A method and system for identifying a volcanic conduit based on high-frequency focused imaging
Patent Information
- Application Number
- CN202510557859.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-29
- Publication Date
- 2026-10-30
AI Technical Summary
然而，由于火山通道周边地震资料品质不佳，这些方法在应用过程中可能产生诸多解释假象
[0032]本发明采用的以上技术方案，与现有技术相比，具有的优点是：本方法采用多步骤处理流程实现火山通道的精确识别：首先对原始地震属性数据体实施提频优化处理，提升数据分辨率；其次对提频数据体进行通道增强处理，改善火山通道的识别精度；然后针对火山通道开展高频聚焦成像处理，通过优势频带聚焦优化，保留最能突显火山通道目标的频谱特征；最后从地震成像数据体中提取相干体、方差体和梯度模体三种叠后地震属性，通过多属性融合技术实现火山通道的精确拾取。该技术有效增强了火山通道目标的地震响应特征，使其与围岩背景形成显著差异，从而达到提高火山通道识别精度的目的。
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Figure CN123010067A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of oil and gas exploration interpretation technology, specifically to a method and system for identifying volcanic channels based on high-frequency focusing imaging. Background Technology
[0002] Current seismic exploration methods face significant ambiguity in identifying volcanic conduits. Currently, conduit identification primarily relies on post-stack seismic attribute analysis. Volcanic conduits are the main channels through which magma penetrates underground rock strata during eruptions or overflows; the seismic data within them is typically of poor quality and exhibits rapid lateral variations. Nevertheless, the distribution characteristics of volcanic conduits can still be characterized by extracting post-stack attributes from seismic data volumes.
[0003] In conventional seismic attribute detection, seismic facies analysis techniques such as variance, curvature, and edge detection are mainly used to define the distribution range of volcanic conduits. However, due to the poor quality of seismic data around volcanic conduits, these methods may produce many interpretative artifacts during application. Although seismic facies analysis techniques can effectively analyze the variation characteristics of sedimentary facies zones and depict the planar distribution of igneous rocks by distinguishing the differences in reflection characteristics between different seismic channels, they have significant limitations in describing the spatial distribution characteristics of volcanic conduits. Not only is it difficult to accurately represent their morphology, but the consistency rate between the identification results and actual drilling data is also relatively low. Summary of the Invention
[0004] The purpose of this invention is to propose a method and system for identifying volcanic passages based on high-frequency focusing imaging, which aims to enhance the seismic response characteristics of the target object, volcanic passages, so that it can be significantly distinguished in the surrounding geological environment.
[0005] According to a first aspect of the present disclosure, a method for identifying volcanic channels based on high-frequency focusing imaging is provided, comprising the following steps:
[0006] Seismic attribute data volume A of the target study area is obtained, and the seismic attribute data volume A is subjected to frequency up-up processing to improve seismic resolution and broaden the effective frequency band, thereby obtaining frequency up-up data volume B.
[0007] Volcano channel enhancement processing is applied to frequency-upgraded data volume B to obtain enhanced data volume C;
[0008] Seismic target samples of the actual drilled volcanic conduit were obtained and spectral analysis was performed to determine the dominant frequency band range of the volcanic conduit response. Based on target frequency focusing imaging technology, target frequency components within the dominant frequency band range were extracted from the enhanced data volume C for signal enhancement. The focused signal was then used to reconstruct the target area image to obtain the focused data volume D2.
[0009] The imaging effect of the focused data volume D2 is analyzed, and the effect is evaluated in combination with the known seismic response characteristics of the volcanic conduit. If the effect is not as expected, the imaging quality is improved by optimizing the target frequency selection until an ideal seismic imaging data volume is obtained.
[0010] The post-stack seismic attribute volume is extracted from the seismic imaging data volume, and the values of each attribute volume are normalized to the [0,1] interval to obtain the post-stack seismic attribute data volume E.
[0011] The post-stack seismic attribute data volume E of all earthquake target samples is overlaid, and the comprehensive feature data volume F of the volcanic conduit target samples is generated by weighted summation.
[0012] In one embodiment, for the seismic attribute data volume A of the target study area, the vertical data range of the target layer is extracted, and wavelet transform technology is used to perform frequency upscaling.
[0013] In one embodiment, a fault mean filtering algorithm is used when performing volcanic conduit enhancement processing to smooth the data and suppress noise, thereby obtaining the enhanced data volume C.
[0014] In one embodiment, based on seismic target samples of actual drilled volcanic channels, typical channel target areas are manually delineated on the profile, and time-frequency joint analysis is carried out on the target areas to determine the dominant frequency band range of the volcanic channel response.
[0015] In one embodiment, a Butterworth bandpass filter is used to remove interference signals that are not related to the main frequency, retaining only signals within the dominant frequency band. Specifically:
[0016] Obtain the center frequency Fc = (f1 + f2) / 2; where f1 and f2 are the lower and upper cutoff frequencies of the bandpass filter, respectively.
[0017] Obtain the bandwidth BW = f2 - f1, which represents the frequency range allowed by the bandpass filter;
[0018] By setting the lower limit frequency, upper limit frequency, and bandwidth of the bandpass filter, the target frequency data is saved, resulting in the bandpass filter data volume D1.
[0019] In one embodiment, the amplitude value of the bandpass filtered data volume D1 is mapped to the grayscale domain and converted into a grayscale value domain, and the grayscale variance is used as the basis for evaluating the degree of focus.
[0020] Gray-scale variance is used to evaluate image sharpness by taking the sum of squares of the differences between the gray-scale value of each pixel and the average gray-scale value.
[0021] When the image reaches the optimal focus state, the gray level changes most drastically due to the richest high-frequency signal components. At this time, the gray level variance reaches its maximum value. By comparing the gray level variance values at different focus positions, the optimal combination of focus parameters that makes the gray level variance reach its extreme value is determined, thereby obtaining the high-frequency focus data volume D2.
[0022] In one embodiment, the post-stack seismic attribute volume E includes a post-stack coherence attribute volume E1, a post-stack variance attribute volume E2, and a post-stack gradient mode attribute volume E3.
[0023] According to a second aspect of the present disclosure, a volcanic channel identification system based on high-frequency focusing imaging is provided, comprising:
[0024] The frequency upscaling module acquires seismic attribute data volume A of the target study area, performs frequency upscaling processing on the seismic attribute data volume A to improve seismic resolution and broaden the effective frequency band, and obtains frequency upscaling data volume B.
[0025] The enhancement module performs volcano channel enhancement processing on the frequency-upgraded data volume B to obtain the enhanced data volume C;
[0026] The focusing module acquires seismic target samples of the actual drilled volcanic conduit and performs spectral analysis to determine the dominant frequency band range of the volcanic conduit response. Based on target frequency focusing imaging technology, the target frequency components within the dominant frequency band range are extracted from the enhanced data volume C for signal enhancement, and the focused signal is used to reconstruct the target area image to obtain the focused data volume D2.
[0027] The imaging module analyzes the imaging effect of the focused data volume D2 and evaluates the effect in combination with the known seismic response characteristics of the volcanic conduit. If the effect is not as expected, the imaging quality is improved by optimizing the target frequency selection until an ideal seismic imaging data volume is obtained.
[0028] The attribute module extracts the post-stack seismic attribute volume from the seismic imaging data volume, normalizes the values of each attribute volume to the [0,1] interval, and obtains the post-stack seismic attribute data volume E.
[0029] The integration module performs overlay operations on the post-stack seismic attribute data volume E of all seismic target samples, and generates the integrated feature data volume F of the volcanic conduit target samples through a weighted summation method.
[0030] According to a third aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the memory, wherein the processor executes the program to implement the aforementioned method for identifying volcanic channels based on high-frequency focusing imaging.
[0031] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the aforementioned method for identifying volcanic channels based on high-frequency focusing imaging.
[0032] Compared with existing technologies, the above-mentioned technical solution adopted in this invention has the following advantages: This method employs a multi-step processing flow to achieve accurate identification of volcanic conduits: First, frequency upscaling optimization is performed on the original seismic attribute data to improve data resolution; second, channel enhancement processing is applied to the frequency-upgraded data to improve the identification accuracy of volcanic conduits; then, high-frequency focusing imaging processing is carried out on the volcanic conduits, retaining the spectral features that best highlight the volcanic conduit target through optimal frequency band focusing optimization; finally, three post-stack seismic attributes—coherence volume, variance volume, and gradient modulus—are extracted from the seismic imaging data, and accurate volcanic conduit picking is achieved through multi-attribute fusion technology. This technology effectively enhances the seismic response characteristics of volcanic conduit targets, making them significantly different from the surrounding rock background, thereby achieving the goal of improving the identification accuracy of volcanic conduits. Attached Figure Description
[0033] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.
[0034] Figure 1 The flowchart shows a volcanic passage identification method based on high-frequency focusing imaging.
[0035] Figure 2 A schematic diagram of the seismic attribute data volume for the target study area;
[0036] Figure 3 A schematic diagram of the frequency-upgraded data volume after wavelet transform;
[0037] Figure 4 A schematic diagram of the data volume after channel enhancement processing;
[0038] Figure 5 A graph showing the bandwidth and main frequency analysis;
[0039] Figure 6 Convert the channel-enhanced data volume into a grayscale color chart;
[0040] Figure 7 This is a schematic diagram of a high-frequency focusing data volume.
[0041] Figure 8 A schematic diagram illustrating enhanced coherence, variance, and gradient modulus data.
[0042] Figure 9 This is a schematic diagram of the final volcanic channel focused data volume processed by this method. Detailed Implementation
[0043] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.
[0044] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0045] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0046] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of this disclosure. It should be noted that each block in a flowchart or block diagram may represent a module, segment, or portion of code, which may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.
[0047] Example 1:
[0048] like Figure 1 As shown, this embodiment provides a volcanic channel identification method based on high-frequency focusing imaging, including the following steps:
[0049] S1. Obtain seismic attribute data volume A for the target study area, such as... Figure 2 As shown, the seismic attribute data volume A is subjected to frequency upscaling to improve seismic resolution and broaden the effective frequency band, resulting in frequency-upgraded data volume B, as follows. Figure 3 As shown;
[0050] Specifically, for the seismic attribute data volume A of the target study area, the vertical data range of the target layer is extracted, and wavelet transform technology is used to perform frequency upscaling, including:
[0051] Seismic attribute data volume A is a three-dimensional array denoted as f(t). It is decomposed into the time-scale domain using continuous wavelet transform. The purpose of this step is to decompose the seismic signal into wavelet components of different scales and frequencies, and then process these wavelet components.
[0052] W(a,b)=∫f(t)ψ*[(tb) / a]dt
[0053] In the formula, W(a,b) represents wavelet coefficients, f(t) represents seismic attribute data volume A, ψ(t) represents wavelet basis functions, and a and b represent scale factor and translation factor, respectively. Wavelet basis functions are a set of functions with specific shapes, which have the characteristics of time-frequency locality and multi-resolution analysis.
[0054] The resolution characteristics of wavelet transform are closely related to the scale factor: a smaller scale factor corresponds to high-frequency component analysis, providing more refined time localization capabilities and significantly improving time resolution; while a larger scale factor focuses on low-frequency component analysis, achieving better frequency resolution. By adjusting the parameter combination of scale factor a and translation factor b, accurate analysis of the signal's time-frequency characteristics can be achieved, thereby effectively improving the time-frequency resolution of seismic data. This multi-scale analysis method provides a flexible time-frequency analysis tool for volcanic conduit identification.
[0055] S2. Perform volcano channel enhancement processing on the frequency-upgraded data volume B to obtain the enhanced data volume C, as follows: Figure 4 As shown;
[0056] Specifically, the fault mean filtering algorithm is used during volcanic conduit enhancement to smooth the data and suppress noise, resulting in the enhanced data volume C, which includes:
[0057] Obtain a two-dimensional profile of the frequency-enhanced data volume B (M, N, O), with the O direction fixed. The image has M rows and N columns. For each pixel (x, y) in the image, 1 ≤ x ≤ M and 1 ≤ y ≤ N.
[0058] The pixel value B'(x,y) after mean filtering can be obtained by the following formula:
[0059]
[0060] Where: K is the total number of pixels in the filter template. For a filter of size (2r+1)×(2r+1), K=(2r+1) 2 r is the filter radius, which determines the filter size; the filter template is a (2r+1)×(2r+1) rectangular region centered at the current pixel (x,y).
[0061]
[0062] The above formula represents the sum of all pixel values within the filter template;
[0063] For each pixel in the image, we take the values of all pixels within a rectangular region surrounding it, then sum these values and divide by the total number of pixels (K). The result is the filtered new pixel value. This process iterates through every pixel in the image, thus obtaining the entire filtered image data volume C.
[0064] For each pixel in the image, a rectangular neighborhood window is created centered on that pixel. The average grayscale value of all pixels within this window is then obtained and used as the new grayscale value for the current pixel. This pixel-by-pixel sliding method is used to traverse the entire image, ultimately generating a smoothed enhanced data volume C. This filtering process effectively suppresses random noise while preserving the overall structural features of the image.
[0065] S3. Obtain seismic target samples from the actual drilled volcanic conduit and perform spectral analysis to determine the dominant frequency band range of the volcanic conduit response, such as... Figure 5 As shown; based on target frequency focusing imaging technology, target frequency components within the dominant frequency band are extracted from the enhanced data volume C for signal enhancement, and the image of the target region is reconstructed using the focused signal to obtain the focused data volume D2;
[0066] Specifically, based on seismic target samples from actual drilled volcanic conduits, typical conduit target areas are manually delineated on the profile, and time-frequency joint analysis is performed on the target areas to determine the dominant frequency band range of the volcanic conduit response. Butterworth bandpass filters are used to remove interference signals unrelated to the dominant frequency, retaining only signals within the dominant frequency band range, including:
[0067] Obtain the center frequency Fc = (f1 + f2) / 2; where f1 and f2 are the lower and upper cutoff frequencies of the bandpass filter, respectively.
[0068] Obtain the bandwidth BW = f2 - f1, which represents the frequency range allowed by the bandpass filter;
[0069] By setting the lower limit frequency, upper limit frequency, and bandwidth value of the bandpass filter, the target frequency data is saved, and the bandpass filter data body D1 is obtained.
[0070] High-frequency focusing processing based on grayscale transformation is performed on the bandpass filtered data volume: the amplitude values of bandpass filtered data volume D1 are mapped to the grayscale domain, and converted into grayscale values as follows. Figure 6 As shown, grayscale variance is used as the criterion for evaluating the degree of focus; its grayscale variance (SMD) function is:
[0071] SMD = ∑[(f(x,y)-μ) 2 ]
[0072] Where: f(x,y) represents the gray value of the bandpass filtered data volume D1 at a certain pixel (x,y); μ represents the average gray value of f.
[0073] μ=(1 / MN)*∑f(x,y)
[0074] Where M and N are the width and height of the image, respectively; Σ represents the summation of all pixels in the image.
[0075] Gray-scale variance is used to evaluate image sharpness by taking the sum of squares of the differences between the gray-scale value of each pixel and the average gray-scale value.
[0076] When the image reaches optimal focus, the high-frequency signal components are most abundant, resulting in the most dramatic gray-level changes, at which point the gray-level variance reaches its maximum value. By comparing the gray-level variance values at different focus positions, the optimal combination of focus parameters that maximizes the gray-level variance is determined, thereby obtaining the high-frequency focus data volume D2, such as... Figure 7 As shown in the figure, this processing procedure establishes a quantitative correspondence between gray-scale statistical characteristics and signal focusing quality, providing an objective evaluation standard for high-frequency enhancement of seismic data.
[0077] S4. Analyze the imaging effect of the focused data volume D2 and evaluate the effect in combination with the known seismic response characteristics of the volcanic conduit; if the effect is not as expected, return to step S3 to improve the imaging quality by optimizing the target frequency selection until an ideal seismic imaging data volume is obtained.
[0078] S5. Extract the post-stack seismic attribute volume from the seismic imaging data volume, normalize the values of each attribute volume to the [0,1] interval, and obtain the post-stack seismic attribute data volume E;
[0079] like Figure 8 As shown, the values of the post-stack coherence attribute body E1, the post-stack variance attribute body E2, and the post-stack gradient magnitude attribute body E3 are obtained respectively, and then normalized to the [0,1] interval to eliminate the dimensional differences between different attributes and ensure the fairness of the fusion analysis.
[0080] S6. Overlay the post-stack seismic attribute data volume E of all seismic target samples, and generate the comprehensive feature data volume F of volcanic conduit target samples through a weighted summation method. This highlights the common response characteristics of the conduit, suppresses the ambiguity of single attributes, enhances the difference characteristics between the volcanic conduit and the surrounding rock, and provides an optimized data foundation for subsequent automatic identification.
[0081] This invention constructs a scientific and efficient method for identifying volcanic conduits by accurately extracting and specifically enhancing the dominant frequency band features of the conduits, and by combining weighted fusion analysis of multi-dimensional seismic attributes such as coherence volume, variance volume, and gradient modulus. This method overcomes the limitations of traditional single-attribute analysis, transforming volcanic conduit enhancement processing from an empirical operation to an intelligent process based on multi-parameter quantitative analysis. It not only avoids the blindness of traditional methods but also enables real-time optimization of processing results, ultimately achieving a significant improvement in the clarity of volcanic conduit boundaries and a marked enhancement in the reliability of geological interpretation.
[0082] Example 2:
[0083] This embodiment provides a volcanic passage identification system based on high-frequency focusing imaging, including:
[0084] The frequency upscaling module acquires seismic attribute data volume A of the target study area, performs frequency upscaling processing on the seismic attribute data volume A to improve seismic resolution and broaden the effective frequency band, and obtains frequency upscaling data volume B.
[0085] The enhancement module performs volcano channel enhancement processing on the frequency-upgraded data volume B to obtain the enhanced data volume C;
[0086] The focusing module acquires seismic target samples of the actual drilled volcanic conduit and performs spectral analysis to determine the dominant frequency band range of the volcanic conduit response. Based on target frequency focusing imaging technology, the target frequency components within the dominant frequency band range are extracted from the enhanced data volume C for signal enhancement, and the focused signal is used to reconstruct the target area image to obtain the focused data volume D2.
[0087] The imaging module analyzes the imaging effect of the focused data volume D2 and evaluates the effect in combination with the known seismic response characteristics of the volcanic conduit. If the effect is not as expected, the imaging quality is improved by optimizing the target frequency selection until an ideal seismic imaging data volume is obtained.
[0088] The attribute module extracts the post-stack seismic attribute volume from the seismic imaging data volume, normalizes the values of each attribute volume to the [0,1] interval, and obtains the post-stack seismic attribute data volume E.
[0089] The integration module performs overlay operations on the post-stack seismic attribute data volume E of all seismic target samples, and generates the integrated feature data volume F of the volcanic conduit target samples through a weighted summation method.
[0090] Example 3:
[0091] An electronic device includes a memory, a processor, and a computer program stored in the memory and running thereon. When the processor executes the program, it implements the aforementioned method for identifying volcanic passages based on high-frequency focusing imaging, comprising:
[0092] Seismic attribute data volume A of the target study area is obtained, and the seismic attribute data volume A is subjected to frequency up-up processing to improve seismic resolution and broaden the effective frequency band, thereby obtaining frequency up-up data volume B.
[0093] Volcano channel enhancement processing is applied to frequency-upgraded data volume B to obtain enhanced data volume C;
[0094] Seismic target samples of the actual drilled volcanic conduit were obtained and spectral analysis was performed to determine the dominant frequency band range of the volcanic conduit response. Based on target frequency focusing imaging technology, target frequency components within the dominant frequency band range were extracted from the enhanced data volume C for signal enhancement. The focused signal was then used to reconstruct the target area image to obtain the focused data volume D2.
[0095] The imaging effect of the focused data volume D2 is analyzed, and the effect is evaluated in combination with the known seismic response characteristics of the volcanic conduit. If the effect is not as expected, the imaging quality is improved by optimizing the target frequency selection until an ideal seismic imaging data volume is obtained.
[0096] The post-stack seismic attribute volume is extracted from the seismic imaging data volume, and the values of each attribute volume are normalized to the [0,1] interval to obtain the post-stack seismic attribute data volume E.
[0097] The post-stack seismic attribute data volume E of all earthquake target samples is overlaid, and the comprehensive feature data volume F of the volcanic conduit target samples is generated by weighted summation.
[0098] Example 4:
[0099] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for identifying volcanic passages based on high-frequency focusing imaging, comprising:
[0100] Seismic attribute data volume A of the target study area is obtained, and the seismic attribute data volume A is subjected to frequency up-up processing to improve seismic resolution and broaden the effective frequency band, thereby obtaining frequency up-up data volume B.
[0101] Volcano channel enhancement processing is applied to frequency-upgraded data volume B to obtain enhanced data volume C;
[0102] Seismic target samples of the actual drilled volcanic conduit were obtained and spectral analysis was performed to determine the dominant frequency band range of the volcanic conduit response. Based on target frequency focusing imaging technology, target frequency components within the dominant frequency band range were extracted from the enhanced data volume C for signal enhancement. The focused signal was then used to reconstruct the target area image to obtain the focused data volume D2.
[0103] The imaging effect of the focused data volume D2 is analyzed, and the effect is evaluated in combination with the known seismic response characteristics of the volcanic conduit. If the effect is not as expected, the imaging quality is improved by optimizing the target frequency selection until an ideal seismic imaging data volume is obtained.
[0104] The post-stack seismic attribute volume is extracted from the seismic imaging data volume, and the values of each attribute volume are normalized to the [0,1] interval to obtain the post-stack seismic attribute data volume E.
[0105] The post-stack seismic attribute data volume E of all earthquake target samples is overlaid, and the comprehensive feature data volume F of the volcanic conduit target samples is generated by weighted summation.
[0106] Those skilled in the art will understand that the modules or steps described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, which can then be stored in a storage device for execution by a computer device. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. This disclosure is not limited to any particular combination of hardware and software.
[0107] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
[0108] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.
Claims
1. A method for identifying volcanic passages based on high-frequency focusing imaging, characterized in that, Includes the following steps: Seismic attribute data volume A of the target study area is obtained, and the seismic attribute data volume A is subjected to frequency up-up processing to improve seismic resolution and broaden the effective frequency band, thereby obtaining frequency up-up data volume B. Volcano channel enhancement processing is applied to frequency-upgraded data volume B to obtain enhanced data volume C; Seismic target samples of the actual drilled volcanic conduit were obtained and spectral analysis was performed to determine the dominant frequency band range of the volcanic conduit response; Based on target frequency focusing imaging technology, target frequency components within the dominant frequency band are extracted from the enhanced data volume C for signal enhancement, and the image of the target region is reconstructed using the focused signal to obtain the focused data volume D2. The imaging effect of the focused data volume D2 is analyzed, and the effect is evaluated in combination with the known seismic response characteristics of the volcanic conduit. If the effect is not as expected, the imaging quality is improved by optimizing the target frequency selection until an ideal seismic imaging data volume is obtained. The post-stack seismic attribute volume is extracted from the seismic imaging data volume, and the values of each attribute volume are normalized to the [0,1] interval to obtain the post-stack seismic attribute data volume E. The post-stack seismic attribute data volume E of all earthquake target samples is overlaid, and the comprehensive feature data volume F of the volcanic conduit target samples is generated by weighted summation.
2. The volcanic channel identification method based on high-frequency focusing imaging according to claim 1, characterized in that, For the seismic attribute data volume A of the target study area, the vertical data range of the target layer is extracted, and wavelet transform technology is used to perform frequency enhancement processing.
3. The volcanic channel identification method based on high-frequency focusing imaging according to claim 1, characterized in that, When performing volcanic conduit enhancement processing, a fault mean filtering algorithm is used to smooth the data and suppress noise to obtain the enhanced data volume C.
4. The volcanic channel identification method based on high-frequency focusing imaging according to claim 1, characterized in that, Based on seismic target samples from actual drilled volcanic conduits, typical conduit target areas were manually delineated on the profile, and time-frequency joint analysis was conducted on the target areas to determine the dominant frequency band range of the volcanic conduit response.
5. A method for identifying volcanic passages based on high-frequency focusing imaging according to claim 1 or 4, characterized in that, The Butterworth bandpass filter removes interference signals unrelated to the main frequency, retaining only signals within the dominant frequency band. Specifically: Obtain the center frequency Fc = (f1 + f2) / 2; where f1 and f2 are the lower and upper cutoff frequencies of the bandpass filter, respectively. Obtain the bandwidth BW = f2 - f1, which represents the frequency range that the bandpass filter allows to pass through; By setting the lower limit frequency, upper limit frequency, and bandwidth of the bandpass filter, the target frequency data is saved, resulting in the bandpass filter data volume D1.
6. The volcanic channel identification method based on high-frequency focusing imaging according to claim 5, characterized in that, The amplitude values of the bandpass filtered data volume D1 are mapped to the grayscale domain and converted into grayscale value domain. The grayscale variance is used as the basis for evaluating the degree of focus. Gray-scale variance is used to evaluate image sharpness by taking the sum of squares of the differences between the gray-scale value of each pixel and the average gray-scale value. When the image reaches the optimal focus state, the gray level changes most drastically due to the richest high-frequency signal components. At this time, the gray level variance reaches its maximum value. By comparing the gray level variance values at different focus positions, the optimal combination of focus parameters that makes the gray level variance reach its extreme value is determined, thereby obtaining the high-frequency focus data volume D2.
7. The volcanic channel identification method based on high-frequency focusing imaging according to claim 1, characterized in that, The post-stack seismic attribute volume E includes the post-stack coherence attribute volume E1, the post-stack variance attribute volume E2, and the post-stack gradient mode attribute volume E3.
8. A volcanic passage identification system based on high-frequency focusing imaging, characterized in that, include: The frequency upscaling module acquires seismic attribute data volume A of the target study area, performs frequency upscaling processing on the seismic attribute data volume A to improve seismic resolution and broaden the effective frequency band, and obtains frequency upscaling data volume B. The enhancement module performs volcano channel enhancement processing on the frequency-upgraded data volume B to obtain the enhanced data volume C; The focusing module acquires seismic target samples of the actual drilled volcanic conduit and performs spectral analysis to determine the dominant frequency band range of the volcanic conduit response; Based on target frequency focusing imaging technology, target frequency components within the dominant frequency band are extracted from the enhanced data volume C for signal enhancement, and the image of the target region is reconstructed using the focused signal to obtain the focused data volume D2. The imaging module analyzes the imaging effect of the focused data volume D2 and evaluates the effect in combination with the known seismic response characteristics of the volcanic conduit. If the effect is not as expected, the imaging quality is improved by optimizing the target frequency selection until an ideal seismic imaging data volume is obtained. The attribute module extracts the post-stack seismic attribute volume from the seismic imaging data volume, normalizes the values of each attribute volume to the [0,1] interval, and obtains the post-stack seismic attribute data volume E. The integration module performs overlay operations on the post-stack seismic attribute data volume E of all seismic target samples, and generates the integrated feature data volume F of the volcanic conduit target samples through a weighted summation method.
9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running thereon, characterized in that, When the processor executes the program, it implements the volcanic channel identification method based on high-frequency focusing imaging as described in any one of claims 1-7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a volcanic channel identification method based on high-frequency focusing imaging as described in any one of claims 1-7.