Method for determining a center of deposition of barite using siliceous rock thickness

Abstract

The application discloses a method for determining a barite deposition center by using siliceous rock thickness, relates to the technical field of mineral resource exploration, and comprises the following steps: collecting seismic wave reflection data and geological data and performing pretreatment; predicting the thickness of a siliceous rock layer based on the processed seismic wave reflection data; inputting the thickness of the siliceous rock layer and the pretreated geological data into a three-dimensional geological model to generate a siliceous rock thickness spatial distribution map; analyzing the spatial distribution map 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 technique to obtain the spatial position of the barite deposition center. The application can accurately determine the position of the barite deposition center by using the spatial analysis technique to perform multi-parameter optimization on the candidate area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mineral resources exploration, and in particular to a method for determining a barite deposition center by using siliceous rock thickness. BACKGROUND

[0002] With the continuous development of geological exploration technology, barite exploration work is gradually moving towards more accurate and efficient direction, and the limitations of traditional methods gradually appear, especially in the identification of deposition center, due to the influence of various complex factors on the formation of the deposit, it is often difficult to effectively locate the ore-forming center of the deposit by relying on single geological characteristics.

[0003] In the field of mineral resources exploration, the thickness prediction method based on seismic wave reflection data is often limited in accuracy when dealing with complex geological environments, especially in areas with multiple levels and complex structures, the reflection characteristics of seismic wave signals are disturbed by other geological levels, resulting in a decrease in the accuracy of siliceous rock layer thickness prediction, and the existing deposition center identification method mostly relies on traditional experience judgment and local geological investigation, lacking systematic spatial analysis and multi-source data fusion support, therefore, there are often problems of low identification accuracy and limited prediction range. SUMMARY

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

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

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a method for determining a barite deposition center by using siliceous rock thickness, which includes collecting seismic wave reflection data and geological data and preprocessing them;

[0008] Based on the processed seismic wave reflection data, the thickness of the siliceous rock layer is predicted;

[0009] The thickness of the siliceous rock layer and the preprocessed geological data are input into a three-dimensional geological model to generate a siliceous rock thickness spatial distribution map;

[0010] The siliceous rock thickness spatial distribution map is analyzed to obtain a siliceous rock thickness change area;

[0011] Based on the siliceous rock thickness change area, a barite deposition center candidate area is identified;

[0012] The barite deposition center candidate area is analyzed by using spatial analysis technology to obtain the spatial position of the barite deposition center.

[0013] As a preferred scheme of the method for determining the barite sedimentary center by using the thickness of siliceous rock, the seismic wave reflection data includes the arrival time of reflected wave, the reflected wave velocity, the reflected horizon and the reflected amplitude, the geological data includes the drilling data, the geological profile data and the geological structure data, and the collected data is subjected to data denoising and standardization processing.

[0014] As a preferred scheme of the method for determining the barite sedimentary center by using the thickness of siliceous rock, the thickness of the siliceous rock layer is predicted based on the processed seismic wave reflection data, including the following steps,

[0015] The propagation time information of the reflected wave is obtained by combining the arrival time of the reflected wave with the depth of the reflected layer;

[0016] The reflected layer interface is identified based on the propagation time information of the reflected wave, the reflected amplitude and the change of the reflected wave velocity;

[0017] The thickness of the siliceous rock layer is obtained by combining the identified reflected layer interface with the depth of each layer of the reflected layer.

[0018] As a preferred scheme of the method for determining the barite sedimentary center by using the thickness of siliceous rock, the three-dimensional geological model is established based on the geological data and the thickness of the siliceous rock, including the following steps,

[0019] The stratigraphic boundary surface is generated by using the Kriging interpolation method based on the drilling data and the geological profile;

[0020] The fault surface is identified by using the fitting method to perform surface fitting on the geological structure data;

[0021] The upper and lower boundaries of the siliceous rock layer are generated by adding the thickness of the siliceous rock layer to the stratigraphic framework;

[0022] The three-dimensional geological model is generated by integrating the stratigraphic boundary surface, the fault surface and the upper and lower boundaries of the siliceous rock layer.

[0023] As a preferred scheme of the method for determining the barite sedimentary center by using the thickness of siliceous rock, the thickness of the siliceous rock layer and the preprocessed geological data are input into the three-dimensional geological model to generate the spatial distribution map of the thickness of the siliceous rock, including the following steps,

[0024] The drilling data and the geological profile data are extracted from the preprocessed geological data, and 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, and the expression is,

[0025] ;

[0026] wherein, is the probability distribution of the thickness of the siliceous rock layer at the spatial point , is the mean of the thickness of the siliceous rock layer at the spatial point , is the actual value of the thickness of the siliceous rock layer at the spatial point , is the standard deviation of the thickness of the siliceous rock layer, is the weight of the drilling data at the spatial point , is the weight of the geological profile data at the spatial point , is the weight of the seismic wave reflection data at the spatial point ;

[0027] visualize the probability of the spatial distribution of the thickness of the siliceous rock to generate a spatial distribution map of the thickness of the siliceous rock.

[0028] As a preferred scheme of the method for determining the barite deposition center by using the thickness of the siliceous rock, the spatial distribution map of the thickness of the siliceous rock is analyzed to obtain the thickness variation region of the siliceous rock, including the following steps,

[0029] using the gradient method to analyze the difference of the thickness of the siliceous rock in the spatial distribution map of the thickness of the siliceous rock to identify the region of the thickness of the siliceous rock with large thickness variation, and the expression is,

[0030] ;

[0031] wherein, is the gradient value of the thickness of the siliceous rock, is the partial derivative in the direction of , is the partial derivative in the direction of , is the partial derivative in the direction of , is the change rate of the thickness of the siliceous rock in the horizontal direction , is the change rate of the thickness of the siliceous rock in the horizontal direction , is the change rate of the thickness of the siliceous rock in the horizontal direction ;

[0032] a gradient threshold is set by the gradient of the thickness of the siliceous rock, and when the gradient of the thickness of the siliceous rock is greater than the gradient threshold, the region with large thickness variation of the siliceous rock is considered, and the thickness variation region of the siliceous rock is obtained.

[0033] As a preferred scheme of the method for determining the barite deposition center by using the thickness of siliceous rock, wherein: the step of identifying the barite deposition center candidate area based on the thickness variation area of the siliceous rock comprises the following steps,

[0034] The spatial overlap analysis method is used to check whether the thickness variation area of the siliceous rock overlaps with the geological data distribution, and the overlapped part is the barite deposition center candidate area;

[0035] The thickness variation area of the siliceous rock is combined with the seismic wave reflection data, the barite deposition depth is obtained through the seismic reflection interface, and the barite deposition center candidate area is found in the range of the barite deposition depth with a larger thickness variation of the siliceous rock layer;

[0036] The relationship between the thickness variation of the siliceous rock layer and the barite deposition environment is analyzed, and the thickness variation area of the siliceous rock layer is the barite deposition center candidate area if it meets the conditions of the barite deposition environment.

[0037] As a preferred scheme of the method for determining the barite deposition center by using the thickness of siliceous rock, wherein: the step of identifying the barite deposition center candidate area based on the thickness variation area of the siliceous rock comprises the following steps,

[0038] The spatial analysis technique is used to analyze the barite deposition center candidate area, and the spatial position of the barite deposition center is obtained, and the expression is,

[0039] ;

[0040] Wherein, is the barite deposition center position of the candidate area, is the thickness gradient value of the siliceous rock of the candidate area, is the barite deposition depth characteristic value of the candidate area, is the barite mineral content of the candidate area, is the barite deposition depth adaptation value of the candidate area, is the geological data characteristic of the candidate area, is the thickness gradient weight adjustment coefficient of the siliceous rock, is the barite deposition depth adjustment coefficient, is the characteristic adjustment coefficient of the geological data, is the adjustment coefficient of the thickness gradient value of the siliceous rock of the candidate area.

[0041] ​​​​​​​In a second aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program, when executed by the processor, implements any step of the method for determining a barite deposition center using siliceous rock thickness according to the first aspect of the present application.

[0042] In a third aspect, the present application provides a computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements any step of the method for determining a barite deposition center using siliceous rock thickness according to the first aspect of the present application.

[0043] The present application has the following beneficial effects: by collecting seismic wave reflection data and geological data, performing denoising and standardization processing, providing high-quality input data for subsequent calculation, based on the processed seismic wave data, identifying the reflection layer interface through propagation time, amplitude and wave velocity change, and combining the reflection layer depth to accurately predict the siliceous rock layer thickness, then using the siliceous rock thickness and multi-source geological data to construct a three-dimensional geological model, using Kriging interpolation to generate the stratum boundary, combining the fitting to identify the fault surface, and integrating to generate a complete three-dimensional structure framework, inputting the thickness data into the model, combining the multi-weight factor to generate the siliceous rock thickness spatial distribution map and visualization, analyzing the thickness change area through gradient, combining the spatial overlap analysis and the sedimentary environment condition to further identify the barite deposition center candidate area, using the spatial analysis technology to optimize the candidate area with multiple parameters, and accurately determining the barite deposition center position. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0045] Figure 1 The flowchart of the method for determining a barite deposition center using siliceous rock thickness in embodiment 1.

[0046] Figure 2 The schematic diagram of establishing a three-dimensional geological model in embodiment 1. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0048] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced without the specific details. In other instances, well-known methods have not been described in detail in order not to unnecessarily obscure aspects of the present application. The present application is not limited to the embodiments described herein which can be practiced with or without the same.

[0049] It is to be understood that the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. "Coupled" is defined as connected, whether directly or indirectly, through one or more intermediaries, and is not necessarily limited to physical or mechanical connections. The terms "program" or "software" are used herein to generally refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects associated with the present application. However, the present application should not be construed as being limited only to hardware implementations. For example, the present application can be implemented in hardware, firmware, software, or any combination thereof.

[0050] Embodiment 1, Reference Figure 1 and Figure 2 The first embodiment of the present application provides a method for determining the center of barite deposition by using the thickness of siliceous rock, comprising the following steps:

[0051] S1, collecting seismic wave reflection data and geological data, and preprocessing.

[0052] S1.1, the seismic wave reflection data includes the arrival time of the reflected wave, the reflected wave speed, the reflected horizon and the reflected amplitude, the geological data includes the drilling data, the geological profile data and the geological structure data, the collected data is processed by data denoising and standardization.

[0053] Further, the time of the reflected wave reaching the ground surface is collected and recorded by a seismic exploration device (such as a seismic detector), the propagation speed of the reflected wave in the medium is obtained by using the velocity analysis method, the reflected layer interface is identified according to the reflected signal in the seismic profile, and the depth and position of the interface are marked to obtain the reflected horizon, the amplitude of the reflected wave signal is extracted as a parameter reflecting the properties 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 by geological mapping to obtain geological profile data, and the faults and folds in the region are collected to obtain geological structure data.

[0054] The frequency components of the seismic wave signal are analyzed by using fast Fourier transform, high-frequency noise or low-frequency interference is filtered out, the abnormal points (such as measurement errors or invalid data) in the drilling data are cleaned, the linear interpolation method is used to fill in the missing data, the geological profile and structure data are smoothed to eliminate random errors introduced in the measurement process, the different reflected wave amplitude values are normalized to eliminate amplitude fluctuations caused by differences in the sensitivity of the seismic equipment or environmental noise, and the geological data is classified and coded or numerically normalized.

[0055] S2, predicting the thickness of the siliceous rock layer based on the processed seismic wave reflection data.

[0056] S2.1, obtain the propagation time information of the reflected wave by combining the arrival time of the reflected wave with the depth of the reflection layer.

[0057] Further, by selecting the first arrival time of the seismic wave using the arrival time of the reflected wave and the depth information of the reflection layer in the seismic wave reflection data, and by calculating the propagation time for different reflection points at different depths to establish a time-depth relationship curve, the time information of the seismic wave can be accurately associated with the spatial depth, and the propagation time information of the reflected wave can be obtained.

[0058] S2.2, identify the reflection layer interface based on the change of the propagation time information, the reflection amplitude and the reflection wave velocity of the reflected wave.

[0059] Further, by extracting the time point of the reflected signal from the propagation time information, and by combining the change characteristics of the reflection amplitude and the wave velocity, different reflection layer interfaces can be identified. The amplitude of the reflected wave usually changes significantly at the interface, and the sudden change in wave velocity also reflects the difference in the properties of the stratum. By automatically detecting the extreme points of the amplitude curve and overlapping the position of the wave velocity change, the position of the reflection layer interface can be accurately identified.

[0060] S2.3, obtain the thickness of the siliceous rock layer by combining the identified reflection layer interface with the depth of each layer of the reflection layer.

[0061] Further, after identifying the reflection layer interface, the depth difference between adjacent reflection layer interfaces is measured, and for a seismic profile with multiple layers, the reflection layer with siliceous rock is selected to obtain the thickness distribution of the siliceous rock layer.

[0062] S3, establish a three-dimensional geological model based on geological data and the thickness of the siliceous rock.

[0063] S3.1, generate a stratum boundary surface using the Kriging interpolation method based on drilling data and geological profiles.

[0064] Further, according to the drilling data and the geological profile, the depth information of the stratum boundary is extracted, and the Kriging interpolation method is used to generate a continuous stratum boundary surface. First, the depth values of the top and bottom boundaries of the stratum in the drilling data are collected, and the dip angle 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 drilling points is used to interpolate to generate a curved surface model of the stratum boundary, ensuring that the accuracy of the boundary surface is higher near the drilling points. The boundary surface is smoothed to eliminate local anomalies in the interpolation process, and the stratum boundary surface is obtained.

[0065] S3.2, use a fitting method to perform surface fitting on the geological structure data to identify fault surfaces.

[0066] Furthermore, the fault point and fault line information in the geological structure data is used to accurately identify the fault plane through surface fitting methods, extract the spatial coordinates of the fault points and the extension direction of the fault lines, construct a fault spatial distribution data set, and use polynomial fitting to perform surface fitting on the fault points 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.

[0067] S3.3. Use the thickness of the siliceous rock layer to add to the stratigraphic framework to generate the upper and lower boundaries of the siliceous rock layer.

[0068] Furthermore, taking the top interface in the stratigraphic framework as a reference, the thickness values ​​of the siliceous rock layer are superimposed on the top interface point by point to calculate the depth of the bottom interface. 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.

[0069] S3.4. Integrate the stratum boundary surfaces, fault planes, and upper and lower boundaries of the siliceous rock layer to generate a three-dimensional geological model.

[0070] Furthermore, the intersection point calculation method is used to determine the intersection line between the stratigraphic boundary and the fault plane in space, ensuring that the cutting and dislocation characteristics of the fault on the stratigraphic layer are correctly expressed, and the upper and lower boundaries of the siliceous rock layer are embedded in the stratigraphic framework. The spatial relationship between the siliceous rock layer and the fault and other stratigraphic boundaries is checked, and any possible overlap or dislocation problems are corrected. Finally, all the data are integrated into a three-dimensional geological model using three-dimensional modeling software (such as GOCAD or Petrel).

[0071] 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.

[0072] 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:

[0073] ;

[0074] in, is the thickness of the siliceous rock layer at a spatial point The probability distribution of Space Point The average thickness of the siliceous rock layer on For spatial points The actual value of the thickness of the siliceous rock layer on the standard deviation of the thickness of the siliceous rock layer, the weight of the drilling data at the spatial point , the weight of the geological profile data at the spatial point , the weight of the seismic wave reflection data at the spatial point .

[0075] Further, the thickness value of the siliceous rock layer at each drilling point and its spatial position are extracted from the drilling data, the weight of the drilling data is calculated, the weight is determined by the data point density or the distance inverse method, the horizon change and thickness information of the siliceous rock layer in the geological profile are extracted, the spatial surface data of the profile is generated, and the profile weight is calculated, the profile weight can be calculated by the distance from the profile point to the spatial point, based on the seismic wave reflection data, the spatial grid data is generated combined with the wave velocity and amplitude characteristics and the weight is assigned, and the thickness of the siliceous rock layer is calculated using a given formula.

[0076] S4.2 Visualize the probability distribution of the thickness of the siliceous rock in space, and generate a thickness distribution map of the siliceous rock in space

[0077] Further, the probability distribution of the thickness of the siliceous rock layer at the spatial point is converted into continuous three-dimensional grid data by interpolation algorithm, the grid data is rendered by using geological modeling software, the high probability area is represented by higher brightness or specific color, and the low probability area is represented by lower brightness or other color, finally, the distribution map is verified by combining the geological profile and drilling data, to ensure that the visualization result accurately expresses the spatial distribution characteristics of the thickness of the siliceous rock layer, so as to intuitively show the distribution rule of the siliceous rock layer in three-dimensional space.

[0078] S5, analyze the thickness distribution map of the siliceous rock to obtain the thickness change area of the siliceous rock.

[0079] S5.1, use the gradient method to analyze the difference of the thickness of the siliceous rock in the thickness distribution map of the siliceous rock, to identify the thickness change area of the siliceous rock with large thickness change, the expression is,

[0080] ;

[0081] wherein, is the gradient value of the thickness of the siliceous rock layer, is the partial derivative in the direction of , is the partial derivative in the direction of , is the partial derivative in the direction of , is the change rate of the thickness of the siliceous rock in the horizontal direction , the rate of change of the thickness of the siliceous rock in the horizontal direction, the rate of change of the thickness of the siliceous rock in the horizontal direction, the rate of change of the thickness of the siliceous rock in the horizontal direction. the rate of change of the thickness of the siliceous rock in the horizontal direction.

[0082] Further, the area with large thickness change of the siliceous rock is identified, the rate of change of the thickness of the siliceous rock in three directions at a spatial point is analyzed by the gradient method, the gradient value of the thickness of the siliceous rock layer is calculated, the size of the gradient value of the thickness of the siliceous rock layer is analyzed after the gradient value of the thickness of the siliceous rock layer is calculated, the place with large gradient value of the thickness of the siliceous rock layer means that the thickness of the siliceous rock layer changes greatly, therefore, the size of the gradient value is proportional to the intensity of the thickness change of the siliceous rock, and the high gradient value indicates that the thickness of the siliceous rock layer changes greatly.

[0083] S5.2, a gradient threshold is set by the gradient of the thickness of the siliceous rock layer, when the gradient of the thickness of the siliceous rock layer is greater than the gradient threshold, the area with large thickness change of the siliceous rock layer is obtained.

[0084] Further, the gradient threshold is set by the gradient of the thickness of the siliceous rock layer, when the gradient of the thickness of the siliceous rock layer is greater than the gradient threshold, the area with large thickness change of the siliceous rock layer is marked as the area with large thickness change, the area corresponds to the sedimentary center, the fault or the strong stratigraphic change zone, and the change area is marked, and the spatial distribution map of the thickness change area of the siliceous rock is generated.

[0085] S6, based on the thickness change area of the siliceous rock, the barite sedimentary center candidate area is identified.

[0086] S6.1, the spatial overlap analysis method is used to check whether the thickness change area of the siliceous rock overlaps with the geological data distribution, and the overlapping part is the barite sedimentary center candidate area.

[0087] Further, the spatial overlap analysis method is used to superimpose and analyze the thickness change area of the siliceous rock and the known geological data distribution, the thickness change area of the siliceous rock is taken as the spatial input, the range and position of the overlapping area and the correlation with the geological conditions are analyzed, the thickness change area of the siliceous rock overlaps with the key geological data (such as the known barite mine), and then these overlapping areas are determined as the potential barite sedimentary center candidate area.

[0088] S6.2, the thickness change area of the siliceous rock is combined with the seismic wave reflection data, the barite deposition depth is obtained through the seismic reflection interface, and the larger thickness change of the siliceous rock layer in the barite deposition depth range is found, which is the barite sedimentary center candidate area.

[0089] Furthermore, the areas of siliceous rock thickness variation are combined with seismic wave reflection data, and the specific depth range of barite deposition is analyzed through the seismic reflection interface. The specific layers or depth intervals where barite deposition may exist are determined through the reflection time-depth conversion of the seismic data. Within these depth ranges, the areas of siliceous rock thickness variation are checked to see whether they show significant thickness variation. If a large gradient variation in siliceous rock thickness is found within the barite deposition depth range, these areas can be further marked as candidate areas for barite deposition centers.

[0090] S6.3. Analyze the relationship between the thickness variation of the siliceous rock layer and the barite depositional environment. If the area where the thickness variation of the siliceous rock layer meets the barite depositional environment conditions, it is a candidate area for the barite depositional center.

[0091] Furthermore, an in-depth analysis of the sedimentary environmental conditions in the area where the siliceous rock thickness changes was conducted to determine whether it met the sedimentary environmental characteristics of barite. Combined with the regional geological background, the focus was on analyzing whether the area where the siliceous rock thickness changes was 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 was evaluated whether the change in siliceous rock thickness was consistent with the formation process of barite deposition. The area where the siliceous rock thickness changes and meets the barite sedimentary environmental conditions was identified as a candidate area for barite deposition center.

[0092] S7. Analyze the candidate area of ​​the barite deposition center using spatial analysis technology to obtain the spatial location of the barite deposition center.

[0093] The siliceous rock thickness gradient value, barite deposition depth and drilling data were extracted from the candidate area of ​​barite deposition center. The extracted data were analyzed using spatial analysis technology to obtain the spatial position of the barite deposition center. The expression is:

[0094] ;

[0095] 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 adapted to the value. For the The characteristics of geological data at each point, is the siliceous rock thickness gradient weight adjustment coefficient, a depth adjustment coefficient of barite deposition, a feature adjustment coefficient of geological data, a depth adjustment coefficient of barite deposition, a depth adjustment coefficient of barite deposition,

[0096] Further, the gradient method is used to obtain the thickness gradient value of each point of the siliceous rock. The gradient value represents the variation intensity of the thickness of the siliceous rock in space. The deposition depth of the barite is extracted from the geological profile and the drilling data. The mineral content of the barite at each point is obtained based on the mineral analysis data. The deposition depth adaptation value of the barite at each point is obtained by matching with the geological conditions (such as the deposition rate, the deposition environment, etc.). The geological features of each point are extracted from the geological survey data, the seismic reflection data, etc.

[0097] is the depth of the barite deposition, which directly affects the location of the barite deposit. A larger value indicates a deep deposit, and a smaller value indicates a deposit located in a shallower stratum. is the content of the barite mineral. A higher mineral content represents that the deposition center of the point can form a barite deposit.

[0098] The embodiment also provides a computer device suitable for the method for judging the barite deposition center by using the thickness of the siliceous rock, which comprises a memory and a processor. The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the method for judging the barite deposition center by using the thickness of the siliceous rock proposed in the above embodiment.

[0099] The computer device can be a terminal. The computer device comprises a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises 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 external terminals in a wired or wireless manner. The wireless manner can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. 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 overlaid on the display screen, or a key, a trackball or a touchpad arranged on the shell of the computer device. In addition, the input device can be an external keyboard, a touchpad or a mouse, etc.

[0100] The embodiment also provides a storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method for determining the barite deposition center by using the thickness of siliceous rock according to the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk, or an optical disk.

[0101] To sum up, the present application collects seismic wave reflection data and geological data, performs denoising and standardization processing, and provides high-quality input data for subsequent calculation, identifies the reflection layer interface through the propagation time, amplitude and wave velocity change based on the processed seismic wave data, accurately predicts the thickness of the siliceous rock layer in combination with the depth of the reflection layer, then constructs a three-dimensional geological model by using the thickness of the siliceous rock and multi-source geological data, generates a stratigraphic boundary by using Kriging interpolation, identifies a fault surface in combination with fitting, integrates to generate a complete three-dimensional structure framework, inputs the thickness data into the model, generates a spatial distribution map of the thickness of the siliceous rock and visualizes the spatial distribution map in combination with multi-weight factors, analyzes the thickness change area by using gradient analysis, further identifies a candidate area of the barite deposition center in combination with spatial overlap analysis and sedimentary environment conditions, and accurately determines the position of the barite deposition center by using spatial analysis technology to perform multi-parameter optimization on the candidate area.

[0102] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A method for determining a barite depositional center using siliceous rock thickness, the method comprising: The application relates to a method for identifying a barite sedimentary center, comprising the following steps: ​ collecting seismic wave reflection data and geological data and performing pretreatment; predicting the thickness of siliceous rock layers based on the processed seismic wave reflection data; inputting the thickness of the siliceous rock layers and the pretreated geological data into a three-dimensional geological model to generate a spatial distribution diagram of the thickness of the siliceous rock layers; analyzing the spatial distribution diagram of the thickness of the siliceous rock layers to obtain a thickness variation region of the siliceous rock layers; based on the thickness variation region of the siliceous rock layers, a barite sedimentary center candidate region is identified; using spatial analysis technology to analyze the barite sedimentary center candidate region to obtain the spatial position of the barite sedimentary center; the above-mentioned analysis of the spatial distribution diagram of the thickness of the siliceous rock layers to obtain the thickness variation region of the siliceous rock layers comprises the following steps: using a gradient method to analyze the difference in the thickness of the siliceous rock layers in the spatial distribution diagram of the thickness of the siliceous rock layers to identify the region with large thickness variation of the siliceous rock layers, and the expression is as follows: ； in, is the gradient value of the siliceous rock thickness, for The partial derivative in the direction, for The partial derivative in the direction, for The partial derivative in the direction, The thickness of siliceous rock in the horizontal direction The rate of change on The thickness of siliceous rock in the horizontal direction The rate of change on The thickness of siliceous rock in the horizontal direction The rate of change of a gradient threshold value is set through the gradient of the thickness of the siliceous rock layers, and when the gradient of the thickness of the siliceous rock layers is greater than the gradient threshold value, the region with large thickness variation of the siliceous rock layers is regarded as a region with large thickness variation of the siliceous rock layers, and the thickness variation region of the siliceous rock layers is obtained; the above-mentioned analysis of the barite sedimentary center candidate region using spatial analysis technology to obtain the spatial position of the barite sedimentary center comprises the following steps: the thickness gradient value of the siliceous rock layers, the barite sedimentary depth and the drilling data are extracted from the barite sedimentary center candidate region, and the extracted data are analyzed using spatial analysis technology to obtain the spatial position of the barite sedimentary center, and the expression is as follows: ； wherein, is a characteristic value of the barite deposition depth of the candidate area, is a characteristic value of the barite deposition center position of the candidate area, is a characteristic value of the siliceous rock thickness gradient of the candidate area, is a characteristic value of the barite mineral content of the candidate area, is a characteristic value of the barite deposition depth of the candidate area, is a characteristic value of the barite mineral content of the candidate area, is a characteristic value of the barite deposition depth of the candidate area, is a characteristic value of the barite mineral content of the candidate area, is a characteristic value of the barite deposition depth of the candidate area, is a characteristic value of the barite mineral content of the candidate area, is a characteristic value of the geological data of the candidate area, is a characteristic value of the barite mineral content of the candidate area, is a weight adjustment coefficient of the siliceous rock thickness gradient, is a barite deposition depth adjustment coefficient, is a characteristic adjustment coefficient of the geological data, is a characteristic value of the barite mineral content of the candidate area, is a characteristic value of the barite mineral content of the candidate area.

2. The method for determining a center of barite deposition using chert thickness as recited in claim 1, wherein: the above-mentioned collecting seismic wave reflection data and geological data and performing pretreatment comprises the following steps: the seismic wave reflection data comprises the arrival time of the reflected wave, the reflected wave velocity, the reflected horizon and the reflected amplitude, and the geological data comprises drilling data, geological profile data and geological structure data, and the collected data are subjected to data denoising and standardization treatment.

3. The method for determining a center of barite deposition using chert thickness as recited in claim 1, wherein: the above-mentioned prediction of the thickness of the siliceous rock layers based on the processed seismic wave reflection data comprises the following steps: the propagation time information of the reflected wave is obtained through the arrival time of the reflected wave and the depth of the reflected horizon; the reflected horizon interface is identified based on the propagation time information of the reflected wave, the reflected amplitude and the change of the reflected wave velocity; the thickness of the siliceous rock layers is obtained through the identified reflected horizon interface and the depth of each layer of the reflected horizon.

4. The method for determining a center of barite deposition using siliceous rock thickness as claimed in claim 1, wherein: the above-mentioned establishment of a three-dimensional geological model based on the geological data and the thickness of the siliceous rock layers comprises the following steps: a stratum boundary surface is generated by adopting the Kriging interpolation method based on the drilling data and the geological profile; a fault surface is identified by using a fitting method to perform surface fitting on the geological structure data; the upper and lower boundaries of the siliceous rock layers are generated by adding the thickness of the siliceous rock layers to the stratum framework; the three-dimensional geological model is generated by integrating the stratum boundary surface, the fault surface and the upper and lower boundaries of the siliceous rock layers.

5. The method for determining a center of barite deposition using chert thickness as recited in claim 1, wherein: the above-mentioned inputting the thickness of the siliceous rock layers and the pretreated geological data into the three-dimensional geological model to generate a spatial distribution diagram of the thickness of the siliceous rock layers comprises the following steps: the drilling data and the geological profile data are extracted from the pretreated geological data, the thickness of the siliceous rock layers, the drilling data and the geological profile data are inputted into the three-dimensional geological model to obtain the spatial distribution probability of the thickness of the siliceous rock layers, and the expression is as follows: ； wherein, is a probability distribution of the thickness of the siliceous rock layer at a spatial point , is a mean value of the thickness of the siliceous rock layer at a spatial point , is an actual value of the thickness of the siliceous rock layer at a spatial point , is a standard deviation of the thickness of the siliceous rock layer, is a weight of the borehole data at a spatial point , is a weight of the geological profile data at a spatial point , is a weight of the seismic wave reflection data at a spatial point ; the spatial distribution probability of the thickness of the siliceous rock layers is visualized to generate a spatial distribution diagram of the thickness of the siliceous rock layers.

6. The method for determining a center of barite deposition using siliceous rock thickness as recited in claim 1, wherein: Based on the thickness variation area of siliceous rock, the following steps are used to identify the barite sedimentary center candidate area, The spatial overlap analysis method is used to check whether the thickness variation area of siliceous rock overlaps with the distribution of geological data, and the overlapping part is the barite sedimentary center candidate area; The thickness variation area of siliceous rock is combined with the seismic wave reflection data, the barite sedimentary depth is obtained through the seismic reflection interface, and the barite sedimentary center candidate area is found in the range of the barite sedimentary depth where the thickness of the siliceous rock layer changes greatly. The relationship between the thickness variation of the siliceous rock layer and the barite sedimentary environment is analyzed, and the thickness variation area of the siliceous rock layer is consistent with the barite sedimentary environment condition, which is the barite sedimentary center candidate area. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the method for determining the barite sedimentary center using the thickness of siliceous rock according to any one of claims 1-6.

8. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the method for determining the barite sedimentary center using the thickness of siliceous rock according to any one of claims 1-6.

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