Coding and decoding technical method suitable for ELF communication

Abstract

The invention discloses a coding and decoding technical method suitable for ELF communication. The coding and decoding technical method comprises the following steps: step 1, carrying out information source coding on measurement while drilling information; step 2, carrying out channel coding on compressed information source coding data; and step 3, decoding. The coding and decoding method disclosed by the invention has the beneficial effects that (1) the signal transmission capability is enhanced; (2) the anti-interference capability is enhanced; (3) the system reliability is improved; and (4) the multi-field application of extremely low frequency communication is promoted.

Description

Technical Field

[0001] The present invention belongs to the field of electromagnetic wave communication while drilling, and in particular relates to a coding and decoding technology method suitable for ELF (extremely low frequency) communication in this field, which can be applied to deep earth communication, submarine communication, EM (electromagnetic wave) communication while drilling, resource exploration and other fields. Background Art

[0002] With the rapid development of electronic technology and the growing demand for high-precision, long-distance communications, extremely low frequency communications are constantly being optimized in design. High-sensitivity receiving antennas, combined with low-noise analog front-ends, high-performance digital processing technology, and continuously optimized modulation, demodulation, and encoding and decoding schemes, can achieve efficient signal capture, precise analysis, and reliable transmission, further promoting scientific and technological progress and development in related fields.

[0003] Extremely low frequency (ELF) signals generally refer to signals with frequencies between 3Hz and 30Hz. These signals have the following significant characteristics:

[0004] Good penetration: Ultra-low frequency signals have good penetration into metal, seawater, soil and other media, which makes them have unique application value in some specific scenarios.

[0005] Long-wave propagation: Due to the low frequency, the wavelength of the extremely low frequency signal is longer, which can form a waveguide effect between the earth's surface and the ionosphere, thereby achieving long-distance propagation.

[0006] Strong anti-interference ability: Compared with high-frequency signals, ultra-low frequency signals are less affected by electromagnetic interference, so they have better stability in some scenarios with complex electromagnetic environments.

[0007] Technologies related to extremely low frequency communications are currently mainly used in the following fields:

[0008] Submarine communications: Extremely low frequency (ELF) signals, due to their ability to penetrate seawater, are widely used for communication between submarines and surface ships or land-based command posts. This method overcomes the difficulty of high-frequency radio waves penetrating seawater, enabling reliable long-distance underwater communication.

[0009] Long-distance communications: Because ELF signals have extremely long wavelengths and very low attenuation (only 1-2 dB per 1,000 kilometers), a single transmitter has the potential to communicate worldwide. Therefore, in some special cases, ELF signal receivers are used to achieve long-distance communications across oceans.

[0010] While-drilling electromagnetic wave engineering communication: Extremely low frequency (ELF) communication while drilling uses electromagnetic waves to transmit information between the wellbore and the surface during the drilling process. This technology boasts fast transmission speeds, stable signals, and minimal impact from drilling fluids, making it widely used in drilling operations in the oil and gas industries.

[0011] As a specific communication technology, the encoding and decoding process of extremely low frequency communication may be affected by many factors, thus showing some shortcomings, mainly including low data transmission rate, susceptibility to interference, and high system complexity.

[0012] It's difficult to discuss the shortcomings of ELF communication codecs in detail, as their implementation and performance can vary depending on system design and application scenarios. However, based on the overall characteristics of ELF communication technology, we can speculate on some common issues that may exist in its codecs.

[0013] First, the extremely narrow frequency band of ELF communication technology means its data transmission rate is relatively low. This low data rate can directly impact the efficiency of codec technology, as even with efficient codec algorithms, it is difficult to significantly increase overall data transmission speeds. Furthermore, the low data rate can limit the amount and complexity of data that codec technology can handle, creating challenges in real-time transmission and large data volumes.

[0014] Secondly, ELF signals are susceptible to various interferences during propagation, including electromagnetic noise at the receiving end and ionospheric disturbances. These interference factors not only affect signal reception quality but can also adversely affect the encoding and decoding process. For example, noise can cause signal distortion or increase bit error rates, thereby increasing the difficulty and complexity of encoding and decoding. To mitigate these interferences, more complex encoding and decoding algorithms and signal processing techniques may be required, but this further increases system complexity and cost.

[0015] Furthermore, the complexity of ELF communication technology's transmitting and receiving equipment limits the flexibility and portability of its codec technology. In practical applications, it may be necessary to consider optimizing device size, weight, and power consumption while maintaining communication quality. Summary of the Invention

[0016] The technical problem to be solved by this invention is to provide a coding and decoding technology method suitable for ELF communications, focusing on receiving and processing extremely low frequency (ELF) signals in the 3Hz to 30Hz range. With its excellent penetration, long-wave propagation characteristics, and anti-interference capabilities, ELF has shown unique application value in submarine communications, geological exploration, pipeline inspection, and tunnel communications.

[0017] The present invention adopts the following technical solutions:

[0018] A coding and decoding technology method suitable for ELF communication, the improvement of which is that it includes the following steps:

[0019] Step 1: Source encode the measurement while drilling information:

[0020] Firstly, the measurement while drilling information is quantized and encoded, and then the encoded data is compressed and encoded;

[0021] Step 2: Channel encode the compressed source coded data:

[0022] Using convolutional coding combined with interleaving technology, the digital signal is converted into a carrier modulation signal of the corresponding modulation mode through channel coding and modulation;

[0023] Step 3, decoding:

[0024] First, the received data signal is synchronously processed. Then, according to the source coding and channel coding rules, the corresponding reverse decoding rules are adopted to perform demodulation decoding, deinterleaving, convolution decoding and source decoding in sequence to recover the measurement while drilling information or communication data information. Frequency error correction and phase error correction are used in the decoding process to detect and correct errors.

[0025] Furthermore, in step 1, the following A-law compression algorithm is used for data compression encoding to compress the 16-bit signed number into 12 bits:

[0026] Assume the original 16-bit number R = R1R2…R 16 , the compressed 12-bit number is C1C2…C 12 , where C1 is the sign bit, C2, C3, C4 are the segment bits, C4, C5-C 12 For each segment of data, let K1 = C2, C3, C4, K2 = C4, C5-C 12 , B is the number of bits of K2, B=8, and the decompressed data is D; the value of K1 is obtained by looking up the table, C1 takes 0 when R is positive and takes 1 when it is negative; the value of K2 is calculated by the following formula:

[0027]

[0028] The data D is decompressed by the following formula:

[0029]

[0030] Furthermore, in step 1, the following μ-law compression algorithm is used for data compression encoding:

[0031] Data Compression:

[0032] Data decompression:

[0033] In the above formula, x∈

[01] , y∈

[01] .

[0034] Furthermore, in step 2, assuming that m and c represent the information sequence and codeword sequence of the convolutional code, respectively, and G and H represent the generator matrix and check matrix of the convolutional code, respectively, the encoding process of the convolutional code is expressed as:

[0035] c=m·G

[0036] The constraint relationship between the output code of the convolutional code and H is:

[0037] c·H T =0

[0038] From the above two equations, it can be deduced that G and H are mutually orthogonal:

[0039] G.H. T =0

[0040] Convolutional code generator matrix G ∞ for:

[0041]

[0042] Check matrix H ∞ for:

[0043]

[0044] G ∞ and H ∞ By the basic generating matrix g ∞ and the basic check matrix h ∞ :

[0045] g ∞ =[g0 g1 … g m 0 …]

[0046] h ∞ =[h0 h1 … h m 0 …]

[0047] In g ∞ In the equation, g0, g1, ..., g m are all non-zero matrices of order k×n, and the remaining elements are all zero matrices. Each row element g of the sub-generator matrix i,1 (x)~g i,n (x) is called a subgenerator:

[0048]

[0049] Similarly, in h ∞ In h0, h1, ..., h m They are all non-zero matrices of order (nk)×n.

[0050] Furthermore, in step 3, the convolutional code is decoded using the Viterbi method.

[0051] Furthermore, in step 3, a block interleaver is selected. The block interleaver rule is to write the codeword sequence into m rows and n columns, and read out in the opposite order of writing, that is, reading out by column. The deinterleaving rule is to reverse the interleaving writing rule, reading the demodulated codewords into columns and then reading them out by rows. The final result is the codeword information of the real sequence; the matrix interleaving function is:

[0052] I(i)=[(i-1)modn]+[(i-1) / n]+1,i=1,2,…,N

[0053] In the above formula, N is the interleaving depth.

[0054] Furthermore, a convolutional interleaver is selected in step 3.

[0055] Furthermore, in step 3, a pseudo-random interleaver is selected to randomly interleave the code group of length N, which can produce N results. The implementation steps are as follows:

[0056] (1) Select a number i1 from the set S = {1, 2, ..., N}. The corresponding probability of selecting i1 is p(i1) = 1 / N. The selected i1 is recorded as I(1). At the same time, i1 is deleted from the set S to obtain a new set, recorded as S1.

[0057] (2) In the kth step, from the set S k-1 ={i∈S,i≠i1,i2,…,i N-k+1} randomly select an integer i k , and its corresponding selection probability is p(i k )=1 / (N-k+1), the selected i k Denoted as I(k), and i k From the set S k-1 , and get a new set, denoted as S k ;

[0058] (3) When k = N, we get I(N), and the corresponding selection probability is p(i N )=1,S N =φ;

[0059] Before applying pseudo-random interleaving, the codes are first pseudo-randomly re-arranged, the arrangement is stored, and finally read out according to the arrangement to obtain a decoded sequence.

[0060] The beneficial effects of the present invention are as follows:

[0061] (1) Enhanced signal transmission capability:

[0062] Extremely low frequency (ELF) communication has strong penetration and long transmission distances, but its signal strength is relatively weak. This invention, by optimizing codec technology, can effectively enhance the communication and transmission capabilities of signals, enabling stable signal transmission even in complex environments and reducing signal attenuation and distortion.

[0063] (2) Enhance anti-interference capability:

[0064] In extremely low frequency communications, there are many interference sources, such as electromagnetic interference, environmental noise, etc. The present invention can improve the performance and anti-interference capability of the communication system by adopting effective coding and decoding technology.

[0065] (3) Improve system reliability:

[0066] The solution of the present invention improves the reliability and stability of the system by adopting technical means such as coding and decoding, and can effectively improve the continuity of communication and the integrity of data even in strong interference environments such as lightning.

[0067] (4) Promote the application of extremely low frequency communications in multiple fields:

[0068] Extremely low frequency communication has broad application prospects in areas such as ocean exploration, underwater communications, and geological exploration. The method of the present invention will promote technological progress and application development in these fields, providing strong support for the innovative development of related industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a flowchart of the encoding and decoding technology method disclosed in the present invention;

[0070] Figure 2 It is a schematic diagram of the process using μ-rate compression;

[0071] Figure 3 It is a schematic diagram of the process of using μ rate decompression;

[0072] Figure 4 This is the schematic diagram of the convolutional code encoder;

[0073] Figure 5 It is a structural diagram of a convolutional code encoder;

[0074] Figure 6 The process block diagram of the convolutional code encoding and decoding system;

[0075] Figure 7 This is the waveform of the received signal containing sudden interference at the drilling engineering site;

[0076] Figure 8 It is a schematic diagram of the interweaving process;

[0077] Figure 9 This is a schematic diagram of the interleaver's transceiver principle;

[0078] Figure 10 It is a schematic diagram of the matrix interleaving and deinterleaving process;

[0079] Figure 11 It is a schematic diagram of the process of convolution interleaving and deinterleaving. DETAILED DESCRIPTION

[0080] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0081] The implementation of ELF communication codecs requires comprehensive consideration of signal characteristics, encoding and decoding methods, and special considerations. In practical applications, appropriate encoding and decoding methods must be selected based on specific needs and scenarios, and corresponding technical measures must be implemented to ensure communication reliability and effectiveness. The following illustrates this approach using a field application of ELF while drilling (LWD) engineering.

[0082] Example 1. This example discloses a coding and decoding technology method suitable for ELF communication. This method, based on the extremely low information transmission rate in the complex on-site electromagnetic environment of while-drilling (LWD) electromagnetic wave communication, selects a coding format suitable for on-site transmission of LWD electromagnetic wave communication. On the one hand, considering the engineering implementation perspective and the specific application scenario (i.e., it is not possible to require too many code bytes for encoding implementation, so as not to affect engineering efficiency); on the other hand, due to the extremely low information transmission rate and unidirectional communication characteristics of LWD electromagnetic wave communication, it is easier to choose a forward error correction channel coding system. Combining these two factors, a coding and decoding method suitable for the engineering characteristics of this field is designed to improve communication performance.

[0083] like Figure 1 As shown, the following steps are included:

[0084] Step 1: Source encode the measurement while drilling information:

[0085] First, measurement-while-drilling information is quantized and encoded, and then the encoded data is subjected to data compression encoding. Compression encoding exploits the statistical properties and redundancy of signals to represent them using fewer bits, thereby achieving data compression and transmission. In extremely low-frequency communications, due to limited bandwidth, compression encoding can effectively improve data transmission efficiency. This invention uses a μ-law or A-law compression algorithm.

[0086] Electromagnetic communication while drilling operates in the ELF band, with carrier frequencies typically below 20 Hz. Different modulation schemes produce different information transmission rates. Without error correction coding or other techniques, the information transmission rate is typically below 10 bit / s. This is due to variations in formation conditions and drilling techniques. Drilling speeds vary widely, from 1 m / 20 min to 1 m / 2 min. In actual operations, four to seven parameters typically need to be transmitted. Each frame of data contains approximately 40 to 64 bits, and four sets of data must be maintained per meter. For example, a drilling speed of 1 m / 2 min requires a transmission rate of approximately (4 × 64) ÷ 120 = 2.13 bps (a higher drilling speed requirement). Considering synchronization and verification, a transmission rate of 3 bps is recommended. By employing appropriate encoding methods and data compression techniques, while maintaining a 3 bps transmission rate, a lower operating frequency can be selected to minimize transmission attenuation. The operating frequency can be selected between 5 Hz and 15 Hz. On the other hand, engineering sites have certain requirements for real-time communication, making it impossible to use very long error-correcting codes. Balancing communication coding and timeliness is a serious consideration in communication system design. Advanced coding methods such as turbo codes, LDPC codes, and polar codes require relatively long information bytes to encode, resulting in low coding efficiency and inability to meet the rapid response requirements of while-drilling electromagnetic wave communication. This is why more advanced coding methods are not adopted.

[0087] The present invention adopts the following coding and decoding scheme to design EM communication while drilling, that is, the source coding and decoding adopts A-law and μ-law compression algorithms.

[0088] Source coding can compress data and remove redundancy without losing the original information, allowing for the transmission of more useful information within limited bandwidth resources, thereby reducing storage and communication channel transmission resources. Assume that the data acquisition resolution of a downhole measurement sub in a while-drilling electromagnetic wave system is 16 bits. To increase the transmission rate and increase the number of transmission items during real-time data transmission to the surface, the 16-bit data can be compressed to 12 bits or even 8 bits for upstream transmission. For example, when uploading, data is transmitted in 4-bit increments. When 16 bits are compressed to 12 bits, the number of data bits is reduced from 4 to 3, significantly improving the transmission rate. The following analysis examines the A-law and μ-law compression algorithms.

[0089] A-law compression algorithm:

[0090] The A-law compression algorithm is a form of logarithmic compression and expansion in PCM non-uniform quantization, which can effectively improve the quantization signal-to-noise ratio of source coding. It can compress 16-bit signed numbers into 12 bits:

[0091] Assume the original 16-bit number R = R1R2…R 16, the compressed 12-bit number is C1C2…C 12 , where C1 is the sign bit, C2, C3, C4 are the segment bits, C4, C5-C 12 For each segment of data, let K1 = C2, C3, C4, K2 = C4, C5-C 12 , B is the number of bits of K2, B=8, and the decompressed data is D; the value of K1 is obtained by looking up the table, C1 takes 0 when R is positive and takes 1 when it is negative; the value of K2 is calculated by the following formula:

[0092]

[0093] The data D is decompressed by the following formula:

[0094]

[0095] Here are some examples:

[0096] (1) When the original value is 2001, after compression: C1 = 0, K1 = 3, K2 = (2001-1024) / (2K1-1) = 244, B = 8, the corresponding compressed value is 0x3F4. The restored data is: D = 2000.

[0097] (2) When the original value is -16100, after compression: C1 = 1, K1 = 6, K2 = (16100-8192) / (2K1-1) = 247, B = 8, the corresponding compressed value is 0xEF7, and the recovered data is: D = -16096.

[0098] μ-law compression algorithm:

[0099] Data Compression:

[0100] Data decompression:

[0101] In the above formula, x∈

[01] , y∈

[01] .

[0102] When performing compression and decompression, the measurement data (here, the original value of the single-exponential analog-to-digital (AD) conversion) must first be normalized to the [0 1] interval before it can be performed.

[0103] Figure 2 This is a compression flow chart for compressing a signed number X (16 bits) into a signed number Y (12 bits); Figure 3 This is a decompression flow chart for decompressing a signed number Y (12 bits) into a signed number X (16 bits).

[0104] When using the above formula for compression and decompression, different μ values ​​will result in different differences between the recovered data and the original data. The specific relationship between μ values ​​and differences is shown in the following table. The compressed data range is 0x0000-0xFD00 and 0x8300-0xFFFF.

[0105] The relationship between μ value and difference (16 bits compressed to 12 bits)

[0106] μ value Difference (AD value) 255 44 127 39 63 34 31 28 15 23 10 21 7 19 2 13

[0107] Step 2: Channel encode the compressed source coded data:

[0108] Adopt convolutional coding combined with interleaving technology, after channel coding, the digital signal is converted into a carrier modulation signal of the corresponding modulation mode through modulation (such as FSK frequency modulation, QPSK phase modulation, etc.);

[0109] Convolutional codes are commonly used error-correcting codes. Unlike block error-correcting codes, the output codeword of this encoding method is related not only to the current input code group information, but also to the previous input code groups, that is, the previous and next code groups are correlated with each other.

[0110] In order to better understand the working principle of convolutional code, the convolutional code is analyzed, such as Figure 4 , as shown in 5, the input sequence is...b i-1 b i b i+1 , the input and output relationship of the convolutional code is as follows:

[0111]

[0112] Assuming that m and c represent the information sequence and codeword sequence of the convolutional code respectively, G and H represent the generator matrix and check matrix of the convolutional code respectively, the encoding process of the convolutional code is expressed as:

[0113] c=m·G

[0114] The constraint relationship between the output code of the convolutional code and H is:

[0115] c·H T =0

[0116] From the above two equations, it can be deduced that G and H are mutually orthogonal:

[0117] G.H. T =0

[0118] Convolutional code generator matrix G ∞ for:

[0119]

[0120] Check matrix H∞ for:

[0121]

[0122] The encoding form of the convolutional code is usually determined by the above two. ∞ and H ∞ By the basic generating matrix g ∞ and the basic check matrix h ∞ Sure:

[0123] g ∞ =[g0 g1 … g m 0 …]

[0124] h ∞ =[h0 h1 … h m 0 …]

[0125] In g ∞ In the equation, g0, g1, ..., g m are all non-zero matrices of order k×n, called sub-generator matrices, and the remaining elements are all zero matrices. Each row element g of the sub-generator matrix is i,1 (x)~g i,n (x) is called a subgenerator:

[0126]

[0127] Similarly, in h ∞ In h0, h1, ..., h m are all (nk)×n order non-zero matrices, called sub-check matrices. ∞ Once the matrix is ​​determined, H ∞ and G ∞ A convolutional code consists of parameters n, k, m and sub-check matrix h ∞ Each element of the vector takes the value 0 or 1.

[0128] Step 3, decoding:

[0129] First, the received data signal is synchronized at the receiving end. Due to the extremely long wavelength of the ELF signal, a precise synchronization mechanism is required to ensure correct signal reception and decoding. This synchronization process includes steps such as carrier synchronization, bit synchronization, and frame synchronization.

[0130] Then, according to the source coding and channel coding rules, the corresponding reverse decoding rules are adopted to perform demodulation decoding, deinterleaving, convolution decoding and source decoding in sequence to recover the measurement while drilling information or communication data information.

[0131] During transmission, signals may be affected by various interferences and noises, resulting in errors in the received signal. Frequency error correction and phase error correction are used during the decoding process to detect and correct these errors and ensure signal accuracy.

[0132] Convolutional code decoding can be divided into two categories: probabilistic decoding and algebraic decoding. The former mainly includes Viterbi decoding and sequence decoding. The latter is based on the algebraic rules of convolutional codes, which is similar to the syndrome deformation of block codes in error control coding. Among the above methods, Viterbi decoding is the best and is widely used in various communication projects for channel coding. Its operation rule is the maximum likelihood algorithm that optimizes the bit error rate. There are many detailed introductions to convolutional code decoding algorithms in related books and literature, so I will not explain them in detail here. Figure 6 This is a block diagram of the convolutional code encoding and decoding system process. The present invention uses the optimal Viterbi method to perform decoding analysis for while-drilling EM communications. This decoding method, applied to while-drilling EM communications, is easy to implement in software or hardware and has high computational efficiency.

[0133] The process of EM engineering communication while drilling is often accompanied by other engineering operations, such as arc welding, tripping, sliding drilling, etc. near the drilling site. These engineering operations will inevitably generate large electromagnetic noise, and its low-frequency components will cause significant interference to the EM signal while drilling, thereby affecting the quality of ground receiving communication. Figure 7 As shown in Figure 1, the impact of short-term burst interference on communication at the engineering site. The present invention uses interleaving technology to minimize this impact as much as possible. Figure 8 As shown in FIG, interleaving can reduce the average bit error rate only when combined with coding. Its essence is to disrupt the order of the original symbol data and then re-arrange them according to certain rules.

[0134] Interleaving codes are mainly used to improve communication quality under sudden interference. Interleaving is actually the process of resetting the positions of elements in the information sequence to obtain an interleaved information sequence. The reverse process is called deinterleaving. Interleaving technology has been widely used in wireless communication technology. Figure 9 This will provide a better understanding of the interleaver's transceiver process.

[0135] Packet Interleaver:

[0136] The rule of the block interleaver is to write the code element sequence into m rows and n columns, and read out in the opposite order of writing, that is, reading out by column. The rule of deinterleaving is to reverse the writing rule of interleaving, that is, reading in the demodulated code element by column and then reading out by row. The final result is the code element information of the real sequence. The process is as follows Figure 10 As shown. The matrix interleaving function is:

[0137] I(i)=[(i-1)modn]+[(i-1) / n]+1,i=1,2,…,N

[0138] In the above formula, N is the interleaving depth. Different sequence lengths have different effects in different application scenarios. For most scenarios, a shorter interleaving depth can achieve relatively better anti-burst interference performance.

[0139] Convolutional Interleaver:

[0140] The main idea of ​​convolution interleaving is to use a first-in-first-out shift register instead of RAM. Under the same interleaving depth, it occupies less resources than the packet interleaver and has a faster transmission delay. The process of convolution interleaving and deinterleaving is as follows: Figure 11 shown.

[0141] Pseudo-random interleaver:

[0142] Random interleaving of a code group of length N can produce N results. The implementation steps are as follows:

[0143] (1) Select a number i1 from the set S = {1, 2, ..., N}. The corresponding probability of selecting i1 is p(i1) = 1 / N. The selected i1 is recorded as I(1). At the same time, i1 is deleted from the set S to obtain a new set, recorded as S1.

[0144] (2) In the kth step, from the set S k-1 ={i∈S,i≠i1,i2,…,i N-k+1} randomly select an integer i k , and its corresponding selection probability is p(i k )=1 / (N-k+1), the selected i k Denoted as I(k), and i k From the set S k-1 , and get a new set, denoted as S k ;

[0145] (3) When k = N, we get I(N), and the corresponding selection probability is p(i N )=1,S N =φ;

[0146] Before applying pseudo-random interleaving, the codes are first pseudo-randomly re-arranged, the arrangement is stored, and finally read out according to the arrangement to obtain a decoded sequence.

Claims

1. A coding and decoding technology method suitable for ELF communication, characterized in that: The steps include: Step 1: Source encode the measurement while drilling information: Firstly, the measurement while drilling information is quantized and encoded, and then the encoded data is compressed and encoded; Step 2: Channel encode the compressed source coded data: The convolutional coding combined with the interleaving technology is used to convert the digital signal into a carrier modulation signal of the corresponding modulation mode after channel coding and modulation; Step 3, decoding: First, the received data signal is synchronously processed, and then according to the source coding and channel coding rules, the corresponding reverse decoding rules are adopted to perform demodulation decoding, deinterleaving, convolution decoding and source decoding in sequence to restore the measurement while drilling information or communication data information. Frequency error correction and phase error correction are used in the decoding process to detect and correct errors.

2. The coding and decoding technology method suitable for ELF communication according to claim 1, characterized in that: In step 1, the following A-law compression algorithm is used for data compression encoding to compress the 16-bit signed number into 12 bits: Assume the original 16-bit number R = R1R2…R 16 , the compressed 12 bits number is C1C2…C 12 , where C1 is the sign bit, C2, C3, C4 are the segment bits, C4, C5-C 12 For each segment of data, let K1 = C2, C3, C4, K2 = C4, C5-C 12 , B is the number of bits of K2, B=8, the decompressed data is D; the value of K1 is obtained by looking up the table, C1 takes 0 when R is positive and takes 1 when it is negative; the value of K2 is calculated by the following formula: The data D is decompressed by the following formula:

3. The coding and decoding technology method suitable for ELF communication according to claim 1 is characterized in that: In step 1, the following μ-law compression algorithm is used for data compression encoding: Data Compression: Data decompression: In the above formula, x∈[01], y∈[01].

4. The coding and decoding technology method suitable for ELF communication according to claim 1 is characterized in that: In step 2, assuming that m and c represent the information sequence and codeword sequence of the convolutional code, respectively, and G and H represent the generator matrix and check matrix of the convolutional code, respectively, the encoding process of the convolutional code is expressed as: c=m·G The constraint relationship between the output code of the convolutional code and H is: c·H T =0 From the above two equations, it can be deduced that G and H are mutually orthogonal: G·H T =0 Convolutional code generator matrix G ∞ for: Check Matrix H ∞ for: G ∞ and H ∞ By the basic generating matrix g ∞ and the basic check matrix h ∞ : g ∞ =[g0 g1…g m 0] h ∞ =[h0 h1…h m 0…] In g ∞ In the equation, g0, g1, …, g m are all non-zero matrices of order k×n, and the remaining elements are all zero matrices. Each row element g of the sub-generator matrix i,1 (x)~g i,n (x) is called a subgenerator: Similarly, in h ∞ In, h0, h1, …, h m They are all non-zero matrices of order (nk)×n.

5. The coding and decoding technology method suitable for ELF communication according to claim 1 is characterized in that: In step 3, the convolutional code is decoded using the Viterbi method.

6. The coding and decoding technology method suitable for ELF communication according to claim 1 is characterized in that: In step 3, a group interleaver is selected. The rule of the group interleaver is to write the codeword sequence in m rows and n columns, and the order of reading is opposite to the writing order, that is, reading out by column. The rule of deinterleaving is to reverse the writing rule of interleaving, that is, reading in the demodulation codeword by column, and then reading out by row. The final result is the codeword information of the real sequence; the function of matrix interleaving is: I(i)=[(i-1)modn]+[(i-1) / n]+1,i=1,2,…,N In the above formula, N is the interleaving depth.

7. The coding and decoding technology method suitable for ELF communication according to claim 1 is characterized in that: In step 3, a convolutional interleaver is selected.

8. The coding and decoding technology method suitable for ELF communication according to claim 1 is characterized in that: In step 3, a pseudo-random interleaver is selected to randomly interleave a code group of length N, which can produce N results. The implementation steps are as follows: (1) Select a number i1 from the set S = {1, 2, …, N}. The corresponding probability of selecting i1 is p(i1) = 1 / N. The selected i1 is denoted as I(1). At the same time, i1 is deleted from the set S to obtain a new set, denoted as S1. (2) In the kth step, from the set S k-1 ={i∈S,i≠i1,i2,…,i N-k+1 } randomly select an integer i k , and its corresponding selection probability is p(i k )=1 / (N-k+1), the selected i k Denoted as I(k), and i k From the set S k-1 , and obtain a new set, denoted as S k ; (3) When k = N, we get I(N), and the corresponding selection probability is p(i N )=1,S N =φ; Before applying pseudo-random interleaving, the codes are first subjected to pseudo-random reordering, and their arrangement is stored, and finally read out according to the arrangement to obtain a decoded sequence.

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