WO2025130321A1 - 叠前地震数据分频压缩方法、装置、设备及存储介质 - Google Patents

叠前地震数据分频压缩方法、装置、设备及存储介质 Download PDF

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WO2025130321A1
WO2025130321A1 PCT/CN2024/126405 CN2024126405W WO2025130321A1 WO 2025130321 A1 WO2025130321 A1 WO 2025130321A1 CN 2024126405 W CN2024126405 W CN 2024126405W WO 2025130321 A1 WO2025130321 A1 WO 2025130321A1
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frequency
data
level
coding
transform coefficient
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French (fr)
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闫海洋
徐朝红
孙赞东
刘昭
朱兴卉
倪琳
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China National Petroleum Corp
BGP Inc
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China National Petroleum Corp
BGP Inc
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/32—Transforming one recording into another or one representation into another
    • G01V1/325—Transforming one representation into another
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/32—Transforming one recording into another or one representation into another
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
    • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/41—Bandwidth or redundancy reduction

Definitions

  • the present invention relates to the field of oil and gas exploration, to seismic data acquisition and signal processing technology, and in particular to a pre-stack seismic data frequency division compression method, a pre-stack seismic data frequency division compression device, a computer device and a computer-readable storage medium.
  • seismic exploration instruments are also moving towards multi-dimensional, multi-component, multi-parameter and high resolution.
  • the amount of seismic exploration data has grown exponentially. Faced with the current situation of real-time recovery of massive data and channel bandwidth transmission constraints, how to improve the real-time transmission capability of massive data in the seismic exploration system is an important bottleneck problem restricting production efficiency.
  • Seismic data compression and reconstruction technology provides an effective way to solve this problem.
  • Seismic data compression is a key technology to solve the transmission and storage of massive seismic data.
  • Data compression can reduce the amount of data from the source of transmission, improve the real-time processing speed of data and save storage space.
  • Seismic exploration data compression methods can be divided into two categories: lossless compression and lossy compression according to whether there is information loss or not.
  • Lossless compression can ensure that the decompressed data is exactly the same as the original data. It is particularly suitable for compressing seismic data that needs further processing after decompression, but its compression efficiency is low, and the compression factor is generally less than 2.
  • Lossy compression can achieve high compression while allowing a small amount of information loss. The compression factor is higher, but there are problems such as information loss.
  • the purpose of the embodiment of the present invention is to provide a method, device, equipment and storage medium for frequency division compression of pre-stack seismic data.
  • the method distinguishes the high-frequency part and the low-frequency part in the pre-stack seismic data through Hartley transform, and then performs compression encoding of different levels on the low-frequency part and the high-frequency part respectively.
  • the low-frequency part that concentrates the main energy of the seismic data is encoded with a high level of number to ensure the signal-to-noise ratio of the compressed data, and the high-frequency part is encoded with a low level of number to improve the compression ratio of the data.
  • the present invention provides a method for frequency division compression of pre-stack seismic data in a first aspect, the method comprising:
  • the low-frequency data includes at least one low-frequency transformation coefficient group
  • the high-frequency data includes at least one high-frequency transformation coefficient group
  • the compression coding level of the low-frequency transform coefficient group is greater than the compression coding level of the high-frequency transform coefficient group, and the compressed data includes target coding symbol groups at each level and auxiliary scanning data at each level;
  • the compression coding includes:
  • the transformation coefficient group is scanned step by step to obtain initial coding symbol groups of each level, each level of coding in the step-by-step coding corresponds to a threshold value, and the initial coding symbol group includes important coding symbols and secondary coding symbols; the secondary coding symbols after the last important coding symbol in the initial coding symbol groups of each level are deleted to obtain target coding symbol groups of each level;
  • each important transform coefficient in the transform coefficient group is quantized level by level according to the threshold value corresponding to each level of coding, and auxiliary scanning data of each level is output.
  • the high-frequency part and the low-frequency part in the pre-stack seismic data are distinguished by Hartley transform, and then the low-frequency part and the high-frequency part are compressed and encoded at different levels respectively.
  • the low-frequency part that concentrates the main energy of the seismic data is encoded at a high level to ensure the signal-to-noise ratio of the compressed data
  • the high-frequency part is encoded at a low level to improve the compression ratio of the data.
  • the secondary coding symbols after the last important coding symbol are deleted. While ensuring the integrity of the data, the amount of data after compression coding is further reduced, saving data transmission resources.
  • the transformed seismic data is divided into low-frequency data and high-frequency data, including:
  • the transformed seismic data are divided into low-frequency data and high-frequency data according to the acquisition parameters and data quality of the seismic data.
  • the transformed seismic data can be more accurately divided into low-frequency data and high-frequency data.
  • the data quality includes the Nyquist frequency
  • the sampling parameters include: a frequency sampling interval
  • the transformed seismic data is divided into low-frequency data and high-frequency data, including:
  • the frequency range of low-frequency data and the frequency range of high-frequency data are determined by calculation according to the Nyquist frequency and the frequency sampling interval;
  • the transformed seismic data are divided into low-frequency data and high-frequency data according to the determined frequency range.
  • high-frequency data and low-frequency data are divided according to data acquisition parameters.
  • the division method is more consistent with the actual data and is more conducive to ensuring the integrity of the data after subsequent compression.
  • the frequency range of the low-frequency data satisfies the following relationship:
  • the frequency range of the high-frequency data satisfies the following relationship:
  • f1 is the low frequency
  • f2 is the high frequency
  • fmax is the Nyquist frequency
  • df is the frequency sampling interval.
  • the transformed data can be accurately divided into a high-frequency part and a low-frequency part.
  • the transformation coefficient group is coded and scanned step by step to obtain initial coding symbol groups at each level, including:
  • the transformation coefficient group is converted into an initial coding symbol group.
  • each important transform coefficient in the transform coefficient group is quantized level by level according to the threshold value corresponding to each level of coding, and auxiliary scanning data of each level is output, including:
  • the current level quantization result of each important transform coefficient is determined according to the quantization interval corresponding to the current level encoding, including:
  • the quantization result is 0; if the important transform coefficient belongs to the next sub-interval of the quantization interval corresponding to the encoding, the quantization result is 1.
  • the method further includes:
  • the decoded data are combined and then subjected to inverse Hartley transform to obtain reconstructed seismic data.
  • the compressed and encoded data is restored to obtain reconstructed seismic data.
  • a second aspect of the present application provides a pre-stack seismic data frequency division and compression device, the device comprising:
  • a data acquisition unit used for acquiring pre-stack seismic data
  • a data transformation unit used for performing Hartley transformation on pre-stack seismic data to obtain transformed seismic data
  • a data division unit used to divide the transformed seismic data into low-frequency data and high-frequency data;
  • the low-frequency data includes at least one low-frequency transformation coefficient group, and the high-frequency data includes at least one high-frequency transformation coefficient group;
  • a data compression unit used for performing compression coding of different levels on the low-frequency transform coefficient group and the high-frequency transform coefficient group respectively to obtain compressed data, wherein the compression coding level of the low-frequency transform coefficient group is greater than the compression coding level of the high-frequency transform coefficient group, and the compressed data includes target coding symbol groups of each level and auxiliary scanning data of each level;
  • the data compression unit comprises:
  • a main scanning module is used to perform a level-by-level coding scan on the transformation coefficient group to obtain initial coding symbol groups at each level, wherein each level of coding in the level-by-level coding corresponds to a threshold value, and the initial coding symbol group includes important coding symbols and secondary coding symbols; and to delete the secondary coding symbols after the last important coding symbol in the initial coding symbol groups at each level to obtain target coding symbol groups at each level;
  • a determination module configured to determine important transform coefficients in the transform coefficient group according to important coding symbols in the primary coding symbol group
  • the auxiliary scanning module is used to quantize each important transform coefficient in the transform coefficient group step by step according to the threshold value corresponding to each level of coding, and output auxiliary scanning data of each level.
  • the high-frequency part and the low-frequency part in the pre-stack seismic data are distinguished by Hartley transform, and then the low-frequency part and the high-frequency part are compressed and encoded at different levels respectively.
  • the low-frequency part that concentrates the main energy of the seismic data is encoded at a high level to ensure the signal-to-noise ratio of the compressed data
  • the high-frequency part is encoded at a low level to improve the compression ratio of the data.
  • the secondary coding symbols after the last important coding symbol are deleted. While ensuring the integrity of the data, the amount of data after compression coding is further reduced, saving data transmission resources.
  • a third aspect of the present application provides a computer device, which includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the pre-stack seismic data frequency division compression method.
  • a fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is loaded and executed by a processor to implement the pre-stack seismic data frequency division and compression method.
  • the fifth aspect of the present application provides a computer program product, which includes a computer program stored in a computer-readable storage medium.
  • a processor reads and executes the computer program from the computer-readable storage medium to implement the pre-stack seismic data frequency division and compression method.
  • the encoding level of low-frequency data is high, which ensures the signal-to-noise ratio of compressed data, and the encoding level of high-frequency data is low, which improves the data compression ratio.
  • This method can better ensure a high signal-to-noise ratio and compression ratio at the same time, and is more in line with the requirements of high-precision compression of seismic data.
  • FIG1 is a schematic diagram of a main scan code provided by an EZW coding method
  • FIG2 is a schematic diagram of an 8 ⁇ 8 wavelet coefficient group provided by the EZW encoding method
  • FIG3 is a schematic diagram of the first level main scan code provided by the EZW coding method
  • FIG4 is a schematic diagram of a second level main scan code provided by the EZW coding method
  • FIG5 is a flow chart of a method for frequency division and compression of pre-stack seismic data provided by an embodiment of the present application
  • FIG6 is a schematic diagram of pre-stack seismic data acquired by a single shot of a Sigsbee 2A model provided in one embodiment of the present application;
  • FIG7 is a schematic diagram of a Hartley transformation result provided by an embodiment of the present application.
  • FIG8a is a partial schematic diagram of low-frequency data provided by an embodiment of the present application.
  • FIG8b is a partial schematic diagram of high-frequency data provided by an embodiment of the present application.
  • FIG9 is a schematic diagram showing a comparison of signal-to-noise ratios of decompressed data at different high-frequency coding levels when the low-frequency coding level is 8, provided in one embodiment of the present application;
  • FIG10 is a schematic diagram showing a comparison of compression ratios of decompressed data at different high-frequency coding levels when the number of low-frequency coding levels is 8, provided in one embodiment of the present application;
  • FIG12 is a schematic diagram showing a comparison of compression ratios of decompressed data at different high-frequency coding levels when the number of low-frequency coding levels is 10, provided in one embodiment of the present application;
  • FIG. 13 is a block diagram of a pre-stack seismic data frequency division and compression device provided in one embodiment of the present application.
  • EZW is an image coding algorithm. Its core is to perform step-by-step coding and quantization in the wavelet domain. The main scan determines the sign and spatial position of important coefficients through step-by-step coding; the auxiliary scan only performs fine quantization on important coefficients in the main scan.
  • EZW uses step-by-step quantization coding, which applies a series of thresholds T 1 , T 2 , ..., TN in turn to determine the importance of wavelet coefficients:
  • c is the wavelet coefficient
  • N is the maximum coding level
  • Ti is the threshold corresponding to the i-th coding.
  • the main scan is used to determine the sign and spatial position of important coefficients, perform coarse quantization through level-by-level scanning, and improve coding efficiency through the zero tree structure.
  • Figure 1 shows a schematic diagram of the main scan coding provided by the EZW algorithm.
  • the zero tree characteristics of the wavelet coefficients are used to mark the wavelet coefficients during the i-th level scan.
  • the absolute value of the wavelet coefficient is greater than Ti , it is an important coefficient for the i-th level scan and is encoded as a symbol P (positive important coefficient) or a symbol N (negative important coefficient); otherwise, it is an unimportant coefficient. If the important coefficient is scanned out at the i-th level, it will not be scanned and encoded at the next level.
  • the wavelet coefficient is an unimportant coefficient with respect to the threshold Ti , and its descendants are all less than the threshold Ti , then it is a zero tree root T, and the descendants of the zero tree root do not need to be encoded; but if there is a coefficient greater than the set threshold Ti among the descendants of the coefficient, it is an isolated zero value Z.
  • the quantizer interval is [dT i , dT i +T i ), where 1 ⁇ d ⁇ Q, which is equally divided into two quantization subintervals [dT i , dT i +0.5T i ) and [dT i +0.5T i , dT i +T i ). If the wavelet coefficient belongs to the previous subinterval, the output quantization symbol "0", otherwise the output quantization symbol is "1". The output symbols "0" and "1" are recorded by an auxiliary scanning table. After determining the number of quantizers and the quantization interval, it is necessary to determine the quantization values corresponding to "0" and "1" in each quantizer. In the qth quantizer, the reconstructed value of the symbol "0" is, please refer to formula 3:
  • the important coefficients of the previous level coding will continue to be quantized in the next level coding, and the important coefficients of the next level coding will not be quantized in the previous level, thereby reducing the number of symbols of the quantized output. Due to the sparsity of wavelet coefficients, large important coefficients are finely quantized (quantized more times), and small important coefficients are quantized less times and occupy fewer bits.
  • FIG. 2 shows a schematic diagram of an 8 ⁇ 8 wavelet coefficient group provided by an embodiment.
  • Table 1 shows the symbols output by the main scanning and auxiliary scanning of the wavelet coefficients shown in Figure 2 using the EZW algorithm.
  • the wavelet coefficients in Figure 2 are scanned with a threshold of 32. If the absolute value of the wavelet coefficient is greater than or equal to 32, the wavelet coefficient is encoded as a symbol P or a symbol N, indicating that it is an important wavelet coefficient, wherein if the wavelet coefficient is a positive number, it is encoded as P, and if the wavelet coefficient is a negative number, it is encoded as N. If the absolute value of the wavelet coefficient is less than 32, it is encoded or not encoded by judging whether it is a descendant coefficient of the zero tree.
  • the wavelet coefficient -31 (21) is not a descendant coefficient of the zero tree and there are important coefficients in the descendant coefficients of the wavelet coefficient, so it is encoded as Z, representing that it is an isolated zero value.
  • the wavelet coefficient 23 (22) is not a descendant coefficient of the zero tree and there are no important coefficients in the descendant coefficients of the wavelet coefficient, so it is encoded as T, representing that it is a zero tree root.
  • FIG3 shows a schematic diagram of the first-level main scan coding provided by an embodiment of the EZW algorithm.
  • P in P1 represents that the coding symbol of 63 in FIG2 is encoded as P
  • the subscript 1 represents that the coding symbol corresponds to the first symbol of the first-level main scan output sequence in Table 1, and when the output symbol of a wavelet coefficient is T, all its descendant coefficients are no longer scanned and are represented by " ⁇ ".
  • the important wavelet coefficients 63, -34, 49 and 47 in the first level main scan are quantized.
  • the process of determining the quantization sign of its auxiliary scan output is as follows:
  • the wavelet coefficient 63 is in the next subinterval [48, 64) of the interval [32, 64), so the quantization symbol of the auxiliary scan is 1 (corresponding to the first character "1" output by the first level auxiliary scan); illustratively, the absolute value of the wavelet coefficient -34 is in the previous subinterval [32, 48) of the interval [32, 64), so the auxiliary scan coding character is 0 (corresponding to the second character "0" output by the first level auxiliary scan).
  • the coded characters of the second-level main scan and auxiliary scan can be determined.
  • the wavelet coefficients in FIG. 2 are scanned with a threshold of 16.
  • FIG. 4 shows a schematic diagram of the second-level main scan coding provided by an embodiment of the present application. Since the wavelet coefficients 63, -34, 49 and 47 in FIG. 2 have been encoded in the first-level main scan, Mark, indicating that it is not encoded in the second level main scan.
  • the wavelet coefficients -31 (21) and 23 (22) are encoded as symbol N and symbol P respectively.
  • the important wavelet coefficients 63, -34, 49 and 47 in the first level main scan and the important wavelet coefficients -31 and 23 in the second level main scan are quantized.
  • the process of determining the symbol of its auxiliary scan output is as follows:
  • the first quantizer interval is [16, 32)
  • the second quantizer interval is [32, 48)
  • the third quantizer interval is [48, 64).
  • the wavelet coefficient 63 is in the next subinterval [56, 64) of the third quantizer interval [48, 64), so the auxiliary scan coding character is 1 (corresponding to the first character "1" of the second level auxiliary scan output); illustratively, the absolute value of the wavelet coefficient -34 is in the previous subinterval [32, 40) of the second quantizer interval [32, 48), so the auxiliary scan coding character is 0 (corresponding to the second character "0" of the first level auxiliary scan output).
  • the coding symbols of each level of main scanning and auxiliary scanning can be determined, as shown in Table 1 below.
  • the wavelet coefficients are encoded step by step by the above method, and the secondary coding symbols isolated zero values (symbol Z) and zero tree roots (symbol T) encoded in the previous level will also be re-scanned and encoded in the next level. Please refer to 31 and 32 in Figure 3 and the corresponding 41 and 42 in Figure 4.
  • the wavelet coefficients are encoded in the first level main scan and in the second level main scan at the same time, which will lead to an increase in the number of encoded isolated zero values and zero tree roots, reduce the data compression ratio, and require more storage space.
  • the present application proposes a pre-stack seismic data frequency division compression method, which first performs Hartley domain transformation on the seismic data, and then divides the low-frequency part and the high-frequency part according to the acquisition parameters and data quality of the seismic data itself; the low-frequency data has a high coding level to ensure the signal-to-noise ratio of the compressed data, and the high-frequency data has a low coding level to improve the data compression ratio.
  • the improved EZW algorithm is adopted, and after each level of main scanning is completed, the secondary coding symbols after the last important coding symbol in the initial coding symbol group are deleted to obtain the target coding symbol group, so as to further reduce the amount of data, save storage space, and reduce the occupation of the data transmission channel.
  • Fig. 5 is a flow chart of a method for frequency division compression of pre-stack seismic data provided by an embodiment of the present invention.
  • the execution subject of each step of the method can be a computer device.
  • the method includes the following steps S1-S4.
  • pre-stack seismic data refers to seismic data that has been collected and processed but has not been subjected to seismic offset correction and stacking. It contains all the information of the seismic wave signal recorded from the seismic instrument, including the reflection and refraction characteristics of the underground medium.
  • Pre-stack seismic data can be used for seismic imaging and underground structural interpretation, but due to the lack of offset correction, underground interfaces at different locations may be misaligned.
  • Figure 6 the figure shows pre-stack seismic data collected by a single shot of a Sigsbee 2A model.
  • S3 Divide the transformed seismic data into low-frequency data and high-frequency data; the low-frequency data includes at least one low-frequency transformation coefficient group, and the high-frequency data includes at least one high-frequency transformation coefficient group.
  • the transformed seismic data is divided into low-frequency data and high-frequency data, including:
  • the transformed seismic data are divided into low-frequency data and high-frequency data according to the acquisition parameters and data quality of the seismic data.
  • the transformed seismic data can be more accurately divided into low-frequency data and high-frequency data.
  • the data quality includes the Nyquist frequency
  • the sampling parameters include: a frequency sampling interval
  • the frequency range of the low-frequency data and the frequency range of the high-frequency data are calculated and determined according to the Nyquist frequency and the frequency sampling interval.
  • the frequency range of the low-frequency data satisfies the following relationship:
  • the frequency range of the high-frequency data satisfies the following relationship:
  • f1 is the low frequency
  • f2 is the high frequency
  • fmax is the Nyquist frequency
  • df is the frequency sampling interval.
  • the transformed seismic data is divided into low-frequency data and high-frequency data according to the determined frequency range.
  • the results after frequency division are shown in Figures 8a and 8b, where Figure 8a is the low-frequency data part and Figure 8b is the high-frequency data part.
  • high-frequency data and low-frequency data are divided according to data acquisition parameters.
  • the division method is more consistent with the actual data and is more conducive to ensuring the integrity of the data after subsequent compression.
  • S4 respectively perform compression coding of different levels on the low-frequency transform coefficient group and the high-frequency transform coefficient group to obtain compressed data, wherein the compression coding level of the low-frequency transform coefficient group is greater than the compression coding level of the high-frequency transform coefficient group, and the compressed data includes target coding symbol groups at various levels and auxiliary scanning data at various levels.
  • the low-frequency part that concentrates the main energy of the seismic data is encoded with a high level to ensure the signal-to-noise ratio of the compressed data
  • the high-frequency part is encoded with a low level to improve the compression ratio of the data.
  • the compression coding includes:
  • the transformation coefficient group is scanned step by step to obtain initial coding symbol groups at each level.
  • Each level of coding in the step-by-step coding corresponds to a threshold value, and the initial coding symbol group includes important coding symbols and secondary coding symbols.
  • the secondary coding symbols after the last important coding symbol in the initial coding symbol group at each level are deleted to obtain target coding symbol groups at each level.
  • the step-by-step encoding process in the main scanning stage is the same as the EZW encoding process described above.
  • the first threshold corresponding to the current level encoding scan is determined according to the iterative threshold function and the transform coefficient group. As described above, the threshold corresponding to each level of encoding can be obtained by iterating the threshold function. Among them, the threshold corresponding to the first level encoding is 32, the threshold corresponding to the second level encoding is 16, the threshold corresponding to the third level encoding is 8, and the threshold corresponding to the fourth level encoding is 4.
  • the transform coefficient is encoded as a symbol P or a symbol N; wherein the symbol P corresponds to an important transform coefficient with a positive value, and the symbol N corresponds to an important transform coefficient with a negative value, and the symbol P and the symbol N are important coding symbols;
  • the transform coefficient is encoded as symbol T or symbol Z or not encoded; wherein the transform coefficient corresponding to symbol T is a zero tree root, the transform coefficient corresponding to symbol Z is an isolated zero value, and the symbol T and the symbol Z are secondary coding symbols;
  • the initial coding symbol group of the current level is obtained.
  • the transformation coefficient group is converted into the initial coding symbol group.
  • the secondary coding symbols after the last important coding symbol in the initial coding symbol group of each level are deleted to obtain the target coding symbol group of each level.
  • Table 2 shows that the target coding symbol group finally obtained by performing the main scanning on the wavelet coefficients in Figure 2 is shown in Table 2.
  • the important transform coefficients in the transform coefficient group are determined according to the important coding symbols in the primary coding symbol group, that is, the transform coefficients corresponding to the coding symbols P and N in the main scanning stage. Taking the wavelet coefficients in Figure 2 as an example, if it is the first level scan, then the first threshold is 32, and the first symbol P of the first level main scan in Table 2 corresponds to 63 in Figure 2.
  • each important transform coefficient in the transform coefficient group is quantized level by level according to the threshold value corresponding to each level of coding, and auxiliary scanning data of each level is output, including:
  • the quantization interval corresponding to the current level coding is determined, and the quantization interval is divided into 2 sub-intervals.
  • the total quantization interval being [T i , 2T 1 ), and the number of quantizers and the quantizer interval corresponding to each level coding. It can be obtained that, exemplarily, the number of quantizers of the first level coding is 1, and the quantization interval of the quantizer is [32, 64), which is equally divided into 2 sub-intervals: [32, 48) and [48, 64).
  • the number of quantizers for the second level encoding is 3, the first quantizer interval is [16, 32), which is divided into two sub-intervals: [16, 24) and [24, 32); the second quantizer interval is [32, 48), which is divided into two sub-intervals: [32, 40) and [40, 48); the third quantizer interval is [48, 64), which is divided into two sub-intervals: [48, 56) and [56, 64).
  • the current level quantization result of each important transform coefficient is determined: if the important transform coefficient belongs to the previous sub-interval of the quantization interval corresponding to the code, the quantization result is 0, and if the important transform coefficient belongs to the next sub-interval of the quantization interval corresponding to the code, the quantization result is 1.
  • the wavelet coefficient 63 is in the next sub-interval [48, 64) of the interval [32, 64) during the first level auxiliary scanning process, so the quantization symbol of the first level auxiliary scanning is 1.
  • the second level auxiliary scanning process it is in the next sub-interval [56, 64) of the third quantizer interval [48, 64), so the second level auxiliary scanning code character is 1.
  • the current level auxiliary scanning data is output.
  • the high-frequency part and the low-frequency part in the pre-stack seismic data are distinguished by Hartley transform, and then the low-frequency part and the high-frequency part are compressed and encoded at different levels respectively.
  • the secondary coding symbols after the last important coding symbol are deleted. While ensuring the integrity of the data, the amount of data after compression encoding is further reduced, saving data transmission resources.
  • the compression coding level of the low-frequency transform coefficient group is greater than the compression coding level of the high-frequency transform coefficient group.
  • FIG. 9 shows the comparison of the signal-to-noise ratio of the decompressed data of different high-frequency transform coefficient group coding levels when the low-frequency transform coefficient group coding level is 8
  • FIG. 11 shows the comparison of the signal-to-noise ratio of the decompressed data of different high-frequency transform coefficient group coding levels when the low-frequency transform coefficient group coding level is 10. It can be seen from FIG. 9 and FIG.
  • FIG. 10 shows the comparison of the compression ratio of the decompressed data of different high-frequency transform coefficient group coding levels when the low-frequency transform coefficient group coding level is 8
  • FIG. 12 shows the comparison of the compression ratio of the decompressed data of different high-frequency transform coefficient group coding levels when the low-frequency transform coefficient group coding level is 10. It can be seen from FIG. 10 and FIG. 12 that the higher the coding level of the high-frequency transform coefficient group, the lower the compression ratio of the decompressed data.
  • the coding levels of the low-frequency transform coefficient group are fixed to 8 and 10, and experiments with different coding levels of the high-frequency transform coefficient group are conducted.
  • the test results are shown in Table 3.
  • the test results are shown in Table 4.
  • the compressed data has a signal-to-noise ratio of 45.45 and a compression ratio of 5.29.
  • the signal-to-noise ratio and compression ratio of Hartley domain frequency division EZW compression are both improved compared to conventional EZW compression.
  • the method further includes:
  • the compressed data is decoded in the corresponding level to obtain the decoded data.
  • the quantizer corresponding to each level of coding is determined according to the iterative threshold function, and the quantizer is used to indicate the number of quantization intervals and the range of quantization intervals corresponding to each level of coding;
  • the reconstruction function is determined according to the quantizer and the threshold corresponding to each level of coding;
  • the reconstruction value of each important transform coefficient is determined according to the reconstruction function, the coding symbol group, the auxiliary scanning data and the transform coefficient group.
  • the wavelet coefficients in FIG. 2 are reconstructed.
  • the coding symbol output by the first-level main scan is P
  • the decoded data are combined and then subjected to inverse Hartley transform to obtain reconstructed seismic data.
  • the compressed and encoded data is restored to obtain reconstructed seismic data.
  • the second aspect of the present application provides a pre-stack seismic data frequency division compression device, as shown in FIG13 , the device comprises:
  • a data acquisition unit used to acquire pre-stack seismic data
  • a data transformation unit used for performing Hartley transformation on pre-stack seismic data to obtain transformed seismic data
  • a data division unit used to divide the transformed seismic data into low-frequency data and high-frequency data;
  • the low-frequency data includes at least one low-frequency transformation coefficient group, and the high-frequency data includes at least one high-frequency transformation coefficient group;
  • a data compression unit used for performing compression coding of different levels on the low-frequency transform coefficient group and the high-frequency transform coefficient group respectively to obtain compressed data, wherein the compression coding level of the low-frequency transform coefficient group is greater than the compression coding level of the high-frequency transform coefficient group, and the compressed data includes target coding symbol groups of each level and auxiliary scanning data of each level;
  • the data compression unit comprises:
  • a main scanning module is used to perform a level-by-level coding scan on the transformation coefficient group to obtain initial coding symbol groups at each level, wherein each level of coding in the level-by-level coding corresponds to a threshold value, and the initial coding symbol group includes important coding symbols and secondary coding symbols; and to delete the secondary coding symbols after the last important coding symbol in the initial coding symbol groups at each level to obtain target coding symbol groups at each level;
  • a determination module configured to determine important transform coefficients in the transform coefficient group according to important coding symbols in the primary coding symbol group
  • the auxiliary scanning module is used to quantize each important transform coefficient in the transform coefficient group step by step according to the threshold value corresponding to each level of encoding, and output auxiliary scanning data at each level.
  • the auxiliary scanning data includes the quantization level and quantization result of each important transform coefficient.
  • the quantization level refers to the number of times the important transform coefficient is quantized.
  • the high-frequency part and the low-frequency part in the pre-stack seismic data are distinguished by Hartley transform, and then the low-frequency part and the high-frequency part are compressed and encoded with different levels respectively.
  • the low-frequency part that concentrates the main energy of the seismic data is encoded with a high level to ensure the signal-to-noise ratio of the compressed data
  • the high-frequency part is encoded with a low level to improve the compression ratio of the data.
  • the secondary encoding symbols after the last important encoding symbol are deleted, which further reduces the amount of data after compression encoding and saves data transmission resources while ensuring data integrity.
  • a third aspect of the present application provides a computer device, which includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the pre-stack seismic data frequency division compression method.
  • a fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is loaded and executed by a processor to implement the pre-stack seismic data frequency division and compression method.
  • the fifth aspect of the present application provides a computer program product, which includes a computer program stored in a computer-readable storage medium.
  • a processor reads and executes the computer program from the computer-readable storage medium to implement the pre-stack seismic data frequency division and compression method.
  • the aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes.

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Abstract

一种叠前地震数据分频压缩方法，包括：获取叠前地震数据（S1）；对叠前地震数据进行哈特莱变换，得到变换后的地震数据（S2）；将变换后的地震数据划分为低频数据和高频数据（S3）；低频数据包括至少一个低频变换系数组，高频数据包括至少一个高频变换系数组；分别对低频变换系数组和高频变换系数组进行不同级数的压缩编码，得到压缩后的数据（S4），低频变换系数组的压缩编码级数大于高频变换系数组的压缩编码级数。还提供一种叠前地震数据分频压缩装置。对集中了地震数据主要能量的低频部分进行高级数编码以保证信噪比，对高频部分进行低级数编码以提高压缩比。

Description

叠前地震数据分频压缩方法、装置、设备及存储介质 技术领域
本发明涉及油气勘探领域，涉及地震资料采集、信号处理技术，具体地涉及一种叠前地震数据分频压缩方法、一种叠前地震数据分频压缩装置、一种计算机设备及一种计算机可读存储介质。
背景技术
随着石油地球物理勘探技术的迅猛发展，以及对勘探精度要求越来越高，地震勘探仪器也向着多维、多分量、多参数和高分辨率迈进，地震勘探数据量随之呈指数增长，面对海量数据实时回收、信道带宽传输制约的现状，如何提高地震勘探系统海量数据实时传输能力是制约生产效率的一个重要瓶颈问题。
地震数据的压缩与重构技术为解决该问题提供了有效途径。地震数据压缩是解决海量地震数据传输及存储的一项关键技术。数据压缩可以从传输的源头减少数据量，提高数据的实时处理速度和节省存储空间。
地震勘探数据压缩方法按照有无信息丢失可分为无损压缩和有损压缩两大类：无损压缩能保证解压数据与原始数据完全一样，特别适合于对解压后还需要做进一步处理的地震数据进行压缩，但其压缩效率低，一般压缩倍数小于2；有损压缩在允许有少量信息损失的情况下可以获得高倍压缩，压缩倍数较高，但存在信息丢失等问题。
如何在保障信息完整性的情况下，尽可能的压缩数据，仍然是需要解决的问题。
发明内容
本发明实施方式的目的是提供一种叠前地震数据分频压缩方法、装置、设备及存储介质，该方法通过哈特莱变换将叠前地震数据中的高频部分和低频部分区分开，然后分别对低频部分和高频部分进行不同级数的压缩编码，将集中了地震数据主要能量的低频部分进行高级数编码，保证压缩数据的信噪比，高频部分进行低级数编码，提高数据的压缩比。
为了实现上述目的，本发明第一方面提供一种叠前地震数据分频压缩方法，所述方法包括：
获取叠前地震数据；
对叠前地震数据进行哈特莱变换，得到变换后的地震数据；
将变换后的地震数据划分为低频数据和高频数据；所述低频数据包括至少一个低频变换系数组，所述高频数据包括至少一个高频变换系数组；
分别对低频变换系数组和高频变换系数组进行不同级数的压缩编码，得到压缩后的数据，所述低频变换系数组的压缩编码级数大于高频变换系数组的压缩编码级数，所述压缩后的数据包括各级目标编码符号组和各级辅助扫描数据；
所述压缩编码包括：
在主扫描阶段，对变换系数组进行逐级编码扫描得到各级初始编码符号组，逐级编码中的每一级编码分别对应一个阈值，所述初始编码符号组包括重要编码符号和次要编码符号；删除各级初始编码符号组中最后一个重要编码符号后面的次要编码符号，得到各级目标编码符号组；
根据初级编码符号组中的重要编码符号确定变换系数组中的重要变换系数；
在辅助扫描阶段，根据每一级编码分别对应的阈值，逐级对变换系数组中的每一个重要变换系数进行量化，输出各级辅助扫描数据。
根据上述技术手段，通过哈特莱变换将叠前地震数据中的高频部分和低频部分区分开，然后分别对低频部分和高频部分进行不同级数的压缩编码，将集中了地震数据主要能量的低频部分进行高级数编码，保证压缩数据的信噪比，高频部分进行低级数编码，提高数据的压缩比，同时，在压缩编码过程中，将最后一个重要编码符号后面的次要编码符号删除，在保障数据完整性的同时，进一步减少压缩编码后的数据量，节约数据传输资源。
在本申请实施例中，将变换后的地震数据划分为低频数据和高频数据，包括：
根据地震数据的采集参数及资料品质将变换后的地震数据划分为低频数据和高频数据。
根据上述技术手段，可以更准确的将变换后的地震数据划分为低频数据和高频数据。
在本申请实施例中，所述资料品质包括奈奎斯特频率，所述采样参数包括：频率采样间隔；
根据地震数据的采集参数及资料品质将变换后的地震数据划分为低频数据和高频数据，包括：
根据奈奎斯特频率以及频率采样间隔计算确定低频数据的频率范围以及高频数据的频率范围；
根据确定的频率范围将变换后的地震数据划分为低频数据和高频数据。
根据上述技术手段，依据数据采集参数来划分高频数据和低频数据，划分方式与数据实际更相符，更有利于后续压缩后保证数据的完整性。
在本申请实施例中，所述低频数据的频率范围满足如下关系：
所述高频数据的频率范围满足如下关系：
或
其中，f1为低频频率，f2为高频频率，fmax为奈奎斯特频率，df为频率采样间隔。
根据上述技术手段，可以准确将变换后的数据划分为高频部分和低频部分。
在本申请实施例中，在主扫描阶段，对变换系数组进行逐级编码扫描得到各级初始编码符号组，包括：
根据迭代阈值函数以及变换系数组确定当前级编码扫描对应的第一阈值；
比较所述变换系数组中每一个变换系数的绝对值与所述第一阈值的大小关系；
若所述变换系数的绝对值大于或等于所述第一阈值，则将所述变换系数编码为符号P或符号N；其中，所述符号P对应于取值为正的重要变换系数，所述符号N对应的于取值为负的重要变换系数，所述符号P和所述符号N为重要编码符号；
若所述变换系数的绝对值小于所述第一阈值，则将所述变换系数编码为符号T或符号Z或不编码；其中，所述符号T和所述符号Z为次要编码符号；
根据所述符号P、所述符号N、所述符号T和所述符号Z，得到当前级初始编码符号组。
根据上述技术手段，将变换系数组转换为初始编码符号组。
在本申请实施例中，在辅助扫描阶段，根据每一级编码分别对应的阈值，逐级对变换系数组中的每一个重要变换系数进行量化，输出各级辅助扫描数据，包括：
根据所述迭代阈值函数和所述变换系数组确定当前级编码对应的阈值；
根据当前级编码对应的阈值，确定当前级编码对应的量化区间，所述量化区间被分为2个子区间；
根据当前级编码对应的量化区间，确定每一个重要变换系数的当前级量化结果；
根据每一个重要变换系数的当前级量化结果，输出当前级辅助扫描数据。
在本申请实施例中，根据当前级编码对应的量化区间，确定每一个重要变换系数的当前级量化结果，包括：
若所述重要变换系数属于所述编码对应的量化区间的前一个子区间，则量化结果为0，若所述重要变换系数属于所述编码对应的量化区间的后一个子区间，则量化结果为1。
在本申请实施例中，在分别对低频变换系数组和高频变换系数组进行不同级数的压缩编码之后，所述方法还包括：
对压缩后的数据进行相应级数的解码，得到解码后的数据；
将解码后的数据合并后进行哈特莱反变换，得到重构地震数据。
根据上述技术手段，将压缩编码后的数据还原得到重构地震数据。
本申请第二方面提供一种叠前地震数据分频压缩装置，所述装置包括：
数据获取单元，用于获取叠前地震数据；
数据变换单元，用于对叠前地震数据进行哈特莱变换，得到变换后的地震数据；
数据划分单元，用于将变换后的地震数据划分为低频数据和高频数据；所述低频数据包括至少一个低频变换系数组，所述高频数据包括至少一个高频变换系数组；
数据压缩单元，用于分别对低频变换系数组和高频变换系数组进行不同级数的压缩编码，得到压缩后的数据，所述低频变换系数组的压缩编码级数大于高频变换系数组的压缩编码级数，所述压缩后的数据包括各级目标编码符号组和各级辅助扫描数据；
所述数据压缩单元包括：
主扫描模块，用于对变换系数组进行逐级编码扫描得到各级初始编码符号组，逐级编码中的每一级编码分别对应一个阈值，所述初始编码符号组包括重要编码符号和次要编码符号；删除各级初始编码符号组中最后一个重要编码符号后面的次要编码符号，得到各级目标编码符号组；
确定模块，用于根据初级编码符号组中的重要编码符号确定变换系数组中的重要变换系数；
辅助扫描模块，用于根据每一级编码分别对应的阈值，逐级对变换系数组中的每一个重要变换系数进行量化，输出各级辅助扫描数据。
根据上述技术手段，通过哈特莱变换将叠前地震数据中的高频部分和低频部分区分开，然后分别对低频部分和高频部分进行不同级数的压缩编码，将集中了地震数据主要能量的低频部分进行高级数编码，保证压缩数据的信噪比，高频部分进行低级数编码，提高数据的压缩比，同时，在压缩编码过程中，将最后一个重要编码符号后面的次要编码符号删除，在保障数据完整性的同时，进一步减少压缩编码后的数据量，节约数据传输资源。
本申请第三方面提供一种计算机设备，所述计算机设备包括处理器和存储器，所述存储器中存储有计算机程序，所述计算机程序由所述处理器加载并执行以实现所述的叠前地震数据分频压缩方法。
本申请第四方面提供一种计算机可读存储介质，所述计算机可读存储介质中存储有计算机程序，所述计算机程序由处理器加载并执行以实现所述的叠前地震数据分频压缩方法。
本申请第五方面提供一种计算机程序产品，所述计算机程序产品包括计算机程序，所述计算机程序存储在计算机可读存储介质中，处理器从所述计算机可读存储介质读取并执行所述计算机程序，以实现所述的叠前地震数据分频压缩方法。
通过上述技术方案，通过对哈特莱域高频和低频数据分开进行EZW编码，由于地震数据的能量主要集中在低频部分，低频数据编码级数高，保证了压缩数据的信噪比，高频数据编码级数低，提高了数据的压缩比。本方法能够较好的同时保证较高的信噪比和压缩比，更符合地震数据高精度压缩要求。
本发明实施方式的其它特征和优点将在随后的具体实施方式部分予以详细说明。
附图说明
附图是用来提供对本发明实施方式的进一步理解，并且构成说明书的一部分，与下面的具体实施方式一起用于解释本发明实施方式，但并不构成对本发明实施方式的限制。在附图中：
图1是EZW编码方法提供的主扫描编码的示意图；
图2是EZW编码方法提供的8×8小波系数组的示意图；
图3是EZW编码方法提供的第1级主扫描编码的示意图；
图4是EZW编码方法提供的第2级主扫描编码的示意图；
图5是本申请一种实施方式提供的叠前地震数据分频压缩方法流程图；
图6是本申请一种实施方式提供的一个Sigsbee 2A模型单炮采集的叠前地震数据示意图；
图7是本申请一种实施方式提供的哈特莱变换结果示意图；
图8a是本申请一种实施方式提供的低频数据部分示意图；
图8b是本申请一种实施方式提供的高频数据部分示意图；
图9是本申请一种实施方式提供的低频编码级数8时，不同高频编码级数解压数据信噪比对比示意图；
图10是本申请一种实施方式提供的低频编码级数8时，不同高频编码级数解压数据压缩比对比示意图；
图11是本申请一种实施方式提供的低频编码级数10时，不同高频编码级数解压数据信噪比对比示意图；
图12是本申请一种实施方式提供的低频编码级数10时，不同高频编码级数解压数据压缩比对比示意图；
图13本申请一种实施方式提供的叠前地震数据分频压缩装置框图。
具体实施方式
以下结合附图对本发明的具体实施方式进行详细说明。应当理解的是，此处所描述的具体实施方式仅用于说明和解释本发明，并不用于限制本发明。
在对本申请技术方案进行介绍说明之前，先对本申请涉及的一些概念进行定义和说明。
EZW算法：EZW是一种图像编码算法。它的核心是在小波域进行逐级编码和量化。主扫描通过逐级编码确定重要系数的符号和空间位置；辅助扫描只对主扫描中的重要系数进行精细量化。
EZW算法的步骤有以下3步。
(1)确定迭代阈值公式。EZW采用逐级量化编码，逐级量化依次应用一系列阈值T1，T2，…，TN来确定小波系数的重要性：
其中，c为小波系数，N为最大编码级数，Ti为第i次编码对应的阈值。
(2)主扫描。主扫描用于确定重要系数的符号和空间位置，通过逐级扫描进行粗量化，通过零树结构提高编码效率。请参考图1，其示出了EZW算法提供的主扫描编码的示意图。首先利用小波系数的零树特性，在第i级扫描时，对小波系数做标记，当小波系数绝对值大于Ti时，则其为第i级扫描的重要系数，编码为符号P(正的重要系数)或符号N(负的重要系数)；否则其为不重要系数。如果重要系数在第i级扫描出来，则不会在下一级再继续进行扫描和编码。如果小波系数是关于阈值Ti的不重要系数，并且其子孙都小于阈值Ti，那么其为零树根T，零树根的子孙不需要编码；但在该系数的子孙中若存在大于设定阈值Ti的系数，则其为孤立零值Z。
(3)辅助扫描。对主扫描表的重要系数(P与N对应的小波系数)进行精细量化。在第i级扫描时，量化区间的最大值是初始阈值T1的2倍，最小值为当前阈值Ti，总的量化区间为[Ti，2T1)，量化间隔为Ti，故第i级扫描的量化器数目为(请参考公式2)：
量化器区间为[dTi，dTi+Ti)，其中1≤d≤Q，将其等分为2个量化子区间[dTi，dTi+0.5Ti)和[dTi+0.5Ti，dTi+Ti)。若小波系数属于前一子区间，则输出量化符号“0”，否则输出量化符号为“1”。输出的符号“0”和“1”由一个辅助扫描表记录。确定量化器数目和量化区间后，就要确定每个量化器中“0”和“1”对应的量化值。在第q个量化器中，符号为“0”的重构值为，请参考公式3：
Ti(q+0.25)(公式3)
符号为“1”的重构值为，请参考公式4：
Ti(q+0.75)(公式4)
上一级编码的重要系数要在下一级编码中继续量化，下一级编码的重要系数不会在上一级中量化，从而减少量化输出的符号数目。由于小波系数的稀疏性，大的重要系数被精细量化(被量化的次数多)，小的重要系数量化次数少，占的位数少。
请参考图2，其示出了一个实施例提供的8×8小波系数组的示意图。如图2所示，其中绝对值最大的小波系数为63，因此根据公式1，可以得到根据Ti＝T1/2i-1，可以得到T1＝32，T2＝16，T3＝8，T4＝4，即第1级编码对应的阈值为32，第2级编码对应的阈值为16，第3级编码对应的阈值8，第4级编码对应的阈值为4。
请参考表1，其示出了使用EZW算法对图2所示的小波系数进行主扫描和辅助扫描输出的符号。在第1级主扫描中，以阈值为32，对图2中的小波系数进行扫描，若小波系数的绝对值大于或等于32则该小波系数被编码为符号P或符号N，代表其为重要的小波系数，其中若小波系数为正数则被编码为P，若小波系数为负数则被编码为N，若小波系数的绝对值小于32，通过判断其是否为零树的子孙系数，对其进行编码或者对其不进行编码。
示例性地，第1级主扫描中，小波系数-31(21处)不是零树的子孙系数且该小波系数的子孙系数存在重要系数，因此被编码为Z，代表其为孤立零值。示例性地，小波系数23(22处)不是零树的子孙系数且该小波系数的子孙系数不存在重要系数，因此被编码为T，代表其为零树根。
由于图2中小波系数63(23处)、小波系数-34(24处)、小波系数49(25处)和小波系数47(26处)的绝对值大于第1级编码对应的阈值32，因此在第1级主扫描中对应的编码分别为P、N、P和P。请参考图3，其示出了EZW算法一个实施例提供的第1级主扫描编码的示意图。图中P1中的P代表将图2中63的编码符号编码为P，下标1代表该编码符号对应了表1中第1级主扫描输出序列的第1个符号，并且当一个小波系数的输出符号为T时，它的所有子孙系数就不再扫描，并用“×”表示。
在第1级辅助扫描中，是对第1级主扫描中的重要小波系数63、-34、49和47进行量化，示例性地，对于小波系数63，确定其辅助扫描输出的量化符号的过程如下：
(1)确定第1级主扫描的量化器，包括确定量化器数目和量化器区间。根据公式2，量化器数目为因此d的取值为1，量化器区间为[dTi，dTi+Ti)，即[32，64)，将其等分为2个量化子区间[32，48)和[48，64)。
(2)确定小波系数编码字符。小波系数63处于区间[32，64)的后一个子区间[48，64)中，因此辅助扫描的量化符号为1(对应第1级辅助扫描输出的第1个字符“1”)；示例性地，小波系数-34的绝对值处于区间[32，64)的前一个子区间[32，48)中，因此辅助扫描编码字符为0(对应第1级辅助扫描输出的第2个字符“0”)。
根据上述方法可以确定第2级的主扫描和辅助扫描的编码字符。在第2级主扫描中，以阈值为16，对图2中的小波系数进行扫描。请参考图4，其示出了本申请一个实施例提供的第2级主扫描编码的示意图。由于图2中小波系数63、-34、49和47在第1级主扫描中已经被编码，用标记，表示在第2级主扫描中，不对其进行编码。
第2级主扫描中将小波系数-31(21处)、23(22处)分别编码为符号N、符号P。在第2级辅助扫描中，是对第1级主扫描中的重要小波系数63、-34、49和47和第2级主扫描中重要小波系数-31和23进行量化，示例性地，对于小波系数63，确定其辅助扫描输出的符号的过程如下：
(1)确定第1级主扫描的量化器，包括确定量化器数目和量化器区间。根据公式2，量化器数目为因此d的取值为1、2、3，第1个量化器区间为[16，32)，第2个量化器区间为[32，48)，第3个量化器区间为[48，64)。
(2)确定小波系数编码字符。小波系数63处于第3个量化器区间为[48，64)的后一个子区间[56，64)中，因此辅助扫描编码字符为1(对应第2级辅助扫描输出的第1个字符“1”)；示例性地，小波系数-34的绝对值位于第2个量化器区间为[32，48)的前一个子区间[32，40)中，因此辅助扫描编码字符为0(对应第1级辅助扫描输出的第2个字符“0”)。
根据上述方法，可以确定出每一级主扫描和辅助扫描的编码符号。见下表1所示。
表1
通过上述方法对小波系数进行逐级编码，上一级编码出的次要编码符号孤立零值(符号Z)和零树根(符号T)也会在下一级进行重新扫描和编码，请参考图3中的31处、32处和图4中的对应的41处、42处，该小波系数在第1级主扫描中进行编码的同时，也在第2级主扫描中进行编码，这样会导致编码出的孤立零值和零树根的数量增多，降低数据压缩比，并且需要占用更多的存储空间。
因此，本申请提出一种叠前地震数据分频压缩方法，该方法首先对地震数据进行哈特莱域变换，然后根据地震数据本身采集参数与资料品质，划分低频部分和高频部分；低频数据编码级数高，保证压缩数据的信噪比，高频数据编码级数低，提高数据的压缩比。在此基础上，采用改进的EZW算法，在每一级主扫描完成后，删除得到的初始编码符号组中最后一个重要编码符号后面的次要编码符号，得到目标编码符号组，实现进一步减少数据量，节省存储空间，减少对数据传输通道的占用。
图5是本发明一种实施方式提供的叠前地震数据分频压缩方法流程图。该方法各步骤的执行主体可以是计算机设备。如图5所示，所述方法包括如下步骤S1-S4。
S1：获取叠前地震数据。
在本申请实施例中，叠前地震数据是指经过采集和处理，但未进行地震偏移校正及叠加的地震数据。它包含了从地震仪器记录的地震波信号的全部信息，包括地下介质的反射和折射特征。叠前地震数据可以用于地震成像和地下构造解释，但由于未经过偏移校正，位于不同位置的地下界面可能会有错位的现象。如图6所示，该图示出了一个Sigsbee 2A模型单炮采集的叠前地震数据。
S2：对叠前地震数据进行哈特莱变换，得到变换后的地震数据。按下式对地震数据进行变换：
其中，x(t)为地震数据，cast＝cost+sint，f为频率，t为时间，dt为时间的微分。哈特莱变换将叠前地震数据中高频部分和低频部分分开，变换后的地震数据如图7所示。
S3：将变换后的地震数据划分为低频数据和高频数据；所述低频数据包括至少一个低频变换系数组，所述高频数据包括至少一个高频变换系数组。
在本申请实施例中，将变换后的地震数据划分为低频数据和高频数据，包括：
根据地震数据的采集参数及资料品质将变换后的地震数据划分为低频数据和高频数据。
根据上述技术手段，可以更准确的将变换后的地震数据划分为低频数据和高频数据。
在本申请实施例中，所述资料品质包括奈奎斯特频率，所述采样参数包括：频率采样间隔；
首先，根据地震数据的采集参数及资料品质将变换后的地震数据划分为低频数据和高频数据，包括：
根据奈奎斯特频率以及频率采样间隔计算确定低频数据的频率范围以及高频数据的频率范围，所述低频数据的频率范围满足如下关系：
所述高频数据的频率范围满足如下关系：
或
其中，f1为低频频率，f2为高频频率，fmax为奈奎斯特频率，df为频率采样间隔。
根据上述技术手段，可以准确将变换后的数据划分为高频部分和低频部分。
然后，根据确定的频率范围将变换后的地震数据划分为低频数据和高频数据，分频后的结果如图8a和图8b所示，图8a是低频数据部分，图8b是高频数据部分。
根据上述技术手段，依据数据采集参数来划分高频数据和低频数据，划分方式与数据实际更相符，更有利于后续压缩后保证数据的完整性。
S4：分别对低频变换系数组和高频变换系数组进行不同级数的压缩编码，得到压缩后的数据，所述低频变换系数组的压缩编码级数大于高频变换系数组的压缩编码级数，所述压缩后的数据包括各级目标编码符号组和各级辅助扫描数据。将集中了地震数据主要能量的低频部分进行高级数编码，保证压缩数据的信噪比，高频部分进行低级数编码，提高数据的压缩比。
在本申请实施例中，所述压缩编码包括：
在主扫描阶段，对变换系数组进行逐级编码扫描得到各级初始编码符号组，逐级编码中的每一级编码分别对应一个阈值，所述初始编码符号组包括重要编码符号和次要编码符号；删除各级初始编码符号组中最后一个重要编码符号后面的次要编码符号，得到各级目标编码符号组。
主扫描阶段进行逐级编码过程与上文描述的EZW编码过程相同，根据迭代阈值函数以及变换系数组确定当前级编码扫描对应的第一阈值。上文已描述通过迭代阈值函数，可以得到每一级编码对应的阈值。其中，第1级编码对应的阈值为32，第2级编码对应的阈值为16，第3级编码对应的阈值8，第4级编码对应的阈值为4。
比较所述变换系数组中每一个变换系数的绝对值与所述第一阈值的大小关系；
若所述变换系数的绝对值大于或等于所述第一阈值，则将所述变换系数编码为符号P或符号N；其中，所述符号P对应于取值为正的重要变换系数，所述符号N对应的于取值为负的重要变换系数，所述符号P和所述符号N为重要编码符号；
若所述变换系数的绝对值小于所述第一阈值，则将所述变换系数编码为符号T或符号Z或不编码；其中，符号T对应的变换系数为零树根，符号Z对应的变换系数为孤立零值，所述符号T和所述符号Z为次要编码符号；
根据所述符号P、所述符号N、所述符号T和所述符号Z，得到当前级初始编码符号组。根据上述技术手段，将变换系数组转换为初始编码符号组。然后，删除各级初始编码符号组中最后一个重要编码符号后面的次要编码符号，得到各级目标编码符号组。请参考表2，其示出了对图2中的小波系数进行主扫描最终得到的目标编码符号组如表2所示。
表2
在主扫描完成后，根据初级编码符号组中的重要编码符号确定变换系数组中的重要变换系数，即主扫描阶段编码符号为P和N对应的变换系数。以图2中小波系数为例，若是第一级扫描，则，第一阈值为32，表2中第一级主扫描的第一个符号P对应了图2中63，我们可以根据主扫描输出的P和N确定出重要变换系数，从而在辅助扫描阶段，对其进行逐级量化。
在辅助扫描阶段，根据每一级编码分别对应的阈值，逐级对变换系数组中的每一个重要变换系数进行量化，输出各级辅助扫描数据，具体包括：
根据所述迭代阈值函数和所述变换系数组确定当前级编码对应的阈值，如前所述，在上文中，我们得到图2的小波系数组对应的每一级编码的阈值分别为：T1＝32，T2＝16，T3＝8，T4＝4。
根据当前级编码对应的阈值，确定当前级编码对应的量化区间，所述量化区间被分为2个子区间。根据总的量化区间为[Ti，2T1)，以及每一级编码对应的量化器数目以及量化器区间。可以得到，示例性地，第1级编码的量化器数目为1，量化器量化区间[32，64)，将其等分为2个子区间为：[32，48)和[48，64)。
示例性地，第2级编码的量化器数目为3，第1个量化器区间为[16，32)，将其等分为2个子区间为：[16，24)和[24，32)；第2个量化器区间为[32，48)，将其等分为2个子区间为：[32，40)和[40，48)；第3个量化器区间为[48，64)，将其等分为2个子区间为：[48，56)和[56，64)。
根据当前级编码对应的量化区间，确定每一个重要变换系数的当前级量化结果：若所述重要变换系数属于所述编码对应的量化区间的前一个子区间，则量化结果为0，若所述重要变换系数属于所述编码对应的量化区间的后一个子区间，则量化结果为1。小波系数63在第一级辅助扫描过程中处于区间[32，64)的后一个子区间[48，64)中，因此第一级辅助扫描的量化符号为1。在第二级辅助扫描过程中处于第3个量化器区间为[48，64)的后一个子区间[56，64)中，因此第二级辅助扫描编码字符为1。
根据每一个重要变换系数的当前级量化结果，输出当前级辅助扫描数据。
根据上述技术手段，通过哈特莱变换将叠前地震数据中的高频部分和低频部分区分开，然后分别对低频部分和高频部分进行不同级数的压缩编码，同时，在压缩编码过程中，将最后一个重要编码符号后面的次要编码符号删除，在保障数据完整性的同时，进一步减少压缩编码后的数据量，节约数据传输资源。
在本申请实施例中，低频变换系数组的压缩编码级数大于高频变换系数组的压缩编码级数，图9示出了低频变换系数组编码级数8时，不同高频变换系数组编码级数解压数据信噪比对比，图11示出了低频变换系数组编码级数10时，不同高频变换系数组编码级数解压数据信噪比对比，从图9和图11中可以看出，高频变换系数组编码级数越高，解压数据信噪比越大，且随着高频变换系数组编码级数增加，每增加一级编码，对信噪比的影响越来越小。图10示出了低频变换系数组编码级数8时，不同高频变换系数组编码级数解压数据压缩比对比，图12示出了低频变换系数组编码级数10时，不同高频变换系数组编码级数解压数据压缩比对比，从图10和图12中可以看出，高频变换系数组编码级数越高，解压数据压缩比越低。
为了得到最佳压缩效果，需要进行不同压缩级数的试验，固定低频变换系数组编码级数为8和10，分别进行不同高频变换系数组编码级数试验，低频变换系数组编码级数为8时，试验结果如表3所示，低频变换系数组编码级数为10时，试验结果如表4所示。
表3

表4
根据上述试验，得到如下试验认知：
(1)分频压缩中随着高频编码级数的增加，信噪比增加，压缩比降低。
(2)由于高频信息相对低频信息能量弱，在高频编码级数增加到一定程度的时候，信噪比增加不明显，压缩比降低。常规的8级EZW编码压缩数据信噪比30.82，压缩比5.71。哈特莱域分频EZW压缩低频编码级数为8，高频编码级数为4时，压缩数据信噪比30.94，压缩比9.44。常规的10级EZW编码压缩数据信噪比44.67，压缩比4.41。哈特莱域分频EZW压缩低频编码级数为10，高频编码级数为8时，压缩数据信噪比45.45，压缩比5.29。哈特莱域分频EZW压缩信噪比和压缩比均较常规的EZW压缩有所提高。
在本申请实施例中，在分别对低频变换系数组和高频变换系数组进行不同级数的压缩编码之后，所述方法还包括：
对压缩后的数据进行相应级数的解码，得到解码后的数据。具体的，根据迭代阈值函数确定每一级编码对应的量化器，量化器用于指示每一级编码分别对应的量化区间的数目以及量化区间的范围；根据量化器以及每一级编码分别对应的阈值确定重构函数；根据重构函数、编码符号组、辅助扫描数据和变换系数组确定每一个重要变换系数的重构值。
基于上文描述的每一级编码分别对应的量化区间的数目以及量化区间的范围，对图2中的小波系数进行重构。
对于变换系数63，其第一级主扫描输出的编码符号为P，第一级辅助扫描的编码为1，则可以确定，其大于第一级编码的阈值32，且在第一级编码中属于后一个子区间，即该变换系数的取值范围为[48，64)，重构值为T1(q+0.75)＝56；在第二级扫描中，第二级辅助扫描的量化器数目为3，T2＝T1/2＝16。总的量化区间为[16，64)，63位于第q＝3个量化区间[48，64)的后一子区间[56，64)，重构值为T2(q+0.75)＝60，在第三级扫描中，第三级辅助扫描的量化器数目为7，T3＝T2/2＝8。总的量化区间为[8，64)，63位于第q＝7个量化区间[56，64)的后一子区间[60，64)，重构值为T3(q+0.75)＝62；依次类推，最终可以得到解码后的重构值63。
将解码后的数据合并后进行哈特莱反变换，得到重构地震数据。
对合并后的数据按下式进行哈特莱反变换：
根据上述技术手段，将压缩编码后的数据还原得到重构地震数据。
本申请第二方面提供一种叠前地震数据分频压缩装置，如图13所示，所述装置包括：
数据获取单元，用于获取叠前地震数据，
数据变换单元，用于对叠前地震数据进行哈特莱变换，得到变换后的地震数据；
数据划分单元，用于将变换后的地震数据划分为低频数据和高频数据；所述低频数据包括至少一个低频变换系数组，所述高频数据包括至少一个高频变换系数组；
数据压缩单元，用于分别对低频变换系数组和高频变换系数组进行不同级数的压缩编码，得到压缩后的数据，所述低频变换系数组的压缩编码级数大于高频变换系数组的压缩编码级数，所述压缩后的数据包括各级目标编码符号组和各级辅助扫描数据；
所述数据压缩单元包括：
主扫描模块，用于对变换系数组进行逐级编码扫描得到各级初始编码符号组，逐级编码中的每一级编码分别对应一个阈值，所述初始编码符号组包括重要编码符号和次要编码符号；删除各级初始编码符号组中最后一个重要编码符号后面的次要编码符号，得到各级目标编码符号组；
确定模块，用于根据初级编码符号组中的重要编码符号确定变换系数组中的重要变换系数；
辅助扫描模块，用于根据每一级编码分别对应的阈值，对变换系数组中的每一个重要变换系数进行逐级量化，输出各级辅助扫描数据，所述辅助扫描数据包括每一个重要变换系数的量化级别和量化结果，所述量化级别是指所述重要变换系数量化的次数。
根据上述技术手段，通过哈特莱变换将叠前地震数据中的高频部分和低频部分区分开，然后分别对低频部分和高频部分进行不同级数的压缩编码，将集中了地震数据主要能量的低频部分进行高级数编码，保证压缩数据的信噪比，高频部分进行低级数编码，提高数据的压缩比， 同时，在压缩编码过程中，将最后一个重要编码符号后面的次要编码符号删除，在保障数据完整性的同时，进一步减少压缩编码后的数据量，节约数据传输资源。
本申请第三方面提供一种计算机设备，所述计算机设备包括处理器和存储器，所述存储器中存储有计算机程序，所述计算机程序由所述处理器加载并执行以实现所述的叠前地震数据分频压缩方法。
本申请第四方面提供一种计算机可读存储介质，所述计算机可读存储介质中存储有计算机程序，所述计算机程序由处理器加载并执行以实现所述的叠前地震数据分频压缩方法。
本申请第五方面提供一种计算机程序产品，所述计算机程序产品包括计算机程序，所述计算机程序存储在计算机可读存储介质中，处理器从所述计算机可读存储介质读取并执行所述计算机程序，以实现所述的叠前地震数据分频压缩方法。
本领域技术人员可以理解实现上述实施方式的方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序存储在一个存储介质中，包括若干指令用以使得单片机、芯片或处理器(processor)执行本发明各个实施方式所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上结合附图详细描述了本发明的可选实施方式，但是，本发明实施方式并不限于上述实施方式中的具体细节，在本发明实施方式的技术构思范围内，可以对本发明实施方式的技术方案进行多种简单变型，这些简单变型均属于本发明实施方式的保护范围。另外需要说明的是，在上述具体实施方式中所描述的各个具体技术特征，在不矛盾的情况下，可以通过任何合适的方式进行组合。为了避免不必要的重复，本发明实施方式对各种可能的组合方式不再另行说明。
此外，本发明的各种不同的实施方式之间也可以进行任意组合，只要其不违背本发明实施方式的思想，其同样应当视为本发明实施方式所公开的内容。

Claims (11)

  1. 一种叠前地震数据分频压缩方法，其特征在于，所述方法包括：
    获取叠前地震数据；
    对叠前地震数据进行哈特莱变换，得到变换后的地震数据；
    将变换后的地震数据划分为低频数据和高频数据；所述低频数据包括至少一个低频变换系数组，所述高频数据包括至少一个高频变换系数组；
    分别对低频变换系数组和高频变换系数组进行不同级数的压缩编码，得到压缩后的数据，所述低频变换系数组的压缩编码级数大于高频变换系数组的压缩编码级数，所述压缩后的数据包括各级目标编码符号组和各级辅助扫描数据；
    所述压缩编码包括：
    在主扫描阶段，对变换系数组进行逐级编码扫描得到各级初始编码符号组，逐级编码中的每一级编码分别对应一个阈值，所述初始编码符号组包括重要编码符号和次要编码符号；删除各级初始编码符号组中最后一个重要编码符号后面的次要编码符号，得到各级目标编码符号组；
    根据初级编码符号组中的重要编码符号确定变换系数组中的重要变换系数；
    在辅助扫描阶段，根据每一级编码分别对应的阈值，逐级对变换系数组中的每一个重要变换系数进行量化，输出各级辅助扫描数据。
  2. 根据权利要求1所述的叠前地震数据分频压缩方法，其特征在于，将变换后的地震数据划分为低频数据和高频数据，包括：
    根据地震数据的采集参数及资料品质将变换后的地震数据划分为低频数据和高频数据。
  3. 根据权利要求2所述的叠前地震数据分频压缩方法，其特征在于，所述资料品质包括奈奎斯特频率，所述采样参数包括频率采样间隔；
    根据地震数据的采集参数及资料品质将变换后的地震数据划分为低频数据和高频数据，包括：
    根据奈奎斯特频率以及频率采样间隔计算确定低频数据的频率范围以及高频数据的频率范围；
    根据确定的频率范围将变换后的地震数据划分为低频数据和高频数据。
  4. 根据权利要求3所述的叠前地震数据分频压缩方法，其特征在于，所述低频数据的频率范围满足如下关系：
    所述高频数据的频率范围满足如下关系：或
    其中，f1为低频频率，f2为高频频率，fmax为奈奎斯特频率，df为频率采样间隔。
  5. 根据权利要求1所述的叠前地震数据分频压缩方法，其特征在于，在主扫描阶段，对变换系数组进行逐级编码扫描得到各级初始编码符号组，包括：
    根据迭代阈值函数以及变换系数组确定当前级编码扫描对应的第一阈值；
    比较所述变换系数组中每一个变换系数的绝对值与所述第一阈值的大小关系；
    若所述变换系数的绝对值大于或等于所述第一阈值，则将所述变换系数编码为符号P或符号N；其中，所述符号P对应于取值为正的重要变换系数，所述符号N对应的于取值为负的重要变换系数，所述符号P和所述符号N为重要编码符号；
    若所述变换系数的绝对值小于所述第一阈值，则将所述变换系数编码为符号T或符号Z或不编码；其中，所述符号T和所述符号Z为次要编码符号；
    根据所述符号P、所述符号N、所述符号T和所述符号Z，得到当前级初始编码符号组。
  6. 根据权利要求1所述的叠前地震数据分频压缩方法，其特征在于，在辅助扫描阶段，根据每一级编码分别对应的阈值，逐级对变换系数组中的每一个重要变换系数进行量化，输出各级辅助扫描数据，包括：
    根据迭代阈值函数和所述变换系数组确定当前级编码对应的阈值；
    根据当前级编码对应的阈值，确定当前级编码对应的量化区间，所述量化区间被分为2个子区间；
    根据当前级编码对应的量化区间，确定每一个重要变换系数的当前级量化结果；
    根据每一个重要变换系数的当前级量化结果，输出当前级辅助扫描数据。
  7. 根据权利要求6所述的叠前地震数据分频压缩方法，其特征在于，根据当前级编码对应的量化区间，确定每一个重要变换系数的当前级量化结果，包括：
    若所述重要变换系数属于所述编码对应的量化区间的前一个子区间，则量化结果为0，若所述重要变换系数属于所述编码对应的量化区间的后一个子区间，则量化结果为1。
  8. 根据权利要求1所述的叠前地震数据分频压缩方法，其特征在于，在分别对低频变换系数组和高频变换系数组进行不同级数的压缩编码之后，所述方法还包括：
    对压缩后的数据进行相应级数的解码，得到解码后的数据；
    将解码后的数据合并后进行哈特莱反变换，得到重构地震数据。
  9. 一种叠前地震数据分频压缩装置，其特征在于，所述装置包括：
    数据获取单元，用于获取叠前地震数据；
    数据变换单元，用于对叠前地震数据进行哈特莱变换，得到变换后的地震数据；
    数据划分单元，用于将变换后的地震数据划分为低频数据和高频数据；所述低频数据包括至少一个低频变换系数组，所述高频数据包括至少一个高频变换系数组；
    数据压缩单元，用于分别对低频变换系数组和高频变换系数组进行不同级数的压缩编码，得到压缩后的数据，所述低频变换系数组的压缩编码级数大于高频变换系数组的压缩编码级数，所述压缩后的数据包括各级目标编码符号组和各级辅助扫描数据；
    所述数据压缩单元包括：
    主扫描模块，用于对变换系数组进行逐级编码扫描得到各级初始编码符号组，逐级编码中的每一级编码分别对应一个阈值，所述初始编码符号组包括重要编码符号和次要编码符号；删除各级初始编码符号组中最后一个重要编码符号后面的次要编码符号，得到各级目标编码符号组；
    确定模块，用于根据初级编码符号组中的重要编码符号确定变换系数组中的重要变换系数；
    辅助扫描模块，用于根据每一级编码分别对应的阈值，逐级对变换系数组中的每一个重要变换系数进行量化，输出各级辅助扫描数据。
  10. 一种计算机设备，其特征在于，所述计算机设备包括处理器和存储器，所述存储器中存储有计算机程序，所述计算机程序由所述处理器加载并执行以实现如权利要求1至8任一项所述的叠前地震数据分频压缩方法。
  11. [根据细则26改正 10.01.2025]
    一种计算机可读存储介质，其特征在于，所述计算机可读存储介质中存储有计算机程序，所述计算机程序由处理器加载并执行以实现如权利要求1至8任一项所述的叠前地震数据分频压缩方法。
PCT/CN2024/126405 2023-12-19 2024-10-22 叠前地震数据分频压缩方法、装置、设备及存储介质 Pending WO2025130321A1 (zh)

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