A method and system for processing geological prediction information based on seismic waves

Abstract

This invention relates to the field of geological early warning technology, specifically to a method for processing geological prediction information based on seismic waves. The method includes the following steps: acquiring seismic data; obtaining N data files, each containing M component data; sequentially performing zero-drift correction, low-pass filtering, and asymmetric amplification on the M component data in the N data files; sequentially performing inter-trace equalization, first-arrival picking, and component fusion on the N asymmetric amplified seismic data; classifying the L sampling points based on the frequency of each sampling point's occurrence in the set TS and set BS, and judging the classified L sampling points using a judgment rule to obtain geological prediction information. The purpose of this invention is to solve the problems of existing geological prediction processing methods that require experts to manually annotate seismic wave parameters based on experience, resulting in long geological information acquisition times, high professionalism, reliance on experience, and slow processing speed.

Description

Technical Field

[0001] This invention relates to the field of geological early warning technology, specifically to a method and system for processing geological forecast information based on seismic waves. Background Technology

[0002] Advanced geological forecasting is an essential part of tunnel construction and plays an important role in information-based tunnel construction, safety assurance, and disaster prevention.

[0003] With the construction of challenging tunnel projects such as deep mountain tunnels and cross-sea tunnels, higher requirements have been placed on engineering geological surveys. In addition, adverse geological bodies such as faults, karst caves, and fractured rock masses are highly concealed, making it difficult to accurately reveal adverse geological conditions along the tunnel route.

[0004] Therefore, advanced geophysical exploration methods, including mechanical drilling, electrical resistivity tomography, electromagnetic methods, seismic wave methods, and infrared methods, have emerged, with seismic wave methods currently being the mainstream application. Because seismic waves involve numerous parameters, their processing requires specialized seismic data processing software, such as the multi-wave, multi-component seismic data processing system used by the Geophysical Instrument Research Laboratory of China University of Mining and Technology (Beijing). This software has a certain learning curve for users, requiring manual annotation of parameters based on experience. Typically, this is done by experienced experts, taking several hours or even tens of hours to process the data. This process is highly specialized, experience-dependent, and slow, thus affecting the acquisition of geological prediction information, slowing down tunnel construction progress, and increasing construction costs. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a geological prediction information processing method and system based on seismic waves. This method solves the problem that existing geological prediction processing methods require experts to manually annotate seismic wave parameters based on experience, resulting in long geological information acquisition times, high professionalism, high experience-based requirements, and slow processing speeds. Consequently, these methods slow down tunnel construction progress and increase construction costs.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a geological prediction information processing method based on seismic waves, comprising the following steps:

[0007] S1. Acquire M seismic data points containing N components using seismic data acquisition instruments. Each component contains L sampling points.

[0008] S2. Merge the component data at the same location in M ​​seismic data in sequence to obtain N data files, each containing M component data;

[0009] S3. Perform zero-drift correction, low-pass filtering, and asymmetric amplification on the M component data in the N data files respectively to obtain N asymmetric amplified seismic data files.

[0010] S4. Perform inter-trace equalization on N asymmetric magnified seismic data in sequence to obtain N inter-trace equalized seismic data files.

[0011] S5. Perform first arrival picking on the M component data in the N inter-trace equalized seismic data files respectively to obtain N seismic data files with first arrival picking.

[0012] S6. Perform component fusion on the M component data in the N first arrival picked seismic data files respectively to obtain N seismic data files with 1 component data.

[0013] S7. Based on the seismic data file containing N 1-component data, obtain the set TS of points that meet the conditions at the top and the set BS of points that meet the conditions at the bottom by using the sampling point number rule;

[0014] Based on the number of times each sampling point appears in the set TS of points satisfying the conditions at the top and the set BS of points satisfying the conditions at the bottom, the L sampling points are then categorized. The categorized L sampling points are then judged according to the judgment rules to obtain geological prediction information of the geological conditions.

[0015] Preferably, based on the geological condition forecast information, the distance d from L sampling points to the working face is calculated using the geological condition distance formula, thereby obtaining the geological condition forecast information distance from the working face.

[0016] The distance formula is d = 2 × p × V / H;

[0017] p is one of the L sampling points, H is the sampling frequency of the seismic data, and V is the preset velocity of the seismic data.

[0018] Preferably, in step S1, the seismic data includes waveform data, sampling frequency H, data type, and preset velocity V, and the N component data, sampling frequency H, number of sampling points, and data type of the M seismic data are the same.

[0019] Preferably, in step S3, the specific steps of zero drift correction are as follows:

[0020] In N data files containing M component data, let the waveform data of the j-th component in the i-th data file be S. i，j =Array[i, j];

[0021] S i，j Contains L sampling points {Si，j [1], S i，j [2], ..., S i，j [L-1], S i，j [L]};

[0022] According to S i，j The average number of sampling points from the int(L*0.2)th point to the Lth sampling point. To perform correction, the number of L sampling points in the j-th component data is reduced by... Right now Obtain the j-th component data from the i-th data file after zero-drift correction;

[0023] The j-th component data S in the i-th data file i，j The low-pass filter uses a Butterworth filter.

[0024] Preferably, in step S3, the j-th component data S in the i-th data file i，j The specific steps for asymmetric amplification are as follows:

[0025] First, take the j-th component data S from the i-th data file. i，j The absolute value of the L sampling points is used to obtain S′. i，j Let S′ i，j The ratio of the average number of sampling points from point 1 to point int(L / 5) to the average number of sampling points from point int(L*0.8) to point L is R. Then, L equal intervals R are taken within the interval [1, R]. i，j , will S i，j With R i，j Multiplying the corresponding numbers yields the asymmetrically amplified component data, i.e., S. i，j [p] = S i，j [p]*R i，j [p]

[0026] Preferably, in step S3, the j-th component data S in the i-th data file i，j The specific steps for asymmetric amplification are as follows:

[0027] First, take the j-th component data S from the i-th data file. i，j The absolute value of the L sampling points is used to obtain S′. i，j Let S′ i，j The ratio of the average number of sampling points from point 1 to point int(L / 5) to the average number of sampling points from point int(L*0.8) to point L is R. Then, L equal intervals R are taken within the interval [1, R]. i，j , will S i，j With R i，jMultiplying the corresponding numbers yields the asymmetrically amplified component data, i.e., S. i，j [p] = S i，j [p]*R i，j [p]

[0028] Preferably, in step S4, the N asymmetric magnified seismic data files are traversed. The specific steps for traversing each asymmetric magnified seismic data file are as follows:

[0029] Take the data of each component from N asymmetric magnified seismic data files, and let S be the data of the j-th component in the i-th data file. i，j For the j-th component data S i，j The absolute value of the number of sampling points is used to obtain S′. i，j S′ i，j Take the average value, that is, get the M average values ​​from the M component data in the i-th data file. Again Take the average value to get pass get

[0030] Each component data in the i-th data file {S i，1 S i，2 S i，2 S i，M} respectively with Multiply the values ​​at the corresponding positions in the middle;

[0031] After traversing through N asymmetric magnified seismic data files, the seismic data file after inter-trace equalization is obtained.

[0032] Preferably, in step S5, the initial pickup includes the following steps:

[0033] Traverse the j-th component data S in the i-th data file i，j Calculate the j-th component data S i，j The maximum value VMAX i，j ;

[0034] Traverse S from left to right i，j With L sampling points, the first one satisfying ≥ VMAX is obtained. i，j / 6 number of sampling points VTH i，j ;

[0035] From VTH i，j Iterate through the sampling points to the left to obtain the first sampling point count < 0, VM0. i，j The number of sampling points VM0 i，jThe value of the number of sampling points traversed is set to 0;

[0036] If VTH i，j or VM0 i，j If it does not exist, then the j-th component data S i，j constant.

[0037] Preferably, in step S6, the component fusion step for each seismic data file after initial arrival picking is as follows:

[0038] Let i be a seismic data file after initial arrival picking. Add the L sampling points at corresponding positions in each component data within the initial arrival picking seismic data file to obtain one component data for the initial arrival picking seismic data file i. Then S i，1 [p] = S i，1 [p]+S i，2 [p]+...+S i，M [p]

[0039] Preferably, in step S7, the following operations are performed on the seismic data file for each 1-component data:

[0040] Let the seismic data file containing 1 component data be the i-th file. First, calculate the points at the top that meet the conditions: For component S... i，1 Calculate its gradient data G i，1 The absolute values ​​of the component data are then taken as the average value. Traverse S from left to right i，1 With G i，1 The positions of all sampling points that satisfy the sampling point number rule are obtained and denoted as T. set，i = {Tp1, Tp2, ...};

[0041] The rules for the number of sampling points are as follows: ① ② The location of points where the gradient on the left is <0; ③ The location of points where the gradient on the right is >0;

[0042] Let TS i for Traverse T set，i Let Tp be the position of a sampling point. k , in Tp k On the left, take two extreme points Tpl that are close to the number of sampling points. k，1 and Tpl k，2 Tpl k，2 >Tpl k，1 ;

[0043] In [Tpl k，1 Tpl k，2 Among the sampling points between [ ], the position Vl with the smallest absolute amplitude value is selected.k If Tpl k，1 or Tpl k，2 Does not exist, Vl k =0;

[0044] In Tp k Take two extreme points Tpr that are close to this point on the right. k，1 and Tpr k，2 Tpr k，2 >Tpr k，1 From [Tpr k，1 Tpr k，2 Among the sampling points between [ ], the position Vr with the smallest absolute amplitude value is selected. k If Tpr k，1 or Tpr k，2 If it does not exist, then Vr k =L, will [Vl k Vr k The positions of all sampling points between [ ] are merged into the set TS i .

[0045] By traversing N seismic data files containing 1-component data, a set of points satisfying the conditions at the top is obtained: TS = {TS1, TS2, ..., TS}. N};

[0046] Based on the set TS of points satisfying the conditions at the top, and by virtue of symmetry, we obtain the set BS = {BS1, BS2, ..., BS...} of points satisfying the conditions at the bottom. N}

[0047] Let p be the number of sampling points, and let Tj be the number of times p appears in each set of TS. p The number of times it appears in each set of BS is Bj. p Then, the number of L sampling points is classified and judged according to the judgment rules to obtain the geological condition prediction information;

[0048] The judgment rule is: if Tj p >Bj p The number of sampling points is classified as "fragmented"; if Tj p <Bj p The number of sampling points is classified as "broken and containing water"; if Tj p =Bj p If Tj ≠ 0, the number of sampling points is classified as "fragmented unknown"; p =Bj p If the value is 0, then the number of sampling points is classified as "complete".

[0049] The present invention also provides a geological prediction information processing system based on seismic waves, including the above-mentioned geological prediction information processing method based on seismic waves.

[0050] Compared with existing technologies, the beneficial effects of this solution are:

[0051] 1. This method processes M seismic data sets containing N components through zero-drift correction, low-pass filtering, and asymmetric amplification to obtain N seismic data files with one component each. Then, based on sampling point count rules, it derives a set of points TS at the top and a set of points BS at the bottom that meet the conditions. Finally, by classifying the sampling points and applying judgment rules to all classified sampling point counts, it obtains geological condition prediction information. This eliminates the need for manual intervention in the parameters of the seismic data, simplifying the acquisition of geological condition prediction information and increasing the speed of obtaining such information.

[0052] 2. By using seismic wave-based geological prediction information processing methods in the seismic wave-based geological prediction information processing system, the waveform images of the resulting geological prediction information are more recognizable and can increase the reliability of the prediction. Attached Figure Description

[0053] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to scale.

[0054] Figure 1 This is a flowchart of a geological prediction information processing method based on seismic waves according to the present invention;

[0055] Figure 2a This is a schematic diagram of the seismic data obtained by the geological prediction information processing method based on seismic waves according to the present invention.

[0056] Figure 2b This is a schematic diagram of a data file containing two components in a geological prediction information processing method based on seismic waves according to the present invention.

[0057] Figure 2c This is a schematic diagram of a data file containing two components of data after zero drift correction in a geological prediction information processing method based on seismic waves according to the present invention.

[0058] Figure 2d This is a schematic diagram of a data file containing two components after filtering, which is part of a geological prediction information processing method based on seismic waves according to the present invention.

[0059] Figure 2eThis is a schematic diagram of an asymmetrically enlarged data file containing two data components in a geological prediction information processing method based on seismic waves according to the present invention.

[0060] Figure 2f This is a schematic diagram of an inter-channel equalization of a data file containing two-component data in a geological prediction information processing method based on seismic waves according to the present invention.

[0061] Figure 2g This is a schematic diagram of the initial arrival of a data file containing two components of data in a geological prediction information processing method based on seismic waves according to the present invention.

[0062] Figure 2h This is a schematic diagram illustrating the process of obtaining a single-component seismic data after component fusion in a seismic wave-based geological prediction information processing method of the present invention.

[0063] Figure 2i This is a schematic diagram illustrating the geological forecast information obtained in the geological forecast information processing method based on seismic waves of the present invention.

[0064] Figure 3 This invention provides a geological prediction information processing method based on seismic waves, resulting in a map showing the geological prediction information of the distance from the working face.

[0065] Figure 4a A schematic diagram of the raw multi-component waveform data of seismic data;

[0066] Figure 4b This is a schematic diagram showing the waveform processing result of earthquake data by the geological prediction information processing method based on seismic waves according to the present invention.

[0067] Figure 4c This is a schematic diagram showing the waveform processing results of existing specialized software for processing seismic waves on seismic data. Detailed Implementation

[0068] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0069] Please refer to Figures 1 to 4c This embodiment provides a method for processing geological prediction information based on seismic waves, including the following steps:

[0070] S1. Acquire M seismic data points containing N components using seismic data acquisition instruments. Each component contains L sampling points.

[0071] The seismic data includes waveform data, sampling frequency H, data type, and preset velocity V, and the N component data, sampling frequency H, number of sampling points, and data type of the M seismic data are the same.

[0072] In this embodiment, as Figure 2a As shown, two seismic data points, each containing one component, are acquired using a seismic data acquisition instrument as input sources. The number of sampling points is L, which is 1024, the acquisition frequency is 10000Hz, and the data type is floating point.

[0073] S2. Merge the component data at the same location in M ​​seismic data in sequence to obtain N data files, each containing M component data; that is, obtain an N×M×L matrix array.

[0074] In this embodiment, as Figure 2b As shown, the data of the first component in the first data file and the data of the first component in the second data file are extracted respectively, and then merged into a single data file containing two components.

[0075] S3. Perform zero-drift correction, low-pass filtering, and asymmetric amplification on the M component data in the N data files respectively to obtain N asymmetric amplified seismic data files.

[0076] That is, firstly, zero drift correction is performed on the N×M component data;

[0077] In this embodiment, as Figure 2c As shown, for the first component data in the first data file, calculate the average value from the point int(1024 / 5) = 204 (int is rounded down) to the point 1024. Subsequently, the first component data S 1，1 =S 1，1 -(-5.2436). Similarly, the data S of the second component in the first data file can be obtained. 1，2 =S 1，2 -(-3.3642).

[0078] Then, the N×M component data are low-pass filtered.

[0079] In this embodiment, as Figure 2d As shown, both the first and second components of the data in the first data file are filtered using a Butterworth filter with an order of 8 and a cutoff frequency of 0.09766.

[0080] Finally, the N×M component data are asymmetrically amplified.

[0081] In this embodiment, as Figure 2eAs shown, S′ is obtained by first taking the absolute value of the number of all sampling points of the first component data in the first data file. 1，1 Calculate S′ 1，1 The average number of sampling points from 0 to int(1024 / 5) is 610.0117, S′ 1，1 The average number of sampling points from int(1024*0.8) to 1024 is 58.0613. The binary ratio R = 610.0117 / 58.0613 = 10.5063. Take 1024 equally spaced numbers R within the interval [1, 10.5063]. 1，1 = [1, 1.0093, 1.0186, ..., 10.4878, 10.4971, 10.5063], S 1，1 With R 1，1， Multiplying the corresponding numbers yields the asymmetrically amplified component data. Similarly, the asymmetrically amplified data of the second component in the first data file can be obtained.

[0082] S4. Perform inter-track equalization on N seismic data files.

[0083] In this embodiment, as Figure 2f As shown, for the first data file, the number of sampling points S for each component is... i，j Take the absolute value to get S′ i，j S′ i，j Taking the average yields two averages. Again Take the average value achievable

[0084] Finally, multiply the number of all sampling points in the first component data by 0.6124. Similarly, multiply the number of sampling points in the second component data by 2.7246 to obtain the seismic data file after inter-trace equalization.

[0085] S5. Perform initial arrival picking on N×M component data.

[0086] In this embodiment, as Figure 2g As shown, iterate through the two components of a data file. For the first component of the first data file, calculate S. 1，1 The maximum value VMAX 1，1 =4006.1112, traverse S from left to right. 1，1 With L sampling points, the first one > VMAX is obtained. 1，1 / 6 number of sampling points VTH 1，1 =758.5661, from VTH 1，1The sampling points are traversed from right to left until the first point VM0 with a value less than 0 is obtained. 1，1 = -63.3202 (number of the 114th sampling point), set the values ​​of sampling points 1 to 114 to 0, if VMAX 1，1 or VM0 1，1 If it does not exist, then the component data remains unchanged. Similarly, VM0 of the second component data in the first data file can be obtained. 1，2 =-13.0737 (number of the second sampling point), set the value of the first component data and the number of sampling points of the first component data in a data file to 0, and obtain a seismic data file after initial arrival picking.

[0087] S6. Perform component fusion on one initial arrival picked seismic data file in sequence to obtain one seismic data with one component.

[0088] In this embodiment, as Figure 2h As shown, for the first data file, the first component data and the second component data are fused together to form a new component S. 1，1 =S 1，1 +S 1，2 This yields a single seismic data file containing merged component data.

[0089] In addition, it can automatically classify the number of sampling points based on the fused component data.

[0090] S7. First, based on the seismic data file of the fused 1-component data, the set of points satisfying the conditions at the top (TS) and the set of points satisfying the conditions at the bottom (BS) are obtained by using the sampling point number rule.

[0091] In this embodiment, as Figure 2i As shown, for S 1，1 First, calculate the points at the top that meet the conditions: for component S 1，1 Calculate its gradient data G 1，1 S 1，1 Take the absolute value S′ of the component data 1，1 S′ 1，1 Take the average value Traverse S from left to right 1，1 With G 1，1 .

[0092] The rule for the number of sampling points is: satisfying ① the number of sampling points is greater than ② Points where the gradient to the left of the number of sampling points is <0, ③ points where the gradient to the right of the number of sampling points is >0, the positions of these points are T. set ={69, 112, 314, 615, 885}.

[0093] Let TS1 be Traverse T setTaking the 69th sampling point as an example, we take two nearest extreme points (points with different gradients on both sides) on the left and right sides of this point. The extreme points on the left are 43 and 58, and the extreme points on the right are 77 and 90. Between positions 43 and 58, the position closest to the x-axis is calculated as 51. Between positions 77 and 90, the position closest to the x-axis is calculated as 85. We then merge the positions of all points between 51 and 85 {51, 52, ..., 84, 85} into set TS1. After traversing all points, we can obtain:

[0094] TS1 = {51, ..., 85, 97, ..., 125, 302, ..., 322, 589, ..., 644, 872, ..., 899}. After traversing each file, we can obtain TS = {TS1}. Similarly, based on the symmetry of the values ​​of the top and bottom points, we can obtain the bottom point BS1 = {289, ..., 337}, BS = {BS1}.

[0095] Finally, based on the frequency of occurrence of each sampling point in the set TS of points satisfying the condition at the top and the set BS of points satisfying the condition at the bottom, the L sampling points are further categorized. A judgment rule is then used to evaluate the categorized L sampling points to derive the geological condition prediction information. The judgment rule is: if Tj p >Bj p The number of sampling points is classified as "fragmented"; if Tj p <Bj p The number of sampling points is classified as "broken and containing water"; if Tj p =Bj p If Tj ≠ 0, the number of sampling points is classified as "fragmented unknown"; p =Bj p If the value is 0, then the number of sampling points is classified as "complete".

[0096] That is, the 1024 sampling points are categorized. Taking the four sampling points of 1, 51, 289, and 302 as examples, the sampling point of 1 does not appear in either TS or BS, Tj1 = Bj1 = 0, so the sampling point of 1 is classified as "complete"; the sampling point of 51 appears in TS, Tj 51 =1 time, Bj appears in BS 51 =0 times, because Tj 51 >Bj 51 51 sampling points were classified as "fragmented"; 289 sampling points appeared in Tj in TS. 289 =0 times, Bj appears in BS 289 = 1 time, because Tj 289 >Bj 289 289 sampling points were classified as "fragmented and containing water"; 302 sampling points appeared in Tj in TS. 302=1 time, Bj appears in BS 302 = 1 time, because Tj 302 >Bj 302 The 302 sampling points are categorized as "fragmented and unknown". In this embodiment, on the horizontal axis, horizontal lines with values ​​between 5-20, 86-98, and 130-136 represent "fragmented", horizontal lines with values ​​between 41-42 represent "fragmented and water-bearing", and the remaining blank areas represent complete areas. Thus, geological conditions can be predicted based on the number of sampling points.

[0097] Meanwhile, based on the preset seismic wave velocity, this embodiment uses a velocity of 3000 m / s. Taking the 500th sampling point as an example, the distance d from this point to the working face... 500 =500*3000 / 10000 / 2=75m; After calculating the distance from sampling points 1 to 1024 to the working face, and based on the classification of each sampling point, the geological forecast information of the distance from the working face is obtained.

[0098] The present invention also provides a geological prediction information processing system based on seismic waves, including the above-mentioned geological prediction information processing method based on seismic waves.

[0099] Based on the above-mentioned method for processing geological prediction information based on seismic waves, the inventors ran this embodiment through software 10 times, obtaining an average computation time of 1.1s, of which the average data processing time was 0.06s, and the average time to obtain the predicted geological information was 1.04s. This improved the efficiency of obtaining the predicted geological information and the geological prediction information from the working face.

[0100] The inventors also compared the waveform data obtained by the system running this method with the waveform data processed by existing multi-wave multi-component seismic data processing system professional software.

[0101] Original graphic as Figure 4a As shown, the waveform processing results of the system using this method are as follows: Figure 4b As shown, the processing results of existing multi-wave multi-component seismic data processing systems are as follows: Figure 4c As shown, by comparing the waveforms of the two, the following conclusions can be drawn:

[0102] The waveforms and trends of the two waveforms are consistent, proving that this method can obtain correct waveform processing results.

[0103] Meanwhile, when the distance from the working face is relatively far, the waveform amplitude of existing multi-wave multi-component seismic data processing systems is significantly attenuated. The waveform image results obtained by using this method through the system are more recognizable and can increase the reliability of prediction.

[0104] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for processing geological prediction information based on seismic waves, characterized in that, Includes the following steps: S1. Acquire M seismic data points containing N components using seismic data acquisition instruments. Each component contains L sampling points. S2. Merge the component data at the same location in M ​​seismic data in sequence to obtain N data files, each containing M component data; S3. Perform zero-drift correction, low-pass filtering, and asymmetric amplification on the M component data in the N data files respectively to obtain N asymmetric amplified seismic data files. S4. Perform inter-trace equalization on N asymmetric magnified seismic data in sequence to obtain N inter-trace equalized seismic data files. S5. Perform first arrival picking on the M component data in the N inter-trace equalized seismic data files respectively to obtain N seismic data files with first arrival picking. S6. Perform component fusion on the M component data in the N first arrival picked seismic data files respectively to obtain N seismic data files with 1 component data. S7. Based on the seismic data file containing N 1-component data, obtain the set TS of points that meet the conditions at the top and the set BS of points that meet the conditions at the bottom by using the sampling point number rule; Based on the number of times each sampling point appears in the set TS of points satisfying the condition at the top and the set BS of points satisfying the condition at the bottom, the L sampling points are then classified. The L sampling points after classification are judged by the judgment rules to obtain geological prediction information of the geological conditions. In step S4, N asymmetric magnified seismic data files are traversed. The specific steps for traversing each asymmetric magnified seismic data file are as follows: Take the data of each component from N asymmetric magnified seismic data files, and let S be the data of the j-th component in the i-th data file. i,j For the j-th component data S i,j The absolute value of the number of sampling points is used to obtain S′. i,j S′ i,j Take the average value, that is, get the M average values ​​from the M component data in the i-th data file. Again Take the average value to get pass get Each component data in the i-th data file {S i,1 ,S i,2 ,S i,2 ,...,S i,M } respectively with Multiply the values ​​at the corresponding positions in the middle; After traversing through N asymmetric magnified seismic data files, the seismic data file after inter-trace equalization is obtained.

2. The geological prediction information processing method based on seismic waves according to claim 1, characterized in that, Based on the geological condition forecast information, the distance d from L sampling points to the working face is calculated using the geological condition distance formula, thus obtaining the geological condition forecast information distance from the working face. The distance formula is d = 2 × p × V / H; p is one of the L sampling points, H is the sampling frequency of the seismic data, and V is the preset velocity of the seismic data.

3. The geological prediction information processing method based on seismic waves according to claim 1, characterized in that, In step S1, the seismic data includes waveform data, sampling frequency H, data type and preset velocity V, and the N component data, sampling frequency H, number of sampling points and data type of the M seismic data are the same.

4. The geological prediction information processing method based on seismic waves according to claim 1, characterized in that, In step S3, the specific steps of zero drift correction are as follows: In N data files containing M component data, let the waveform data of the j-th component in the i-th data file be S. i,j =Array[i,j]; S i,j Contains L sampling points {S i,j [1],S i,j [2],...,S i,j [L-1],S i,j [L]}; According to S i,j The average number of sampling points from the int(L*0.2)th point to the Lth sampling point. To perform correction, the number of L sampling points in the j-th component data is reduced by... Right now Obtain the j-th component data from the i-th data file after zero-drift correction; The j-th component data S in the i-th data file i,j The low-pass filter uses a Butterworth filter.

5. The geological prediction information processing method based on seismic waves according to claim 1, characterized in that, In step S3, the j-th component data S in the i-th data file i,j The specific steps for asymmetric amplification are as follows: First, take the j-th component data S from the i-th data file. i,j The absolute value of the L sampling points is used to obtain S′. i,j Let S′ i,j The ratio of the average number of sampling points from point 1 to point int(L / 5) to the average number of sampling points from point int(L*0.8) to point L is R. Then, L equally spaced numbers R are taken within the interval [1,R]. i,j , will S i,j With R i,j Multiplying the corresponding numbers yields the asymmetrically amplified component data, i.e., S. i,j [p] = S i,j [p]*R i,j [p] 6. The geological prediction information processing method based on seismic waves according to claim 5, characterized in that, In step S5, the initial pickup includes the following steps: Traverse the j-th component data S in the i-th data file i,j Calculate the j-th component data S i,j The maximum value VMAX i,j ; Traverse S from left to right i,j With L sampling points, the first one satisfying ≥ VMAX is obtained. i,j / 6 number of sampling points VTH i,j ; From VTH i,j Iterate through the sampling points to the left to obtain the first sampling point count < 0, VM0. i,j The number of sampling points VM0 i,j The value of the number of sampling points traversed is set to 0; If VTH i,j or VM0 i,j If it does not exist, then the j-th component data S i,j constant.

7. The geological prediction information processing method based on seismic waves according to claim 6, characterized in that, In step S6, the component fusion steps for each seismic data file after initial arrival picking are as follows: Let i be a seismic data file after initial arrival picking. Add the L sampling points at corresponding positions in each component data within the initial arrival picking seismic data file to obtain one component data for the initial arrival picking seismic data file i. Then S i,1 [p] = S i,1 [p]+S i,2 [p]+...+S i,M [p] 8. The geological prediction information processing method based on seismic waves according to claim 1, characterized in that, In step S7, the following operations are performed on the seismic data file for each 1-component data: Let the seismic data file containing 1 component data be the i-th file. First, calculate the points at the top that meet the conditions: For component S... i,1 Calculate its gradient data G i,1 The absolute values ​​of the component data are then taken as the average value. Traverse S from left to right i,1 With G i,1 The positions of all sampling points that satisfy the sampling point number rule are obtained and denoted as T. set,i ={Tp1,Tp2,...}; The sampling point number rule is as follows: ① Component data ② The location of points where the gradient on the left is <0; ③ The location of points where the gradient on the right is >0; Let TS i for Traverse T set,i Let Tp be the position of a sampling point. k , in Tp k On the left, take two extreme points Tpl that are close to the number of sampling points. k,1 and Tpl k,2 Tpl k,2 >Tpl k,1 ; In [Tpl k,1 ,Tpl k,2 Among the sampling points between [ ], the position Vl with the smallest absolute amplitude value is selected. k If Tpl k,1 or Tpl k,2 Does not exist, Vl k =0; In Tpr k Take two extreme points Tpr that are close to this point on the right. k,1 and Tpr k,2 Tpr k,2 >Tpr k,1 From [Tpr k,1 ,Tpr k,2 Among the sampling points between [ ], the position Vr with the smallest absolute amplitude value is selected. k If Tpr k,1 or Tpr k,2 If it does not exist, then Vr k =L, will [Vl k Vr k The positions of all sampling points between [ ] are merged into the set TS i ; By traversing N seismic data files containing 1-component data, a set of points satisfying the conditions at the top is obtained: TS = {TS1, TS2, ..., TS}. N }; Based on the set TS of points satisfying the conditions at the top, and by applying symmetry, we obtain the set TS = {BS1, BS2, ..., BS} of points satisfying the conditions at the bottom. N }; Let p be the number of sampling points, and let Tj be the number of times p appears in each set of TS. p The number of times it appears in each set of BS is Bj. p Then, the number of L sampling points is classified and judged according to the judgment rules to obtain the geological condition prediction information; The judgment rule is: if Tj p >Bj p The number of sampling points is classified as "fragmented"; if Tj p <Bj p The number of sampling points is classified as "broken and containing water"; if Tj p =Bj p If Tj ≠ 0, the number of sampling points is classified as "fragmented unknown"; p =Bj p If the value is 0, then the number of sampling points is classified as "complete".

9. A geological prediction information processing system based on seismic waves, characterized in that: The method for processing geological prediction information based on seismic waves, as described in any one of claims 1 to 8.

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