Method for judging barite deposition center by utilizing thickness of siliceous rock

Abstract

The invention discloses a method for judging barite deposition center by using siliceous rock thickness, and relates to the technical field of mineral resource exploration, and the method comprises the steps: collecting seismic wave reflection data and geological data, and carrying out the preprocessing; predicting the thickness of the siliceous rock stratum based on the processed seismic wave reflection data; the thickness of the siliceous rock stratum and the preprocessed geological data are input into a three-dimensional geological model, and a siliceous rock thickness space distribution diagram is generated; analyzing the spatial distribution diagram of the siliceous rock thickness to obtain a siliceous rock thickness change area; identifying a barite deposition center candidate area based on the siliceous rock thickness change area; and analyzing the barite deposition center candidate area by using a spatial analysis technology to obtain the spatial position of the barite deposition center. According to the method, the candidate area is subjected to multi-parameter optimization by utilizing a space analysis technology, and the barite deposition center position is accurately determined.

Description

Technical Field

[0001] The invention relates to the technical field of mineral resource exploration, in particular to a method for judging a barite deposition center by utilizing the thickness of siliceous rock. Background Art

[0002] With the continuous development of geological exploration technology, barite exploration has gradually become more accurate and efficient. The limitations of traditional methods have gradually emerged, especially in the identification of sedimentary centers. Since the formation of mineral deposits is affected by many complex factors, it is often difficult to effectively locate the mineralization center of the mineral deposit by relying solely on a single geological feature.

[0003] In the field of mineral resource exploration, the current thickness prediction method based on seismic wave reflection data is often limited in accuracy when dealing with complex geological environments, especially in multi-layered and complex structural areas. The reflection characteristics of seismic wave signals are interfered by other geological layers, resulting in a decrease in the accuracy of siliceous rock layer thickness prediction. Most of the existing sedimentary center identification methods rely on traditional empirical judgments and local geological surveys, lacking the support of systematic spatial analysis and multi-source data fusion. Therefore, there are often problems of low identification accuracy and limited prediction range. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for determining the barite deposition center by using the thickness of siliceous rock, which solves the problems of low thickness prediction accuracy and insufficient deposition center identification accuracy in complex geological environments in the prior art.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for determining a barite deposition center using the thickness of siliceous rock, which comprises collecting seismic wave reflection data and geological data, and performing preprocessing; Predict the thickness of siliceous rock layers based on processed seismic wave reflection data; The thickness of the siliceous rock layer and the pre-processed geological data are input into the three-dimensional geological model to generate a spatial distribution map of the siliceous rock thickness; The spatial distribution map of siliceous rock thickness is analyzed to obtain the area where the siliceous rock thickness varies; Based on the variation of siliceous rock thickness, candidate areas of barite deposition centers were identified; The candidate area of ​​barite deposition center was analyzed by spatial analysis technology to obtain the spatial position of the barite deposition center.

[0007] As a preferred solution of the method of using the thickness of siliceous rock to determine the barite deposition center described in the present invention, the seismic wave reflection data includes the arrival time, reflection wave velocity, reflection layer and reflection amplitude of the reflection wave, and the geological data includes drilling data, geological profile data and geological structure data, and the collected data is subjected to data denoising and standardization processing.

[0008] As a preferred embodiment of the method for determining the barite deposition center by using the thickness of siliceous rock according to the present invention, the thickness of the siliceous rock layer is predicted based on the processed seismic wave reflection data, including the following steps: The propagation time information of the reflected wave is obtained by combining the arrival time of the reflected wave with the depth of the reflecting layer; Identify the interface of the reflection layer based on the propagation time information of the reflected wave, the change of the reflected amplitude and the reflected wave velocity; The thickness of the siliceous rock layer is obtained by combining the identified reflector interface with the depth of each reflector layer.

[0009] As a preferred solution of the method for determining the barite deposition center by using the thickness of siliceous rock described in the present invention, the three-dimensional geological model is established based on geological data and the thickness of siliceous rock, including the following steps: Based on the borehole data and geological sections, the stratigraphic boundary surface is generated using the Kriging interpolation method; Use fitting methods to perform surface fitting on geological structure data to identify fault planes; The thickness of the siliceous rock layer is added to the stratigraphic framework to generate the upper and lower boundaries of the siliceous rock layer; The stratigraphic boundary surfaces, fault planes, and upper and lower boundaries of the siliceous rock formations are integrated to generate a three-dimensional geological model.

[0010] As a preferred solution of the method for determining the barite deposition center by using the thickness of siliceous rock described in the present invention, the thickness of the siliceous rock layer and the pre-processed geological data are input into the three-dimensional geological model to generate a spatial distribution map of the siliceous rock thickness, including the following steps: The drilling data and geological profile data are extracted from the preprocessed geological data. The thickness of the siliceous rock layer, the drilling data and the geological profile data are input into the three-dimensional geological model to obtain the spatial distribution probability of the thickness of the siliceous rock layer, which is expressed as: ; in, is the thickness of the siliceous rock layer at a point in space The probability distribution of Space Point The average thickness of the siliceous rock layer on For space point The actual value of the thickness of the siliceous rock layer on is the standard deviation of the thickness of the siliceous rock layer, For space point The weight of the drilling data on For space point The weight of the geological profile data on For space point The weight of the seismic wave reflection data; The spatial distribution probability of siliceous rock thickness is visualized to generate a spatial distribution map of siliceous rock thickness.

[0011] As a preferred solution of the method for determining the barite deposition center by using the thickness of siliceous rock according to the present invention, the spatial distribution diagram of the siliceous rock thickness is analyzed to obtain the siliceous rock thickness variation area, which includes the following steps: The gradient method is used to analyze the difference in siliceous rock thickness in the siliceous rock thickness spatial distribution map to identify the siliceous rock thickness areas with large thickness changes. The expression is: ; in, is the gradient value of the siliceous rock layer thickness, for The partial derivative of the direction, for The partial derivative of the direction, for The partial derivative of the direction, is the thickness of siliceous rock in the horizontal direction The rate of change on is the thickness of siliceous rock in the horizontal direction The rate of change on is the thickness of siliceous rock in the horizontal direction The rate of change of A gradient threshold is set by the gradient of the siliceous rock layer thickness. When the gradient of the siliceous rock layer thickness is greater than the gradient threshold, it is regarded as an area with a large change in the siliceous rock layer thickness, thereby obtaining a siliceous rock thickness change area.

[0012] As a preferred solution of the method for determining the barite deposition center by using the thickness of siliceous rock according to the present invention, the candidate area of ​​the barite deposition center is identified based on the area where the thickness of the siliceous rock changes, including the following steps: The spatial overlap analysis method is used to check whether the area of ​​chert thickness variation overlaps with the geological data distribution, and the overlapping part is the candidate area of ​​barite deposition center; The area of ​​siliceous rock thickness variation is combined with seismic wave reflection data to obtain the barite deposition depth through the seismic reflection interface. If a large siliceous rock layer thickness variation is found within the barite deposition depth range, it is a candidate area for the barite deposition center. The relationship between the thickness change of siliceous rock layers and the barite depositional environment is analyzed. The area where the thickness change of siliceous rock layers meets the barite depositional environmental conditions is a candidate area for the barite depositional center.

[0013] As a preferred embodiment of the method for determining the barite deposition center by using the thickness of siliceous rock according to the present invention, the candidate area of ​​the barite deposition center is analyzed by using spatial analysis technology to obtain the spatial position of the barite deposition center, which includes the following steps: The siliceous rock thickness gradient value, barite deposition depth and drilling data were extracted from the candidate area of ​​the barite deposition center. The extracted data were analyzed using spatial analysis technology to obtain the spatial position of the barite deposition center, which is expressed as: ; in, For candidate district The location of the barite deposition center. For the The thickness gradient value of siliceous rock at each point, For the The characteristic value of barite deposition depth at each point, For the The barite mineral content of each point, For the The barite deposition depth at each point is matched to the value. For the The characteristics of the geological data of each point, is the siliceous rock thickness gradient weight adjustment coefficient, is the barite deposition depth adjustment coefficient, is the characteristic adjustment coefficient of geological data, For the The adjustment coefficient of the siliceous rock thickness gradient value at each point.

[0014] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for determining the barite deposition center by using the thickness of siliceous rock as described in the first aspect of the present invention is implemented.

[0015] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for determining the barite deposition center by using the thickness of siliceous rock as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: by collecting seismic wave reflection data and geological data, denoising and standardization are performed to provide high-quality input data for subsequent calculations; based on the processed seismic wave data, the reflection layer interface is identified by propagation time, amplitude and wave velocity changes, and the thickness of the siliceous rock layer is accurately predicted in combination with the reflection layer depth; subsequently, a three-dimensional geological model is constructed using the siliceous rock thickness and multi-source geological data, the stratigraphic boundary is generated by Kriging interpolation, the fault plane is identified by fitting, and a complete three-dimensional structural framework is generated by integration; the thickness data is input into the model, and a spatial distribution map of the siliceous rock thickness is generated and visualized in combination with multiple weight factors; the thickness change area is analyzed by gradient, combined with spatial overlap analysis and sedimentary environmental conditions, the candidate area of ​​the barite deposition center is further identified, and the candidate area is optimized by multi-parameters using spatial analysis technology to accurately determine the location of the barite deposition center. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 This is a flow chart of the method for determining the barite deposition center using the thickness of siliceous rock in Example 1.

[0019] Figure 2 This is a schematic diagram of establishing a three-dimensional geological model in Example 1. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1, reference Figure 1 and Figure 2, which is the first embodiment of the present invention, provides a method for determining the barite deposition center by using the thickness of siliceous rock, comprising the following steps: S1. Collect seismic wave reflection data and geological data and perform preprocessing.

[0024] S1.1. Seismic wave reflection data include the arrival time, reflection wave velocity, reflection layer and reflection amplitude of the reflection wave. Geological data include drilling data, geological profile data and geological structure data. The collected data are subjected to data denoising and standardization processing.

[0025] Furthermore, the time when the reflected wave reaches the surface is collected and recorded by seismic exploration equipment (such as seismic detectors), and the propagation speed of the reflected wave in the medium is obtained by velocity analysis method. The interface of the reflection layer is identified according to the reflection signal in the seismic profile, and the depth and position of the interface are marked to obtain the reflection layer. The amplitude of the reflected wave signal is extracted as a parameter reflecting the attributes of the geological interface. The formation thickness, lithology and mineral composition are extracted from the existing drilling records to form drilling data. The formation structure, dip angle and lithology distribution of the study area are obtained through geological mapping to obtain geological profile data. The faults and folds within the area are collected to obtain geological structure data. Fast Fourier transform is used to analyze the frequency components of seismic wave signals, filter out high-frequency noise or low-frequency interference, clean up abnormal points in drilling data (such as measurement errors or invalid data), use linear interpolation to fill in missing data, smooth geological profiles and structural data, eliminate random errors introduced during the measurement process, normalize different reflection wave amplitude values, eliminate amplitude fluctuations caused by differences in seismic equipment sensitivity or environmental noise, and classify and encode geological data or perform numerical normalization.

[0026] S2. Predict the thickness of the siliceous rock layer based on the processed seismic wave reflection data.

[0027] S2.1. The propagation time information of the reflected wave is obtained by combining the arrival time of the reflected wave with the depth of the reflecting layer.

[0028] Furthermore, by utilizing the arrival time of the reflected wave and the depth information of the reflection layer in the seismic wave reflection data, the first arrival time of the seismic wave is selected, and the propagation time of the reflection points at different depths is calculated to establish a time-depth relationship curve. This can accurately link the time information of the seismic wave with the spatial depth and obtain the propagation time information of the reflected wave.

[0029] S2.2. Identify the interface of the reflection layer based on the propagation time information of the reflected wave, the change of the reflection amplitude and the reflected wave velocity.

[0030] Furthermore, the time point of the reflection signal is extracted from the propagation time information, and the interfaces of different reflection layers are identified by combining the changing characteristics of the reflection wave amplitude and wave velocity. The amplitude of the reflection wave usually changes significantly at the interface, and the sudden change in wave velocity also reflects the difference in the properties of the formation. The position of the reflection layer interface can be accurately identified by automatically detecting the extreme points of the amplitude curve and overlapping them with the position of the wave velocity change.

[0031] S2.3. The thickness of the siliceous rock layer is obtained by combining the identified reflection layer interface with the depth of each reflection layer.

[0032] Furthermore, after the reflection layer interface is identified, by measuring the depth difference between adjacent reflection layer interfaces, for the seismic profile of the multi-layer structure, the reflection layer with siliceous rock is screened out to obtain the thickness distribution of the siliceous rock layer.

[0033] S3. Establish a three-dimensional geological model based on geological data and siliceous rock thickness.

[0034] S3.1. Based on the borehole data and geological profiles, the stratigraphic boundary surface is generated using the Kriging interpolation method.

[0035] Furthermore, according to the borehole data and geological profile, the depth information of the stratum boundary is extracted, and the continuous stratum boundary surface is generated by the Kriging interpolation method. First, the depth values ​​of the top and bottom interfaces of the stratum in the borehole data are collected, and the inclination and extension direction of the stratum in the geological profile are combined to establish a spatial data point set. Based on the Kriging interpolation method, the spatial weight relationship of the borehole points is used to interpolate and generate a surface model of the stratum boundary to ensure that the boundary surface has a high accuracy near the borehole point. The boundary surface is smoothed to eliminate local anomalies in the interpolation process and obtain the stratum boundary surface. S3.2. Use the fitting method to perform surface fitting on the geological structure data to identify the fault plane.

[0036] Furthermore, the fault point and fault line information in the geological structure data is used to accurately identify the fault plane through the surface fitting method, the spatial point coordinates of the fault point and the extension direction of the fault line are extracted, and the fault spatial distribution data set is constructed. The fault points are fitted with polynomial fitting to generate an initial fault plane model. The fitting results are optimized using the fault dip and strike data, the direction and position of the fault plane are corrected, and the residual analysis of the fitted fault plane is performed to eliminate abnormal data points to generate a high-precision fault plane.

[0037] S3.3. Add the thickness of the siliceous rock layer to the stratigraphic framework to generate the upper and lower boundaries of the siliceous rock layer.

[0038] Furthermore, taking the top interface in the stratigraphic framework as a reference, the thickness value of the siliceous rock layer is superimposed on the top interface point by point, and the depth of the bottom interface is calculated. The thickness and shape of the upper and lower boundaries are corrected according to the drilling data to ensure that the range of the siliceous rock layer is consistent with the actual geological conditions. The spatial interpolation method is used to generate continuous surfaces for the upper and lower boundaries of the siliceous rock layer to eliminate local fluctuations in the thickness distribution and obtain the upper and lower boundaries of the siliceous rock layer.

[0039] S3.4. Integrate the stratigraphic boundary surface, fault plane, and upper and lower boundaries of the siliceous rock layer to generate a three-dimensional geological model.

[0040] Furthermore, the intersection calculation method is used to determine the intersection line between the stratigraphic boundary and the fault plane in space to ensure that the cutting and dislocation characteristics of the fault on the stratigraphic layer are correctly expressed, the upper and lower boundaries of the siliceous rock layer are embedded in the stratigraphic framework, and its spatial relationship with the fault and other stratigraphic boundaries is checked to correct possible overlap or dislocation problems; finally, all data are integrated into a three-dimensional geological model using three-dimensional modeling software (such as GOCAD or Petrel).

[0041] S4. Input the thickness of the siliceous rock layer and the pre-processed geological data into the three-dimensional geological model to generate a spatial distribution map of the siliceous rock thickness.

[0042] S4.1 extracts the drilling data and geological profile data from the preprocessed geological data, inputs the thickness of the siliceous rock layer, the drilling data and the geological profile data into the three-dimensional geological model, and obtains the spatial distribution probability of the thickness of the siliceous rock layer, which is expressed as: ; in, is the thickness of the siliceous rock layer at a point in space The probability distribution of Space Point The average thickness of the siliceous rock layer on For space point The actual value of the thickness of the siliceous rock layer on is the standard deviation of the thickness of the siliceous rock layer, For space point The weight of the drilling data on For space point The weight of the geological profile data on For space point The weight of the seismic wave reflection data.

[0043] Furthermore, the thickness value of the siliceous rock layer and its spatial position at each drilling point are extracted from the drilling data, and the weight of the drilling data is calculated. The weight is determined by the data point density or the inverse distance method. The stratigraphic changes and thickness information of the siliceous rock layer in the geological section are extracted, and the spatial surface data of the section is generated. The section weight is calculated. The section weight can be calculated from the distance from the section point to the spatial point. Based on the seismic wave reflection data, the spatial grid data is generated and weighted in combination with the wave velocity and amplitude characteristics. The thickness of the siliceous rock layer is calculated using a given formula.

[0044] S4.2 Visualize the spatial distribution probability of siliceous rock thickness and generate a spatial distribution map of siliceous rock thickness Furthermore, the thickness of the siliceous rock layer at the spatial point The probability distribution is converted into continuous three-dimensional grid data through interpolation algorithm, and the grid data is rendered using geological modeling software. High-probability areas are represented by higher brightness or specific colors, and low-probability areas are represented by lower brightness or other colors. Finally, the distribution map is compared and verified in combination with geological profiles and drilling data to ensure that the visualization results accurately express the spatial distribution characteristics of the thickness of the siliceous rock layer, thereby intuitively displaying the distribution law of the siliceous rock layer in three-dimensional space.

[0045] S5. Analyze the spatial distribution map of siliceous rock thickness to obtain the area where the siliceous rock thickness varies.

[0046] S5.1. Use the gradient method to analyze the differences in siliceous rock thickness in the spatial distribution map of siliceous rock thickness to identify siliceous rock thickness areas with large thickness variations. The expression is: ; in, is the gradient value of the siliceous rock layer thickness, for The partial derivative of the direction, for The partial derivative of the direction, for The partial derivative of the direction, is the thickness of siliceous rock in the horizontal direction The rate of change on is the thickness of siliceous rock in the horizontal direction The rate of change on is the thickness of siliceous rock in the horizontal direction The rate of change on .

[0047] Furthermore, the areas where the thickness of the siliceous rock changes greatly can be identified. The gradient method can be used to analyze the rate of change of the siliceous rock thickness in three directions at a spatial point, and the gradient value of the siliceous rock layer thickness can be calculated. After calculating the gradient value of the siliceous rock layer thickness, the size of the gradient value of the siliceous rock layer thickness is analyzed. The places where the gradient value of the siliceous rock layer thickness is large mean that the thickness of the siliceous rock layer has changed greatly. Therefore, the size of the gradient value is proportional to the intensity of the change in the thickness of the siliceous rock. A high gradient value indicates that the thickness of the siliceous rock layer has changed dramatically.

[0048] S5.2 sets a gradient threshold by the gradient of the siliceous rock layer thickness. When the gradient of the siliceous rock layer thickness is greater than the gradient threshold, it is regarded as an area with a large change in the siliceous rock layer thickness, and the siliceous rock thickness change area is obtained.

[0049] Furthermore, a gradient threshold is set by the gradient of the siliceous rock layer thickness. When the gradient of the siliceous rock layer thickness is greater than the gradient threshold, the area is marked as an area with a large thickness change. This area corresponds to a sedimentary center, a fault or a strong stratigraphic change zone, and the change area is marked to generate a spatial distribution map of the siliceous rock thickness change area.

[0050] S6. Based on the area of ​​variation in siliceous rock thickness, candidate areas of barite deposition centers are identified.

[0051] S6.1. Use the spatial overlap analysis method to check whether the area of ​​siliceous rock thickness variation overlaps with the geological data distribution. The overlapping part is the candidate area of ​​barite deposition center.

[0052] Furthermore, the spatial overlapping analysis method is used to overlay the area where the siliceous rock thickness varies with the known geological data distribution. The area where the siliceous rock thickness varies is used as the spatial input to analyze the scope, location and correlation of the overlapping area with the geological conditions. If the area where the siliceous rock thickness varies overlaps with key geological data (such as known barite mines), these overlapping areas are identified as potential candidate areas for barite deposition centers.

[0053] S6.2. The area of ​​siliceous rock thickness variation is combined with the seismic wave reflection data, and the barite deposition depth is obtained through the seismic reflection interface. If a large variation in siliceous rock layer thickness is found within the barite deposition depth range, it is a candidate area for the barite deposition center.

[0054] Furthermore, the area where the siliceous rock thickness varies is combined with the seismic wave reflection data, and the specific depth range of barite deposition is analyzed through the seismic reflection interface. The specific strata or depth intervals where barite deposition may exist are determined through the reflection time-to-depth conversion of the seismic data. Within these depth ranges, it is checked whether the area where the siliceous rock thickness varies shows significant thickness variation. If a large gradient variation in the siliceous rock thickness is found within the depth range of barite deposition, these areas can be further marked as candidate areas for barite deposition centers.

[0055] S6.3. Analyze the relationship between the thickness change of the siliceous rock layer and the barite deposition environment. If the area where the thickness change of the siliceous rock layer meets the barite deposition environment conditions, it is a candidate area for the barite deposition center.

[0056] Furthermore, an in-depth analysis of the sedimentary environmental conditions in the area where the siliceous rock thickness changes is conducted to determine whether it meets the sedimentary environmental characteristics of barite. Combined with the regional geological background, the focus is on analyzing whether the area where the siliceous rock thickness changes is located in a suitable sedimentary environment, a tectonic belt rich in fluid circulation, a sedimentary stable area, a sulfur-containing fluid activity area or a sedimentary center. Through sedimentary dynamics analysis, it is evaluated whether the change in siliceous rock thickness is consistent with the formation process of barite deposition. The area where the siliceous rock thickness changes and meets the barite sedimentary environmental conditions is identified as a candidate area for barite deposition center.

[0057] S7. Analyze the candidate area of ​​the barite deposition center using spatial analysis technology to obtain the spatial position of the barite deposition center.

[0058] The siliceous rock thickness gradient value, barite deposition depth and drilling data were extracted from the candidate area of ​​the barite deposition center. The extracted data were analyzed using spatial analysis technology to obtain the spatial position of the barite deposition center, which is expressed as: ; in, For candidate district The location of the barite deposition center. For the The thickness gradient value of siliceous rock at each point, For the The characteristic value of barite deposition depth at each point, For the The barite mineral content of each point, For the The barite deposition depth at each point is matched to the value. For the The characteristics of the geological data of each point, is the siliceous rock thickness gradient weight adjustment coefficient, is the barite deposition depth adjustment coefficient, is the characteristic adjustment coefficient of geological data, For the The adjustment coefficient of the siliceous rock thickness gradient value at each point.

[0059] Furthermore, the gradient method is used to obtain the gradient value of the siliceous rock thickness at each point. The gradient value represents the intensity of the change in the thickness of the siliceous rock in space. The deposition depth of barite is extracted from the geological profile and drilling data. Based on the mineral analysis data, the mineral content of barite at each point is obtained. By matching with the geological conditions (such as deposition rate, deposition environment, etc.), the adaptation value of the barite deposition depth at each point is obtained. The geological characteristics of each point are extracted through geological survey data, seismic reflection data, etc.; The depth of barite deposition directly affects the location of barite deposits. Values ​​indicate deeply buried deposits, smaller A value of 0 indicates that the deposit is located in a shallower stratum. , is the content of barite mineral. The higher the mineral content, the more likely it is that the sedimentation center at that point will form a barite deposit.

[0060] This embodiment also provides a computer device, which is suitable for the method of using the thickness of siliceous rock to determine the deposition center of barite, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method of using the thickness of siliceous rock to determine the deposition center of barite as proposed in the above embodiment.

[0061] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication 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. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0062] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, the method for determining the barite deposition center by using the thickness of siliceous rock proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0063] In summary, the present invention collects seismic wave reflection data and geological data, performs denoising and standardization processing, provides high-quality input data for subsequent calculations, and identifies the reflection layer interface through propagation time, amplitude and wave velocity changes based on the processed seismic wave data, and accurately predicts the thickness of the siliceous rock layer in combination with the reflection layer depth. Subsequently, a three-dimensional geological model is constructed using the thickness of the siliceous rock and multi-source geological data, and the stratigraphic boundary is generated by Kriging interpolation. The fault plane is identified by fitting, and a complete three-dimensional structural framework is generated by integration. The thickness data is input into the model, and a spatial distribution map of the siliceous rock thickness is generated and visualized in combination with multiple weight factors. The thickness change area is analyzed by gradient, and the spatial overlap analysis and sedimentary environmental conditions are combined to further identify the candidate area of ​​the barite deposition center, and the spatial analysis technology is used to perform multi-parameter optimization on the candidate area to accurately determine the position of the barite deposition center.

[0064] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for determining the barite deposition center by using the thickness of siliceous rock, characterized in that: include, Collect seismic wave reflection data and geological data and perform pre-processing; Predict the thickness of siliceous rock layers based on processed seismic wave reflection data; The thickness of the siliceous rock layer and the pre-processed geological data are input into the three-dimensional geological model to generate a spatial distribution map of the siliceous rock thickness; The spatial distribution map of siliceous rock thickness is analyzed to obtain the area where the siliceous rock thickness varies; Based on the variation of siliceous rock thickness, candidate areas of barite deposition centers were identified; The candidate area of ​​barite deposition center was analyzed by spatial analysis technology to obtain the spatial position of the barite deposition center.

2. The method for determining the barite deposition center by using the thickness of siliceous rock according to claim 1, characterized in that: Collecting seismic wave reflection data and geological data and preprocessing them include the following steps: The seismic wave reflection data includes the arrival time, reflection wave velocity, reflection layer and reflection amplitude of the reflection wave, and the geological data includes drilling data, geological profile data and geological structure data. The collected data are subjected to data denoising and standardization processing.

3. The method for determining the barite deposition center by using the thickness of siliceous rock according to claim 2, characterized in that: Based on the processed seismic wave reflection data, predicting the thickness of the siliceous rock layer includes the following steps: The propagation time information of the reflected wave is obtained by combining the arrival time of the reflected wave with the depth of the reflecting layer; Identify the interface of the reflection layer based on the propagation time information of the reflected wave, the change of the reflected amplitude and the reflected wave velocity; The thickness of the siliceous rock layer is obtained by combining the identified reflector interface with the depth of each reflector layer.

4. The method for determining the barite deposition center by using the thickness of siliceous rock according to claim 3, characterized in that: The establishment of a 3D geological model based on geological data and siliceous rock thickness includes the following steps: Based on the borehole data and geological sections, the stratigraphic boundary surface is generated using the Kriging interpolation method; Use fitting methods to perform surface fitting on geological structure data to identify fault planes; The thickness of the siliceous rock layer is added to the stratigraphic framework to generate the upper and lower boundaries of the siliceous rock layer; The stratigraphic boundary surfaces, fault planes, and upper and lower boundaries of the siliceous rock formations are integrated to generate a three-dimensional geological model.

5. The method for determining the barite deposition center by using the thickness of siliceous rock according to claim 4, characterized in that: Inputting the thickness of the siliceous rock layer and the pre-processed geological data into the three-dimensional geological model to generate a siliceous rock thickness spatial distribution map includes the following steps: The drilling data and geological profile data are extracted from the preprocessed geological data. The thickness of the siliceous rock layer, the drilling data and the geological profile data are input into the three-dimensional geological model to obtain the spatial distribution probability of the thickness of the siliceous rock layer, which is expressed as: ； in, is the thickness of the siliceous rock layer at a point in space The probability distribution of Space Point The average thickness of the siliceous rock layer on For space point The actual value of the thickness of the siliceous rock layer on is the standard deviation of the thickness of the siliceous rock layer, For space point The weight of the drilling data on For space point The weight of the geological profile data on For space point The weight of the seismic wave reflection data; The spatial distribution probability of siliceous rock thickness is visualized to generate a spatial distribution map of siliceous rock thickness.

6. The method for determining the barite deposition center by using the thickness of siliceous rock according to claim 5, characterized in that: Analyzing the spatial distribution map of siliceous rock thickness to obtain the siliceous rock thickness variation area includes the following steps: The gradient method is used to analyze the difference in siliceous rock thickness in the siliceous rock thickness spatial distribution map to identify the siliceous rock thickness areas with large thickness changes. The expression is: ； in, is the gradient value of the siliceous rock layer thickness, for The partial derivative of the direction, for The partial derivative of the direction, for The partial derivative of the direction, is the thickness of siliceous rock in the horizontal direction The rate of change on is the thickness of siliceous rock in the horizontal direction The rate of change on is the thickness of siliceous rock in the horizontal direction The rate of change of A gradient threshold is set by the gradient of the siliceous rock layer thickness. When the gradient of the siliceous rock layer thickness is greater than the gradient threshold, it is regarded as an area with a large change in the siliceous rock layer thickness, thereby obtaining a siliceous rock thickness change area.

7. The method for determining the barite deposition center by using the thickness of siliceous rock according to claim 6, characterized in that: Based on the variation of siliceous rock thickness, the candidate areas of barite deposition centers are identified, including the following steps: The spatial overlap analysis method is used to check whether the area of ​​chert thickness variation overlaps with the geological data distribution, and the overlapping part is the candidate area of ​​barite deposition center; The area of ​​siliceous rock thickness variation is combined with seismic wave reflection data to obtain the barite deposition depth through the seismic reflection interface. If a large siliceous rock layer thickness variation is found within the barite deposition depth range, it is a candidate area for the barite deposition center. The relationship between the thickness change of siliceous rock layers and the barite depositional environment is analyzed. The area where the thickness change of siliceous rock layers meets the barite depositional environmental conditions is a candidate area for the barite depositional center.

8. The method for determining the barite deposition center by using the thickness of siliceous rock according to claim 7, characterized in that: Using spatial analysis technology to analyze the candidate area of ​​barite deposition center, the spatial location of the barite deposition center is obtained, including the following steps: The siliceous rock thickness gradient value, barite deposition depth and drilling data were extracted from the candidate area of ​​the barite deposition center. The extracted data were analyzed using spatial analysis technology to obtain the spatial position of the barite deposition center, which is expressed as: ； in, For candidate district The location of the barite deposition center. For the The thickness gradient value of siliceous rock at each point, For the The characteristic value of barite deposition depth at each point, For the The barite mineral content of each point, For the The barite deposition depth at each point is matched to the value. For the The characteristics of the geological data of each point, is the siliceous rock thickness gradient weight adjustment coefficient, is the barite deposition depth adjustment coefficient, is the characteristic adjustment coefficient of geological data, For the The adjustment coefficient of the siliceous rock thickness gradient value at each point.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for determining the barite deposition center by using the thickness of siliceous rock as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for determining the barite deposition center by using the thickness of siliceous rock as described in any one of claims 1 to 8 are implemented.

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