Geological fault data processing method and device, computer equipment and storage medium

Abstract

The invention provides a geological fault data processing method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining survey line data which comprises main fault data and pseudo fault data; extracting the main fault data from the survey line data by using a preset parameter range based on a preset main fault identification extraction algorithm to obtain first main fault data; eliminating the pseudo fault data from the measuring line data by using a preset elimination algorithm to obtain second main fault data; and merging the first main fault data and the second main fault data to obtain main fault merged data. Based on a preset main fault identification extraction algorithm and a preset elimination algorithm, identification of main fault data is completed, pseudo fault data in the data is eliminated, and the resolution and precision of the fault data are enhanced, so that the precision of geological fault data and subsequent speed modeling and imaging processing is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic data processing, and particularly relates to a method, device, computer device, and storage medium for processing geological fault data. Background Art

[0002] Geological fault data is important data in the velocity modeling and structural interpretation processes. Currently, the fault data formed by most interpretation software often appears too fragmented when the signal-to-noise ratio of seismic data is low, ultimately resulting in inaccurate fault descriptions. The corresponding method for improving the accuracy of fault data is to perform more detailed-scale interpretation based on the imaging profile, which not only consumes a large amount of manpower and material resources but also cannot guarantee that the final result will necessarily meet the production requirements. The post-stack data generated by current interpretation software only contains the data volume of the fault structure, stripping the data in the area outside the fault structure, resulting in low accuracy of the fault data. Summary of the Invention

[0003] Based on this, it is necessary to provide a method, device, computer device, and storage medium for processing geological fault data in view of the above technical problems.

[0004] A method for processing geological fault data includes:

[0005] Obtain survey line data, where the survey line data includes main fault data and pseudo-fault data;

[0006] Using a preset parameter range, based on a preset main fault identification and extraction algorithm, extract the main fault data from the survey line data to obtain first main fault data;

[0007] Using a preset rejection algorithm, remove the pseudo-fault data from the survey line data to obtain second main fault data;

[0008] Merge the first main fault data and the second main fault data to obtain main fault merged data.

[0009] In one embodiment, the step of using a preset parameter range, based on a preset main fault identification and extraction algorithm, to extract the main fault data from the survey line data to obtain first main fault data includes:

[0010] Using a preset waveform amplitude range, extract the main fault data within the preset waveform amplitude range from the survey line data to obtain first main fault data.

[0011] In one embodiment, the step of using a preset parameter range, based on a preset main fault identification and extraction algorithm, to extract the main fault data from the survey line data to obtain first main fault data includes:

[0012] Calculate the lengths of the respective survey line data;

[0013] Using a preset fault data length range, according to the lengths of the respective survey line data, extract the main fault data within the preset fault data length range from the survey line data to obtain first main fault data.

[0014] In one embodiment, the step of using a preset parameter range and based on a preset main fault identification and extraction algorithm to extract the main fault data from the survey line data to obtain first main fault data includes:

[0015] Calculate the fault azimuth angle fields of the respective survey line data;

[0016] Using a preset main azimuth angle range, according to the fault azimuth angle fields of the respective survey line data, extract the main fault data within the preset main azimuth angle range from the survey line data to obtain first main fault data.

[0017] In one embodiment, the step of using a preset parameter range and based on a preset main fault identification and extraction algorithm to extract the main fault data from the survey line data to obtain first main fault data includes:

[0018] Divide the survey line data into multiple fault data in the time direction;

[0019] Using a preset parameter range and based on a preset main fault identification and extraction algorithm, respectively extract the main fault data from each of the fault data to obtain first main fault data.

[0020] In one embodiment, the step of using a preset rejection algorithm to remove the pseudo-fault data from the survey line data to obtain second main fault data includes:

[0021] Divide the survey line data into fault data for multiple channels by channel;

[0022] Using a preset rejection algorithm, remove the pseudo-fault data from the fault data for each channel to obtain second main fault data.

[0023] In one embodiment, after the step of merging the first main fault data and the second main fault data to obtain main fault merged data, further included is:

[0024] Perform dip angle field processing on the main fault merged data to obtain main fault dip angle data.

[0025] A geological fault data processing device, comprising:

[0026] A survey line data acquisition module, configured to acquire survey line data, wherein the survey line data includes main fault data and pseudo-fault data;

[0027] A first main fault acquisition module is used to extract the main fault data from the survey line data by using a preset parameter range and based on a preset main fault identification and extraction algorithm to obtain first main fault data;

[0028] A second main fault acquisition module is used to remove the pseudo fault data from the survey line data by using a preset removal algorithm to obtain second main fault data;

[0029] A merging module is used to merge the first main fault data and the second main fault data to obtain main fault merged data.

[0030] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the following steps when executing the computer program:

[0031] Acquiring survey line data, wherein the survey line data includes main fault data and pseudo fault data;

[0032] Using a preset parameter range and based on a preset main fault identification and extraction algorithm, extract the main fault data from the survey line data to obtain first main fault data;

[0033] Using a preset elimination algorithm, the pseudo fault data is eliminated from the survey line data to obtain second main fault data;

[0034] The first main fault data and the second main fault data are combined to obtain main fault combined data.

[0035] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0036] Acquiring survey line data, wherein the survey line data includes main fault data and pseudo fault data;

[0037] Using a preset parameter range and based on a preset main fault identification and extraction algorithm, extract the main fault data from the survey line data to obtain first main fault data;

[0038] Using a preset elimination algorithm, the pseudo fault data is eliminated from the survey line data to obtain second main fault data;

[0039] The first main fault data and the second main fault data are combined to obtain main fault combined data.

[0040] The above geological fault data processing method, device, computer equipment and storage medium, based on a preset main fault identification and extraction algorithm and a preset elimination algorithm, complete the identification of main fault data, eliminate the pseudo-fault data in the data, enhance the resolution and accuracy of the fault data, and thus improve the accuracy of geological fault data and subsequent velocity modeling and imaging processing. Brief Description of the Drawings

[0041] Figure 1 It is a schematic flowchart of the geological fault data processing method in an embodiment;

[0042] Figure 2 It is a structural block diagram of the geological fault data processing device in an embodiment;

[0043] Figure 3 It is an internal structure diagram of the computer equipment in an embodiment;

[0044] Figure 4 It is a flowchart of the main fault identification technology in an embodiment;

[0045] Figure 5 It is a schematic flowchart of the multi-threaded parallel processing of geological fault data in an embodiment;

[0046] Figure 6 It is a schematic diagram of the interface for real-time quality control of fault processing in an embodiment;

[0047] Figure 7 It is a schematic diagram of the interface for the fault data interaction and editing technology in an embodiment. Detailed Embodiments

[0048] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] Embodiment 1

[0050] In this embodiment, as Figure 1 shown, a geological fault data processing method is provided, which includes:

[0051] Step 110, obtaining survey line data, where the survey line data includes main fault data and pseudo-fault data.

[0052] In this embodiment, the survey line data can also be referred to as the main line data, including main fault data and pseudo-fault data. The main fault data and pseudo-fault data are important terms in geology for describing faults and fractures on the earth's crust.

[0053] Main fault data mainly describes large-scale and large-scale faults and fractures on the earth's crust. These faults and fractures are usually the result of long-term geological actions (such as plate movement, seismic activity, etc.), and they have an important impact on the earth's geological structure, the earth's magnetic field, and crustal movement. Main fault data can help us understand the formation and evolution of the earth's crust, and can also be used to predict the possibility of future geological disasters (such as earthquakes, volcanic eruptions, etc.). Pseudo-fault data mainly describes small-scale and small-scale faults and fractures. These faults and fractures are usually caused by local geological actions (such as groundwater flow, rock pressure changes, etc.), and their impact on crustal movement and geological structure is relatively small. Pseudo-fault data can help us understand the geological structure and geological processes inside the earth's crust, and can also be used to guide mineral resource exploration and geological engineering design.

[0054] Therefore, main fault data and pseudo-fault data are important data types for describing faults and fractures in the earth's crust. They have different characteristics and impact ranges, and are of great significance for understanding the formation and evolution of the earth's crust and predicting geological disasters.

[0055] Step 120: extract the main fault data from the survey line data using a preset parameter range and based on a preset main fault identification and extraction algorithm to obtain first main fault data.

[0056] In this embodiment, by setting limits on the waveform peak range of each survey line, obtaining and setting limits on the fault length, obtaining the fault azimuth field and setting main azimuth limits, etc., pseudo-fault data in the data are eliminated, the main fault data is extracted, and the first main fault data is obtained.

[0057] It is worth mentioning that the preset parameter range is adjustable. Specifically, in this embodiment, the fault data of a single survey line before and after processing are simultaneously displayed through a graphic window. The processing personnel adjust the processing control parameters in real time according to the processing effect. The control parameters are the preset parameter ranges, and the data is picked up to remove and strengthen the area. Then, the processing effect is displayed through error evaluation and synchronous display technology, and a real-time quality control analysis algorithm is provided. In this way, by adjusting the preset parameter range, the accuracy of the first main fault data can be effectively improved.

[0058] Step 130: using a preset elimination algorithm, eliminate the pseudo fault data from the survey line data to obtain second main fault data.

[0059] It should be understood that the fault data body formed in the tectonic area often contains some broken pseudo-fault data, which makes it difficult to identify the real fault. In this embodiment, based on the preset main fault identification and extraction algorithm and the preset elimination algorithm, the main fault data is identified, and the pseudo-fault data in the data is eliminated, thereby enhancing the resolution and accuracy of the fault data.

[0060] Step 140: Merge the first main fault data and the second main fault data to obtain main fault merged data.

[0061] In this embodiment, merging the first main fault data and the second main fault data can obtain more accurate main fault data.

[0062] In the above embodiment, based on the preset main fault identification and extraction algorithm and the preset rejection algorithm, the identification of the main fault data is completed, and the pseudo-fault data in the data is rejected, enhancing the resolution and accuracy of the fault data, thereby improving the accuracy of the geological fault data and subsequent velocity modeling and imaging processing.

[0063] In one embodiment, the step of extracting the main fault data from the survey line data based on the preset main fault identification and extraction algorithm using the preset parameter range to obtain the first main fault data includes:

[0064] Extract the main fault data within the preset waveform amplitude range from the survey line data to obtain the first main fault data.

[0065] In this embodiment, the preset waveform amplitude range is the waveform peak range. The waveform peak range of each survey line is restricted using the preset waveform amplitude range, and the main fault data within the preset waveform amplitude range is selected to obtain the first main fault data.

[0066] In one embodiment, the step of extracting the main fault data from the survey line data based on the preset main fault identification and extraction algorithm using the preset parameter range to obtain the first main fault data includes:

[0067] Calculate the lengths of the respective survey line data;

[0068] Using the preset fault data length range, according to the lengths of the respective survey line data, extract the main fault data within the preset fault data length range from the survey line data to obtain the first main fault data.

[0069] In this embodiment, the sum of each fault data is calculated to obtain the fault length. The length of each survey line is restricted using the preset fault data length range, and the main fault data within the preset fault data length range is selected to obtain the first fault data.

[0070] In one embodiment, the step of extracting the main fault data from the survey line data based on the preset main fault identification and extraction algorithm using the preset parameter range to obtain the first main fault data includes:

[0071] Calculate the fault azimuth fields of the respective survey line data;

[0072] Using a preset main azimuth range, according to the fault azimuth fields of the respective survey line data, extract the main fault data within the preset main azimuth range from the survey line data to obtain first main fault data.

[0073] In this embodiment, calculate the azimuth fields of the respective faults, and using a preset main azimuth range, extract the main fault data within the preset main azimuth range from the survey line data to obtain first main fault data.

[0074] In one embodiment, the step of using a preset parameter range and based on a preset main fault identification and extraction algorithm to extract the main fault data from the survey line data to obtain first main fault data includes:

[0075] Divide the survey line data into multiple fault data in the time direction;

[0076] Using a preset parameter range and based on a preset main fault identification and extraction algorithm, extract the main fault data from each of the fault data to obtain first main fault data.

[0077] In this embodiment, use one of the processing threads of the processor to divide the survey line data into multiple fault data in the time direction, and divide each of the divided fault data to different processing threads for calculation. Each processing thread is responsible for using a preset parameter range and based on a preset main fault identification and extraction algorithm to extract the main fault with the fault starting point in this time period, so as to obtain first main fault data.

[0078] In one embodiment, the step of using a preset rejection algorithm to reject the pseudo-fault data from the survey line data to obtain second main fault data includes:

[0079] Divide the survey line data into fault data of multiple channels by channel;

[0080] Using a preset rejection algorithm, reject the pseudo-fault data from the fault data of each channel to obtain second main fault data.

[0081] In this embodiment, use one of the processing threads of the processor to divide the survey line data into fault data of multiple channels by channel, and distribute the fault data to the corresponding processing threads by channel for calculation. Each processing thread uses a preset rejection algorithm to reject the pseudo-fault data from the fault data of each channel to obtain second main fault data, so as to obtain second main fault data.

[0082] In the above embodiment, such as Figure 5As shown, when extracting the main fault data, the basis for dividing the survey line data into multiple fault data is the time direction, while when removing the pseudo-fault data, the basis for dividing the survey line data into multiple fault data is the trace. Therefore, the fault data is divided from different dimensions, so as to form the first fault data and the second fault data extracted based on different dimensions. In this way, the merged main fault merged data is made more accurate.

[0083] In addition, in the above embodiment, the first main fault data and the second main fault data are respectively extracted by different processing threads. Among them, the thread for extracting the first main fault data divides the fault data into multiple computing threads in the time direction, and each computing thread is responsible for extracting the main fault with the fault starting point in this time period; the thread for extracting the first main fault data removes the pseudo-fault data by trace and distributes the traces to the corresponding thread for calculation; then the first main fault data and the second main fault data are merged by the main thread. In this way, by extracting the main fault data and removing the pseudo-fault data by sub-threads, the computing resources can be used more reasonably, and the computing efficiency can be effectively improved.

[0084] In one embodiment, after the step of merging the first main fault data and the second main fault data to obtain the main fault merged data, it further includes:

[0085] Performing dip field processing on the main fault merged data to obtain main fault dip data.

[0086] In this embodiment, the main fault dip data is also called the fault data dip field. The fault data dip field refers to a two-dimensional or three-dimensional spatial distribution composed of a series of fault data points, which can reflect the fault strike and dip changes. In geology, a fault is an important geological phenomenon, usually caused by factors such as crustal movement, geological structure, and volcanic activity. The fault data dip field can help us understand the distribution, strike, dip, etc. of faults, so as to infer the crustal movement mode and crustal structure.

[0087] Through the fault data dip field, the direction and dip of the fault plane can be calculated, and then the nature and activity characteristics of the fault can be analyzed. For example, the dip of a normal fault is usually greater than 45 degrees, while the dip of a reverse fault is less than 30 degrees. In addition, different-scale fracture systems can be identified through the fault data dip field, thus providing important information for geological research.

[0088] The fault data dip field is an important type of geological data, which can help understand the crustal structure and movement mode, and is of great significance for geological research and geological disaster prediction.

[0089] Embodiment Two

[0090] In this embodiment, a method for improving the processing accuracy of tomographic data is provided. The processing method includes: the main fault identification technology for improving tomographic accuracy, the interactive data modification and real-time monitoring technology of processing effects based on plane data display, and the tomographic data volume processing method based on multi-threading.

[0091] Main fault identification technology: In complex structural areas, the tomographic data volume formed by commercial interpretation software often contains some fragmented pseudo-fault data, which makes it difficult to identify real faults. In this embodiment, the following Figure 4 processing process is used to complete the main fault identification: techniques such as restricting the waveform peak range of each survey line, calculating and restricting the fault length, calculating the fault azimuth field, and restricting the main azimuth angle are used to eliminate the pseudo-fault data in the data, enhancing the resolution and accuracy of the tomographic data.

[0092] Real-time quality control technology for tomographic processing: As shown in Figure 6 and Figure 7 , the tomographic data of a single survey line before and after processing are simultaneously displayed through a graphic window. The processing personnel can adjust the processing control parameters in real time according to the processing effect, pick up the data elimination and enhancement areas, and then display the processing effect through error evaluation and synchronous display technology, providing a real-time quality control analysis algorithm.

[0093] Parallel processing technology for tomographic data volume: The algorithm involved in the present invention is implemented on a single multi-core PC machine. To meet the requirements of real-time monitoring, parallel computing is used for tomographic data processing. The parallelism includes the parallel technology in the real-time quality control process of tomographic processing and the parallel processing technology for generating the final tomographic data volume.

[0094] The real-time quality control of tomographic interactive processing uses multi-threading to process and display the data of a single side line as shown in Figure 5 .

[0095] Among them, the main fault extraction thread pool manages the main single-layer extraction threads. The tomographic data is divided into multiple calculation threads in the time direction, and each thread is responsible for extracting the main fault of the fault starting point in this time period; the pseudo-fault data is eliminated by survey line and distributed to the corresponding threads for calculation; then the data is merged through the main thread, and finally the dip angle calculation is completed through the dip angle field processing thread.

[0096] In this embodiment, the tomographic data finally formed through interactive and comparison processing technology is of great significance for subsequent velocity modeling and improving the accuracy and effect of data processing.

[0097] The specific implementation process is as follows:

[0098] The first step is to organize the tomographic data volume in segy format, and realize the access of in-memory data organized by survey line through the interface provided by the Segy file operation class.

[0099] In the second step, a multi-thread pool is used to process the read survey line data according to the default processing parameters.

[0100] The third step is to display the original data and processed data according to the two-dimensional plane data display.

[0101] In the fourth step, the final processing parameters are determined by adjusting the main fault extraction, amplitude range control parameters and interactive editing.

[0102] The fifth step is to output the processed fault data or superposition data according to the processing parameters to complete the processing.

[0103] In this embodiment, digital signal processing and interactive processing are used for the formed fault data body, and algorithms such as main fault extraction, main fault waveform extraction, and dip wave field evaluation are introduced to eliminate pseudo-fault data contained in the fault data. Interactive editing and real-time quality control technology are used to realize real-time evaluation of processing effects. After processing, processed high-precision fault data or superposition data are formed according to business needs, thereby improving the effects of velocity modeling and data processing.

[0104] This application uses digital signal processing and interactive processing methods to remove pseudo-fault data contained in fault data bodies, thereby improving the accuracy of geological fault data and subsequent velocity modeling and imaging processing, and develops a group of interactive and batch processing algorithms based on fault data bodies to achieve what you see is what you get processing effects. Seismic data processing personnel can monitor the processing effects in real time by interactively setting processing parameters, thereby improving the effect of data processing in complex structural areas.

[0105] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0106] Embodiment 3

[0107] In this embodiment, Figure 2 As shown, a geological fault data processing device is provided, comprising:

[0108] The survey line data acquisition module 210 is used to acquire survey line data, wherein the survey line data includes main fault data and pseudo fault data.

[0109] In this embodiment, the survey line data may also be referred to as main line data, including main fault data and pseudo fault data. Main fault data and pseudo fault data are important terms used in geology to describe faults and fractures on the earth's crust.

[0110] Main fault data mainly describes large-scale and large-scale faults and fractures on the earth's crust. These faults and fractures are usually the result of long-term geological actions (such as plate movement, seismic activity, etc.), and they have an important impact on the earth's geological structure, the earth's magnetic field, and crustal movement. Main fault data can help us understand the formation and evolution of the earth's crust, and can also be used to predict the possibility of future geological disasters (such as earthquakes, volcanic eruptions, etc.). Pseudo-fault data mainly describes small-scale and small-scale faults and fractures. These faults and fractures are usually caused by local geological actions (such as groundwater flow, rock pressure changes, etc.), and their impact on crustal movement and geological structure is relatively small. Pseudo-fault data can help us understand the geological structure and geological processes inside the earth's crust, and can also be used to guide mineral resource exploration and geological engineering design.

[0111] Therefore, main fault data and pseudo-fault data are important data types for describing faults and fractures in the earth's crust. They have different characteristics and impact ranges, and are of great significance for understanding the formation and evolution of the earth's crust and predicting geological disasters.

[0112] The first main fault acquisition module 220 is used to extract the main fault data from the survey line data by using a preset parameter range and based on a preset main fault identification and extraction algorithm to obtain first main fault data.

[0113] In this embodiment, by setting limits on the waveform peak range of each survey line, obtaining and setting limits on the fault length, obtaining the fault azimuth field and setting main azimuth limits, etc., pseudo-fault data in the data are eliminated, the main fault data is extracted, and the first main fault data is obtained.

[0114] It is worth mentioning that the preset parameter range is adjustable. Specifically, in this embodiment, the fault data of a single survey line before and after processing are simultaneously displayed through a graphic window. The processing personnel adjust the processing control parameters in real time according to the processing effect. The control parameters are the preset parameter ranges, and the data is picked up to remove and strengthen the area. Then, the processing effect is displayed through error evaluation and synchronous display technology, and a real-time quality control analysis algorithm is provided. In this way, by adjusting the preset parameter range, the accuracy of the first main fault data can be effectively improved.

[0115] The second main fault acquisition module 230 is used to eliminate the pseudo fault data from the survey line data by using a preset elimination algorithm to obtain second main fault data.

[0116] It should be understood that the fault data volume formed in the tectonic area often contains some fragmented pseudo-fault data, which makes it difficult to identify the true faults. In this embodiment, based on the preset main fault identification and extraction algorithm and the preset rejection algorithm, the identification of the main fault data is completed, and the pseudo-fault data in the data is removed, enhancing the resolution and accuracy of the fault data.

[0117] The merging module 240 is used to merge the first main fault data and the second main fault data to obtain the main fault merged data.

[0118] In this embodiment, merging the first main fault data and the second main fault data can obtain more accurate main fault data.

[0119] In the above embodiment, based on the preset main fault identification and extraction algorithm and the preset rejection algorithm, the identification of the main fault data is completed, and the pseudo-fault data in the data is removed, enhancing the resolution and accuracy of the fault data, thereby improving the accuracy of geological fault data and subsequent velocity modeling and imaging processing.

[0120] In one embodiment, the first main fault acquisition module is further configured to extract the main fault data within the preset waveform amplitude range from the survey line data by using the preset waveform amplitude range, to obtain the first main fault data.

[0121] In this embodiment, the preset waveform amplitude range is the waveform peak range. By using the preset waveform amplitude range to limit the waveform peak range of each survey line, the main fault data within the preset waveform amplitude range is selected to obtain the first main fault data.

[0122] In one embodiment, the first main fault acquisition module is further configured to calculate the length of each survey line data; and use the preset fault data length range to extract the main fault data within the preset fault data length range from the survey line data according to the length of each survey line data, to obtain the first main fault data.

[0123] In this embodiment, the sum of each fault data is calculated to obtain the fault length. By using the preset fault data length range to limit the length of each survey line, the main fault data within the preset fault data length range is selected to obtain the first fault data.

[0124] In one embodiment, the first main fault acquisition module is further configured to calculate the fault azimuth angle field of each survey line data; and use the preset main azimuth angle range to extract the main fault data within the preset main azimuth angle range from the survey line data according to the fault azimuth angle field of each survey line data, to obtain the first main fault data.

[0125] In this embodiment, an azimuth angle field of each fault is calculated, and using a preset main azimuth angle range, the main fault data within the preset main azimuth angle range is extracted from the survey line data to obtain first main fault data.

[0126] In one embodiment, the first main fault acquisition module includes:

[0127] A time direction division unit, configured to divide the survey line data into multiple fault data according to the time direction;

[0128] A first main fault acquisition unit, configured to use a preset parameter range and based on a preset main fault identification and extraction algorithm, extract the main fault data from each of the fault data to obtain first main fault data.

[0129] In this embodiment, one of the processing threads of the processor divides the survey line data into multiple fault data according to the time direction, and each of the divided fault data is respectively assigned to different processing threads for calculation. Each processing thread is responsible for using a preset parameter range and based on a preset main fault identification and extraction algorithm to extract the main fault whose starting point is in this time period, so as to obtain first main fault data.

[0130] In one embodiment, the second main fault acquisition module includes:

[0131] A trace division unit, configured to divide the survey line data into fault data of multiple traces according to traces;

[0132] A second main fault data acquisition unit, configured to use a preset rejection algorithm to reject the pseudo-fault data from the fault data of each trace to obtain second main fault data.

[0133] In this embodiment, one of the processing threads of the processor divides the survey line data into fault data of multiple traces according to traces, and distributes the fault data to the corresponding processing threads for calculation according to traces. Each processing thread uses a preset rejection algorithm to reject the pseudo-fault data from the fault data of each trace to obtain second main fault data, so as to obtain second main fault data.

[0134] In one embodiment, the device further includes:

[0135] A main fault dip angle data acquisition module, configured to perform dip angle field processing on the main fault merged data to obtain main fault dip angle data.

[0136] In this embodiment, the dip data of the main fault, also known as the dip field of fault data, refers to the two-dimensional or three-dimensional spatial distribution composed of a series of fault data points, which can reflect the strike and dip changes of the fault. In geology, a fault is an important geological phenomenon, usually caused by factors such as crustal movement, geological structure, and volcanic activity. The dip field of fault data can help us understand information such as the distribution, strike, and dip of faults, thereby inferring the movement mode of the crust and the crustal structure.

[0137] Through the dip field of fault data, the direction and dip of the fault plane can be calculated, and then the nature and activity characteristics of the fault can be analyzed. For example, the dip of a normal fault is usually greater than 45 degrees, while the dip of a reverse fault is less than 30 degrees. In addition, different-scale fracture systems can also be identified through the dip field of fault data, providing important information for geological research.

[0138] The dip field of fault data is an important type of geological data, which can help understand the crustal structure and movement mode, and is of great significance for geological research and geological disaster prediction.

[0139] For the specific limitations of the geological fault data processing device, reference can be made to the limitations on the geological fault data processing method in the above text, which will not be elaborated here. Each unit in the above geological fault data processing device can be implemented in whole or in part by software, hardware, and their combination. The above units can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above units.

[0140] Embodiment 4

[0141] In this embodiment, a computer device is provided. Its internal structure diagram can be as Figure 3As shown in the figure. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program, and a database is deployed on the non-volatile storage medium. The database is used to store survey line data, main fault data, and pseudo-fault data. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with other computer devices on which application software is deployed. When the computer program is executed by the processor, it implements a method for processing geological fault data. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0142] Those skilled in the art can understand that Figure 3 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0143] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the following steps are implemented:

[0144] Step 110, obtain survey line data, where the survey line data includes main fault data and pseudo-fault data.

[0145] In this embodiment, the survey line data can also be called the main line data, including main fault data and pseudo-fault data. The main fault data and the pseudo-fault data are important terms in geology used to describe faults and fractures on the earth's crust.

[0146] Main fault data mainly describes large-scale and large-scale faults and fractures on the earth's crust. These faults and fractures are usually the result of long-term geological actions (such as plate movement, seismic activity, etc.), and they have an important impact on the earth's geological structure, the earth's magnetic field, and crustal movement. Main fault data can help us understand the formation and evolution of the earth's crust, and can also be used to predict the possibility of future geological disasters (such as earthquakes, volcanic eruptions, etc.). Pseudo-fault data mainly describes small-scale and small-scale faults and fractures. These faults and fractures are usually caused by local geological actions (such as groundwater flow, rock pressure changes, etc.), and their impact on crustal movement and geological structure is relatively small. Pseudo-fault data can help us understand the geological structure and geological processes inside the earth's crust, and can also be used to guide mineral resource exploration and geological engineering design.

[0147] Therefore, main fault data and pseudo-fault data are important data types for describing faults and fractures in the earth's crust. They have different characteristics and impact ranges, and are of great significance for understanding the formation and evolution of the earth's crust and predicting geological disasters.

[0148] Step 120: extract the main fault data from the survey line data using a preset parameter range and based on a preset main fault identification and extraction algorithm to obtain first main fault data.

[0149] In this embodiment, by setting limits on the waveform peak range of each survey line, obtaining and setting limits on the fault length, obtaining the fault azimuth field and setting main azimuth limits, etc., pseudo-fault data in the data are eliminated, the main fault data is extracted, and the first main fault data is obtained.

[0150] It is worth mentioning that the preset parameter range is adjustable. Specifically, in this embodiment, the fault data of a single survey line before and after processing are simultaneously displayed through a graphic window. The processing personnel adjust the processing control parameters in real time according to the processing effect. The control parameters are the preset parameter ranges, and the data is picked up to remove and strengthen the area. Then, the processing effect is displayed through error evaluation and synchronous display technology, and a real-time quality control analysis algorithm is provided. In this way, by adjusting the preset parameter range, the accuracy of the first main fault data can be effectively improved.

[0151] Step 130: using a preset elimination algorithm, eliminate the pseudo fault data from the survey line data to obtain second main fault data.

[0152] It should be understood that the fault data body formed in the tectonic area often contains some broken pseudo-fault data, which makes it difficult to identify the real fault. In this embodiment, based on the preset main fault identification and extraction algorithm and the preset elimination algorithm, the main fault data is identified, and the pseudo-fault data in the data is eliminated, thereby enhancing the resolution and accuracy of the fault data.

[0153] Step 140: Merge the first primary fault data and the second primary fault data to obtain the merged primary fault data.

[0154] In this embodiment, merging the first primary fault data and the second primary fault data can obtain more accurate primary fault data.

[0155] In the above embodiment, based on the preset primary fault identification and extraction algorithm and the preset rejection algorithm, the identification of the primary fault data is completed, and the pseudo-fault data in the data is rejected, enhancing the resolution and accuracy of the fault data, thereby improving the accuracy of the geological fault data and subsequent velocity modeling and imaging processing.

[0156] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0157] Extract the primary fault data within the preset waveform amplitude range from the survey line data to obtain the first primary fault data.

[0158] In this embodiment, the preset waveform amplitude range is the waveform peak range. The waveform peak range of each survey line is restricted by using the preset waveform amplitude range, and the primary fault data within the preset waveform amplitude range is selected to obtain the first primary fault data.

[0159] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0160] Calculate the lengths of the respective survey line data;

[0161] Extract the primary fault data within the preset fault data length range from the survey line data according to the lengths of the respective survey line data by using the preset fault data length range to obtain the first primary fault data.

[0162] In this embodiment, the sum of each fault data is calculated to obtain the fault length. The length of each survey line is restricted by using the preset fault data length range, and the primary fault data within the preset fault data length range is selected to obtain the first fault data.

[0163] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0164] Calculate the fault azimuth fields of the respective survey line data;

[0165] Extract the primary fault data within the preset primary azimuth range from the survey line data according to the fault azimuth fields of the respective survey line data by using the preset primary azimuth range to obtain the first primary fault data.

[0166] In this embodiment, the azimuth angle fields of each fault are calculated, and using a preset main azimuth angle range, the main fault data within the preset main azimuth angle range is extracted from the survey line data to obtain the first main fault data.

[0167] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0168] The survey line data is divided into multiple fault data in the time direction;

[0169] Using a preset parameter range, based on a preset main fault identification and extraction algorithm, the main fault data is respectively extracted from each of the fault data to obtain the first main fault data.

[0170] In this embodiment, one of the processing threads of the processor divides the survey line data into multiple fault data in the time direction, and each of the divided fault data is respectively assigned to different processing threads for calculation. Each processing thread is responsible for using a preset parameter range to extract the main fault at the fault starting point in this time period based on a preset main fault identification and extraction algorithm, thereby obtaining the first main fault data.

[0171] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0172] The survey line data is divided into fault data of multiple channels by channel;

[0173] Using a preset rejection algorithm, the pseudo-fault data is removed from the fault data of each channel to obtain the second main fault data.

[0174] In this embodiment, one of the processing threads of the processor divides the survey line data into fault data of multiple channels by channel, and the fault data is distributed to the corresponding processing threads by channel for calculation. Each processing thread uses a preset rejection algorithm to remove the pseudo-fault data from the fault data of each channel to obtain the second main fault data, thereby obtaining the second main fault data.

[0175] In the above embodiments, when extracting the main fault data, the basis for dividing the survey line data into multiple fault data is the time direction, while the basis for removing the pseudo-fault data is the channel when dividing the survey line data into multiple fault data. Therefore, the fault data is divided from different dimensions, so as to form the first fault data and the second fault data extracted based on different dimensions. In this way, the merged main fault merged data is made more accurate.

[0176] In addition, in the above embodiments, the first main fault data and the second main fault data are extracted by different processing threads. Among them, the thread for extracting the first main fault data divides the fault data into multiple computing threads in the time direction, and each computing thread is responsible for extracting the main fault with the fault starting point in that time period; the thread for extracting the first main fault data divides the channels according to the pseudo-fault data elimination by channel and calculates them in the corresponding threads; then the first main fault data and the second main fault data are merged through the main thread. In this way, the main fault data is extracted by sub-threads and the pseudo-fault data is eliminated, which can more reasonably utilize computing resources and effectively improve the computing efficiency.

[0177] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0178] Perform dip field processing on the merged main fault data to obtain main fault dip data.

[0179] In this embodiment, the main fault dip data is also referred to as the fault data dip field. The fault data dip field refers to a two-dimensional or three-dimensional spatial distribution composed of a series of fault data points, which can reflect the fault strike and dip changes. In geology, a fault is an important geological phenomenon, usually caused by factors such as crustal movement, geological structure, and volcanic activity. The fault data dip field can help us understand information such as the distribution, strike, and dip of faults, and thus infer the crustal movement mode and crustal structure.

[0180] Through the fault data dip field, the direction and dip of the fault plane can be calculated, and then the nature and activity characteristics of the fault can be analyzed. For example, the dip of a normal fault is usually greater than 45 degrees, while the dip of a reverse fault is less than 30 degrees. In addition, different-scale fracture systems can be identified through the fault data dip field, providing important information for geological research.

[0181] The fault data dip field is an important type of geological data, which can help understand the crustal structure and movement mode, and is of great significance for geological research and geological disaster prediction.

[0182] Embodiment Five

[0183] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0184] Step 110, obtain survey line data, where the survey line data includes main fault data and pseudo-fault data.

[0185] In this embodiment, the survey line data can also be referred to as the main line data, including main fault data and pseudo-fault data. The main fault data and the pseudo-fault data are important terms in geology for describing faults and fractures on the earth's crust.

[0186] The main fault data mainly describes the large-scale and wide-range faults and fractures on the earth's crust. These faults and fractures are usually the result of long-term geological processes (such as plate movements, seismic activities, etc.), and they have important impacts on the earth's geological structure, geomagnetic field, and crustal movement. The main fault data can help us understand the formation and evolution process of the earth's crust, and can also be used to predict the possibility of future geological disasters (such as earthquakes, volcanic eruptions, etc.). The pseudo-fault data, on the other hand, mainly describes the small-scale and small-range faults and fractures. These faults and fractures are usually caused by local geological processes (such as underground water flow, rock pressure changes, etc.), and their impacts on crustal movement and geological structure are relatively small. The pseudo-fault data can help us understand the geological structure and geological processes inside the earth's crust, and can also be used to guide mineral resource exploration and geological engineering design.

[0187] Therefore, the main fault data and the pseudo-fault data are important data types for describing the faults and fractures on the earth's crust, with different characteristics and influence ranges, and are of great significance for understanding the formation and evolution of the earth's crust and predicting geological disasters.

[0188] Step 120, using a preset parameter range, based on a preset main fault identification and extraction algorithm, extract the main fault data from the survey line data to obtain the first main fault data.

[0189] In this embodiment, by setting limits on the waveform peak value range of each survey line, calculating and setting limits on the fault length, calculating the fault azimuth field and setting the main azimuth limit, etc., the pseudo-fault data in the data is removed, and the main fault data is extracted to obtain the first main fault data.

[0190] It is worth mentioning that the preset parameter range is adjustable. Specifically, in this embodiment, the fault data of a single survey line before and after processing is simultaneously displayed through a graphic window. The processing personnel can adjust the processing control parameters in real time according to the processing effect. This control parameter is the preset parameter range. Pick up the data removal and enhancement areas, and then display the processing effect through error evaluation and synchronous display technology, providing a real-time quality control analysis algorithm. In this way, by adjusting the preset parameter range, the accuracy of obtaining the first main fault data can be effectively improved.

[0191] Step 130, using a preset removal algorithm, remove the pseudo-fault data from the survey line data to obtain the second main fault data.

[0192] It should be understood that the fault data volume formed in the tectonic area often contains some fragmented pseudo-fault data, which makes it difficult to identify the true faults. In this embodiment, based on the preset main fault identification and extraction algorithm and the preset removal algorithm, the identification of the main fault data is completed, and the pseudo-fault data in the data is removed, enhancing the resolution and accuracy of the fault data.

[0193] Step 140: Merge the first primary fault data and the second primary fault data to obtain primary fault merged data.

[0194] In this embodiment, merging the first primary fault data and the second primary fault data can obtain more accurate primary fault data.

[0195] In the above embodiment, based on the preset primary fault identification and extraction algorithm and the preset rejection algorithm, the identification of the primary fault data is completed, and the pseudo-fault data in the data is removed, enhancing the resolution and accuracy of the fault data, thereby improving the accuracy of the geological fault data and subsequent velocity modeling and imaging processing.

[0196] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0197] Extract the primary fault data within the preset waveform amplitude range from the survey line data to obtain the first primary fault data.

[0198] In this embodiment, the preset waveform amplitude range is the waveform peak range. The waveform peak range of each survey line is restricted by the preset waveform amplitude range, and the primary fault data within the preset waveform amplitude range is selected to obtain the first primary fault data.

[0199] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0200] Calculate the lengths of the respective survey line data;

[0201] Extract the primary fault data within the preset fault data length range from the survey line data according to the lengths of the respective survey line data to obtain the first primary fault data.

[0202] In this embodiment, the sum of each fault data is calculated to obtain the fault length. The length of each survey line is restricted by the preset fault data length range, and the primary fault data within the preset fault data length range is selected to obtain the first fault data.

[0203] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0204] Calculate the fault azimuth fields of the respective survey line data;

[0205] Extract the primary fault data within the preset primary azimuth range from the survey line data according to the fault azimuth fields of the respective survey line data to obtain the first primary fault data.

[0206] In this embodiment, the azimuth angle field of each fault is calculated, and using the preset main azimuth angle range, the main fault data within the preset main azimuth angle range is extracted from the survey line data to obtain the first main fault data.

[0207] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0208] The survey line data is divided into multiple fault data according to the time direction;

[0209] Using the preset parameter range, based on the preset main fault identification and extraction algorithm, the main fault data is respectively extracted from each of the fault data to obtain the first main fault data.

[0210] In this embodiment, one of the processing threads of the processor divides the survey line data into multiple fault data according to the time direction, and each of the divided fault data is respectively assigned to different processing threads for calculation. Each processing thread is responsible for using the preset parameter range to extract the main fault at the fault starting point during this time period based on the preset main fault identification and extraction algorithm, so as to obtain the first main fault data.

[0211] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0212] The survey line data is divided into fault data of multiple channels according to channels;

[0213] Using the preset rejection algorithm, the pseudo-fault data is removed from the fault data of each channel to obtain the second main fault data.

[0214] In this embodiment, one of the processing threads of the processor divides the survey line data into fault data of multiple channels according to channels, and distributes the fault data to the corresponding processing threads for calculation according to channels. Each processing thread uses the preset rejection algorithm to remove the pseudo-fault data from the fault data of each channel to obtain the second main fault data, so as to obtain the second main fault data.

[0215] In the above embodiment, when extracting the main fault data, the basis for dividing the survey line data into multiple fault data is the time direction, while the basis for removing the pseudo-fault data is the channel when dividing the survey line data into multiple fault data. Therefore, the fault data is divided from different dimensions, so as to form the first fault data and the second fault data extracted based on different dimensions. In this way, the merged main fault merged data is made more accurate.

[0216] In addition, in the above embodiments, the first main fault data and the second main fault data are respectively extracted by different processing threads. Among them, the thread for extracting the first main fault data divides the fault data into multiple computing threads in the time direction, and each computing thread is responsible for extracting the main fault with the fault starting point in this time period; the thread for extracting the first main fault data assigns the channels to the corresponding thread calculations by removing the pseudo-fault data for each channel; then the first main fault data and the second main fault data are merged by the main thread. In this way, by using sub-threads to extract the main fault data and remove the pseudo-fault data, the computing resources can be utilized more reasonably, effectively improving the computing efficiency.

[0217] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0218] Perform dip field processing on the merged main fault data to obtain main fault dip data.

[0219] In this embodiment, the main fault dip data is also referred to as the fault data dip field. The fault data dip field refers to a two-dimensional or three-dimensional spatial distribution composed of a series of fault data points, which can reflect the strike and dip changes of the fault. In geology, a fault is an important geological phenomenon, usually caused by factors such as crustal movement, geological structure, and volcanic activity. The fault data dip field can help us understand information such as the distribution, strike, and dip of the fault, thereby inferring the movement mode of the crust and the crustal structure.

[0220] Through the fault data dip field, the direction and dip of the fault plane can be calculated, and then the nature and activity characteristics of the fault can be analyzed. For example, the dip angle of a normal fault is usually greater than 45 degrees, while the dip angle of a reverse fault is less than 30 degrees. In addition, different-scale fracture systems can also be identified through the fault data dip field, providing important information for geological research.

[0221] The fault data dip field is an important type of geological data, which can help understand the crustal structure and movement mode, and is of great significance for geological research and geological disaster prediction.

[0222] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0223] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0224] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A method for processing geological fault data, characterized in that, it includes: Obtain survey line data, wherein the survey line data includes main fault data and pseudo-fault data; Using a preset parameter range, based on a preset main fault identification and extraction algorithm, extract the main fault data from the survey line data to obtain first main fault data; Using a preset elimination algorithm, eliminate the pseudo-fault data from the survey line data to obtain second main fault data; Merge the first main fault data and the second main fault data to obtain main fault merged data.

2. The method according to claim 1, characterized in that, The step of using a preset parameter range, based on a preset main fault identification and extraction algorithm, extracting the main fault data from the survey line data to obtain first main fault data includes: Using a preset waveform amplitude range, extract the main fault data within the preset waveform amplitude range from the survey line data to obtain first main fault data.

3. The method according to claim 1, characterized in that, The step of using a preset parameter range, based on a preset main fault identification and extraction algorithm, extracting the main fault data from the survey line data to obtain first main fault data includes: Calculate the length of each survey line data; Using a preset fault data length range, according to the length of each survey line data, extract the main fault data within the preset fault data length range from the survey line data to obtain first main fault data.

4. The method according to claim 1, characterized in that, The step of using a preset parameter range, based on a preset main fault identification and extraction algorithm, extracting the main fault data from the survey line data to obtain first main fault data includes: Calculate the fault azimuth field of each survey line data; Using a preset main azimuth range, according to the fault azimuth field of each survey line data, extract the main fault data within the preset main azimuth range from the survey line data to obtain first main fault data.

5. The method according to claim 1, characterized in that, The step of using a preset parameter range, based on a preset main fault identification and extraction algorithm, extracting the main fault data from the survey line data to obtain first main fault data includes: Divide the survey line data into multiple fault data in the time direction; Using a preset parameter range, based on a preset main fault identification and extraction algorithm, extract the main fault data from each of the fault data to obtain first main fault data.

6. The method according to claim 1, characterized in that, The step of using a preset elimination algorithm, eliminating the pseudo-fault data from the survey line data to obtain second main fault data includes: Divide the survey line data into fault data of multiple channels by channel; Using a preset elimination algorithm, eliminate the pseudo-fault data from the fault data of each channel to obtain second main fault data.

7. The method according to any one of claims 1-6, characterized in that, After the step of merging the first main fault data and the second main fault data to obtain main fault merged data, it further includes: Perform dip field processing on the merged main fault data to obtain main fault dip data.

8. A geological fault data processing device characterized in that it includes: A survey line data acquisition module for acquiring survey line data, where the survey line data includes main fault data and pseudo-fault data; A first main fault acquisition module for extracting the main fault data from the survey line data based on a preset main fault identification and extraction algorithm using a preset parameter range to obtain first main fault data; A second main fault acquisition module for removing the pseudo-fault data from the survey line data using a preset removal algorithm to obtain second main fault data; A merging module for merging the first main fault data and the second main fault data to obtain merged main fault data.

9. A computer device includes a memory and a processor, and the memory stores a computer program. characterized in that when the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium has a computer program stored thereon. characterized in that when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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