained by the method of this invention is not affected, but the shifting steps and number of operations are reduced, thereby reducing the number of MCU operations and the computational load of the MCU, and thus improving the computational efficiency.

[0069] Preferably, the present invention can also perform continuous downsampling. Based on the next downsampling factor M', the downsampled data obtained from the previous downsampling M is used as the data to be downsampled, and the aforementioned steps of the present invention are used again for filtering and sampling to obtain the downsampled data of downsampling M'. The specific process is as follows: Figure 2 As shown.

[0070] like Figure 5 and 6 As shown, the embodiment of the present invention using the above-mentioned downsampling is as follows: setting the original sampling rate =2400Hz, using downsampling factor =16, then the sampling rate of the first downsampled data obtained by the method of the present invention is... =150Hz; for Continue to use downsampling factor By filtering and sampling at a sampling rate of 10, the second downsampled data is obtained. =15Hz; for Use downsampling factor =15 Continue filtering and sampling to obtain the fourth downsampled data, and obtain the sampling rate. =1Hz, therefore, the four sampling rates obtained by each downsampling are 2400Hz, 150Hz, 15Hz and 1Hz, thus enabling the target frequency band to achieve full coverage from 0.00001Hz to 320Hz, as shown in Table 1.

[0071] Table 1 Frequency range covered by different sampling rates

[0072]

[0073] Preferably, the downsampling process of the present invention can also be calculated for sample points at corresponding positions. For example, when selecting one of the sample points, it can be selected in advance, such as the first, last, or middle one of M samples. Since there is a large amount of data to be sampled, the accuracy of the final downsampling data obtained by the method of the present invention is not affected.

[0074] As an embodiment of the present invention, such as Figure 7 As shown, the present invention also discloses an ultra-low frequency magnetotelluric data sampling device for deep mineral resource exploration. The device includes: a data acquisition station 1 and an ultra-low frequency electromagnetic detection and acquisition component connected to each other; the acquisition component includes a superconducting magnetic sensor 2 for measuring in the DC~1kHz frequency band, a digital fluxgate sensor 3 for measuring in the DC~0.1Hz frequency band, and an inductive magnetic field sensor 4 for measuring in the 0.1Hz~1kHz frequency band. The superconducting magnetic sensor 2, the digital fluxgate sensor 3, and the inductive magnetic field sensor 4 are all connected to the corresponding interfaces of the data acquisition station 1.

[0075] like Figure 8 As shown, the data acquisition station 1 includes a data acquisition circuit 15 and an interface. The data acquisition circuit 15 includes a sensor data acquisition channel 151, a control unit 152, a power supply module 153, and several switches 154. The sensor data acquisition channel 151, the power supply module 153, and the several switches 154 are all connected to the control unit 152. The interfaces include a superconducting magnetic sensor interface 11, a digital fluxgate sensor interface 12, an inductive magnetic field sensor interface 13, and an electric field sensor interface 14. Correspondingly, the sensor acquisition channels include a superconducting magnetic sensor acquisition channel, a digital fluxgate sensor acquisition channel, an inductive magnetic field sensor acquisition channel, and an electric field sensor acquisition channel. The superconducting magnetic sensor interface 11 is connected to both the superconducting magnetic sensor acquisition channel and the superconducting magnetic field sensor 2, and is used to transmit the temperature and attitude information measured and acquired by the superconducting magnetic field sensor 2, as well as the information stored in its internal Flash memory, to the control unit 152 via the superconducting magnetic sensor acquisition channel. The digital fluxgate sensor interface 12 is connected to both the digital fluxgate sensor acquisition channel and the digital fluxgate sensor 3, and is used to transmit the information measured and acquired by the digital fluxgate sensor 3 to the control unit 152 via the digital fluxgate sensor acquisition channel.

[0076] The inductive magnetic field sensor interface 13 is connected to the inductive magnetic field sensor acquisition channel and the inductive magnetic field sensor 4, and is used to transmit the information measured and acquired by the inductive magnetic field sensor 4 to the control unit 152 through the inductive magnetic field sensor acquisition channel.

[0077] The electric field sensor interface 14 is connected to the electric field sensor acquisition channel and the electric field sensor 5, and is used to transmit the electric field components and temperature information measured by the electric field sensor 5 to the control unit 152 through the electric field sensor acquisition channel.

[0078] The four acquisition channels included in sensor acquisition channel 151 have the same structure and are composed of ADC, etc. The ADC converts the analog signals measured and acquired by each sensor through each interface into digital signals, and transmits the discretized data to control unit 152.

[0079] The control unit 152 consists of a microcontroller (MCU), which is responsible for logic control and data processing. The microcontroller filters and extracts the information collected by each sensor using the method described above. The specific sampling process is as described above.

[0080] Power module 153 is responsible for generating digital power supplies such as +5V, +3.3V, +1.8V, and +1.2V required by the ultra-low frequency magnetotelluric acquisition circuit, as well as analog power supplies such as +12V, ±12V, and ±2.5V required by various analog circuits and sensors.

[0081] There are three switches 154 in total. The first switch is located between the superconducting magnetic sensor interface 11 and the power supply module 153; the second switch is located between the digital fluxgate sensor interface 12 and the power supply module 153; and the third switch is located between the inductive magnetic field sensor interface 13 and the power supply module 153. The on / off state of each switch is controlled by the control unit 152. The on / off state of the switches is used to determine whether to supply power to external sensors and corresponding measurement channels, and whether to supply power to internal modules such as the ADC.

[0082] This invention employs an intelligent and efficient extraction and filtering method. By optimizing the filtering and extraction algorithm in the MCU of the ultra-low frequency geomagnetic acquisition station, it reduces the MCU's computation time and number of operations, thereby accelerating the data quality calculation and evaluation speed.

[0083] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0084] The foregoing description illustrates and describes several preferred embodiments of the present invention. However, as previously stated, it should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the inventive concept described herein through the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for sampling ultra-low frequency magnetotelluric data for deep mineral resource exploration, characterized in that, include: S1. The collected ultra-low frequency magnetotelluric data from deep mineral resource exploration is converted from analog to digital to obtain digitized magnetotelluric data. The digitized magnetotelluric data is then processed to form sampling data to be sampled. S2. Based on the required downsampling factor M, where M is a positive integer greater than or equal to 1, establish a filter and set the number of filter coefficients to be an integer multiple of M; S3. Divide all continuous sample points in the data to be downsampled into groups of M consecutive sample points. Send these groups of sample points sequentially into a filter for filtering and sampling. Specifically, move the consecutive groups of sample points sequentially into the filter and within the filter, moving each group of sample points from its current position to the next position until it leaves the filter. Each time a group of sample points moves, filtering and sampling are performed. Calculate the output of the filter after filtering and sampling, and sort all the filter outputs to obtain the downsampled data. This includes: S31. When the first set of samples is input into the filter, the output of the filter is the sum of the products of each sample in the first set of samples and the filter coefficients corresponding to the shifted-in positions; S32. The second set of samples is then input into the filter. It is determined whether the sum of the number of samples in the first and second sets is greater than the number of filter coefficients. If so, the first set of samples is removed from the filter, and the filter performs filtering and sampling on the second set of samples. At this time, the output of the filter is the sum of the products of each sample in the second set of samples and the filter coefficients corresponding to the shifted-in positions. The subsequent sets of samples after the second set of samples are input into the filter, and the above operation is performed. Finally, the outputs of all filters are sorted to obtain downsampled data. If not, proceed to the following step: calculate the output of the filter, which is the sum of the products of each sample in the first set of samples and the coefficients of the filter at the corresponding position. The subsequent sample points after the second set of samples are moved into the filter in sequence. The sample points that were moved in beforehand move in the filter in sequence. Each sample point moves from its current position to the next position until it is moved out of the filter. Each time the filter moves, it filters and samples all the sample points in it. Its output is the sum of the products of each sample point in each set of samples in the filter and the coefficients of the filter that has moved to the corresponding position. The results of all the sums are sorted to obtain the downsampled data.

2. The method according to claim 1, characterized in that, The filter is a finite impulse response (FIR) anti-spoofing low-pass filter.

3. The method according to claim 1, characterized in that, The output is calculated as the sum of the products of each sample point in all groups of samples in the filter and the filter coefficients corresponding to the shift position.

4. The method according to claim 1, characterized in that, It also includes using downsampled data as the data to be sampled, and using other downsampling factors to filter and downsample again in steps S2 and S3 to obtain other downsampled data, wherein the number of other downsampling factors is at least one.

5. An ultra-low frequency magnetotelluric data sampling device for deep mineral resource exploration, characterized in that, The device includes: a data acquisition station and an ultra-low frequency electromagnetic detection and acquisition component connected to each other; The data acquisition component includes several different sensors installed on-site; The acquisition station includes a control unit, which communicates with each sensor and performs filtering and sampling processing on the information collected by each sensor using the method described in any one of claims 1-4.

6. The apparatus according to claim 5, characterized in that, The different sensors are superconducting magnetic sensors, digital fluxgate sensors, inductive magnetic field sensors, and electric field sensors.

7. The apparatus according to claim 5, characterized in that, The data acquisition station also includes a switch, a data acquisition channel, and a power supply module.

8. The apparatus according to claim 5, characterized in that, The control unit is implemented using a microcontroller (MCU).

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