Geologic body identification method and device, equipment and storage medium

Abstract

The invention provides a geologic body identification method and device, equipment and a storage medium, and belongs to the technical field of seismic exploration. The method comprises the following steps: acquiring a plurality of reflection coefficients; adjusting the plurality of reflection coefficients based on a geologic body category to obtain a reflection data body; processing the reflection data volume based on a three-dimensional point spread function to obtain a sample seismic data volume; training a geologic body identification model based on the sample seismic data body; and based on the trained geologic body identification model, predicting a target seismic data body to obtain the position of the geologic body belonging to the geologic body category in the target stratum area. According to the technical scheme, the recognition performance of the geologic body recognition model can be improved, and when the target seismic data volume obtained through exploration is predicted based on the trained geologic body recognition model, the position of the geologic body can be recognized more accurately.

Description

Technical Field

[0001] The present application relates to the field of seismic exploration technology, and in particular to a method, device, equipment and storage medium for identifying a geological body. Background Art

[0002] In the field of seismic exploration technology, the interpretation of geological bodies such as caves and collapses is the basic work of carbonate reservoir seismic data interpretation. Among them, "interpretation" can refer to the identification of the location of geological bodies such as caves or collapses in the formation. How to identify the location of caves or collapses based on seismic data is the focus of research in this field.

[0003] In related technologies, traditional cave interpretation is mostly done manually. Interpreters generally use seismic attributes to assist in the interpretation of caves and collapses. However, the efficiency of manual interpretation is very low and requires a lot of time and effort. With the continuous deepening of oil and gas exploration, the amount of seismic data has also increased, and the efficiency of manual interpretation has been unable to meet production needs. Summary of the invention

[0004] The embodiment of the present application provides a method, device, equipment and storage medium for identifying a geological body, which improves the accuracy of geological body identification. The technical solution is as follows:

[0005] In one aspect, a method for identifying a geological body is provided, the method comprising:

[0006] Acquire a plurality of reflection coefficients, wherein the plurality of reflection coefficients are used to simulate the properties of reflected seismic wavelets at a plurality of locations in a plurality of strata in a three-dimensional space;

[0007] Based on the geological body category, the multiple reflection coefficients are adjusted to obtain a reflection data volume, wherein the reflection data volume is used to simulate the reflection characteristics of the geological body belonging to the geological body category;

[0008] Processing the reflection data volume based on a three-dimensional point spread function to obtain a sample seismic data volume, wherein the three-dimensional point spread function is used to represent the propagation properties of seismic wavelets in time and space, and the sample seismic data volume is used to simulate the characteristics of a geological body belonging to the geological body category;

[0009] Based on the sample seismic data body, a geological body recognition model is trained, and the trained geological body recognition model is used to identify the position of the geological body belonging to the geological body category in the stratum;

[0010] Based on the trained geological body recognition model, a target seismic data body is predicted to obtain the position of a geological body belonging to the geological body category in a target stratigraphic region, wherein the target seismic data body is obtained based on the exploration of the target stratigraphic region.

[0011] In some embodiments, the adjusting the multiple reflection coefficients based on the geological body type to obtain the reflection data body includes:

[0012] Performing wrinkle deformation on the multiple reflection coefficients to obtain an intermediate data volume, wherein the intermediate data volume is used to simulate the reflection characteristics of a real stratum structure;

[0013] Based on the geological body category, a plurality of target reflection coefficients in the intermediate data body are adjusted to obtain the reflection data body, and the positions of the target reflection coefficients are used to indicate the positions of the geological bodies belonging to the geological body category.

[0014] In some embodiments, adjusting a plurality of target reflection coefficients in the intermediate data volume based on the geological volume category to obtain the reflection data volume includes:

[0015] In the case where the geological body type is a karst cave, a plurality of first target reflection coefficients and a plurality of second target reflection coefficients are obtained, wherein the positions of the first target reflection coefficients are used to represent the edge of the karst cave, and the positions of the second target reflection coefficients are used to represent the inside of the karst cave, and the plurality of first target reflection coefficients surround the plurality of second target reflection coefficients;

[0016] The plurality of first target reflection coefficients are adjusted, wherein the value of any first target reflection coefficient after adjustment is different from the values ​​of other reflection coefficients of the same formation;

[0017] adjusting the values ​​of the plurality of second target reflection coefficients to zero;

[0018] The reflection data volume is obtained based on the adjusted plurality of first target reflection coefficients and the plurality of second target reflection coefficients.

[0019] In some embodiments, adjusting a plurality of target reflection coefficients in the intermediate data volume based on the geological volume category to obtain the reflection data volume includes:

[0020] In the case where the geological body category is collapse, a plurality of third target reflection coefficients are obtained, and the positions of the third target reflection coefficients are used to represent the edge of the collapse;

[0021] The positions of the plurality of third target reflection coefficients are adjusted downward by a preset distance to obtain the reflection data volume, where the preset distance is used to represent the depth of the collapse.

[0022] In some embodiments, the processing of the reflection data volume based on the three-dimensional point spread function to obtain a sample seismic data volume includes:

[0023] Determining the three-dimensional point spread function based on an aperture parameter and a frequency parameter, wherein the aperture parameter and the frequency parameter are used to reflect the exploration effect of the three-dimensional point spread function on various positions in the formation;

[0024] The reflection data volume is convolved based on the three-dimensional point spread function to obtain the sample seismic data volume.

[0025] In some embodiments, the method further comprises:

[0026] Adjusting at least one of the aperture parameter and the frequency parameter to obtain a new three-dimensional point spread function;

[0027] The reflection data volume is convolved based on the new three-dimensional point spread function to obtain a new sample seismic data volume.

[0028] In some embodiments, the training of the geological body recognition model based on the sample seismic data body includes:

[0029] Inputting the sample seismic data body into the geological body identification model to obtain seismic data features;

[0030] Based on the seismic data features, identifying the location of the geological body belonging to the geological body category in the sample seismic data volume;

[0031] The geological volume recognition model is trained based on the difference between the position of the identified geological volume and the annotated position of the sample seismic data volume, the annotated position being determined based on the adjusted reflection coefficient.

[0032] On the other hand, a geological body identification device is provided, the device comprising:

[0033] An acquisition module, used for acquiring a plurality of reflection coefficients, wherein the plurality of reflection coefficients are used for simulating the properties of reflected seismic wavelets at a plurality of positions in a plurality of strata in a three-dimensional space;

[0034] A first adjustment module is used to adjust the multiple reflection coefficients based on the geological body category to obtain a reflection data body, wherein the reflection data body is used to simulate the reflection characteristics of the geological body belonging to the geological body category;

[0035] A processing module, used for processing the reflection data volume based on a three-dimensional point spread function to obtain a sample seismic data volume, wherein the three-dimensional point spread function is used to represent the propagation properties of seismic wavelets in time and space, and the sample seismic data volume is used to simulate the characteristics of a geological body belonging to the geological body category;

[0036] A training module, used for training a geological body recognition model based on the sample seismic data body, wherein the trained geological body recognition model is used for identifying the position of a geological body belonging to the geological body category in a stratum;

[0037] The prediction module is used to predict the target seismic data body based on the trained geological body recognition model to obtain the position of the geological body belonging to the geological body category in the target stratigraphic area, and the target seismic data body is obtained based on the exploration of the target stratigraphic area.

[0038] In some embodiments, the first adjustment module includes:

[0039] A deformation unit, used for performing fold deformation on the multiple reflection coefficients to obtain an intermediate data volume, wherein the intermediate data volume is used to simulate the reflection characteristics of a real stratum structure;

[0040] The adjustment unit is used to adjust multiple target reflection coefficients in the intermediate data volume based on the geological body category to obtain the reflection data volume, and the position of the target reflection coefficient is used to represent the position of the geological body belonging to the geological body category.

[0041] In some embodiments, the adjustment unit is used to obtain multiple first target reflection coefficients and multiple second target reflection coefficients when the geological body category is a cave, the position of the first target reflection coefficient is used to represent the edge of the cave, the position of the second target reflection coefficient is used to represent the inside of the cave, and the multiple first target reflection coefficients surround the multiple second target reflection coefficients; adjust the multiple first target reflection coefficients, and the value of any adjusted first target reflection coefficient is different from the values ​​of other reflection coefficients of the same stratum; adjust the values ​​of the multiple second target reflection coefficients to zero; and obtain the reflection data body based on the adjusted multiple first target reflection coefficients and the multiple second target reflection coefficients.

[0042] In some embodiments, the adjustment unit is used to obtain multiple third target reflection coefficients when the geological body category is collapse, and the positions of the third target reflection coefficients are used to indicate the edge of the collapse; the positions of the multiple third target reflection coefficients are adjusted downward by a preset distance to obtain the reflection data body, and the preset distance is used to indicate the depth of the collapse.

[0043] In some embodiments, the processing module is used to determine the three-dimensional point spread function based on aperture parameters and frequency parameters, and the aperture parameters and the frequency parameters are used to reflect the exploration effect of the three-dimensional point spread function on various positions in the formation; based on the three-dimensional point spread function, the reflection data volume is convolved to obtain the sample seismic data volume.

[0044] In some embodiments, the apparatus further comprises:

[0045] A second adjustment module, used for adjusting at least one of the aperture parameter and the frequency parameter to obtain a new three-dimensional point spread function;

[0046] The processing module is further used to convolve the reflection data volume based on the new three-dimensional point spread function to obtain the new sample seismic data volume.

[0047] In some embodiments, the training module is used to input the sample seismic data body into the geological body identification model to obtain seismic data characteristics; based on the seismic data characteristics, identify the position of the geological body belonging to the geological body category in the sample seismic data body; based on the difference between the position of the identified geological body and the marked position of the sample seismic data body, train the geological body identification model, and the marked position is determined based on the adjusted reflection coefficient.

[0048] On the other hand, a computer device is provided, which includes a processor and a memory, wherein the memory is used to store at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the geological body identification method in the embodiment of the present application.

[0049] On the other hand, a computer-readable storage medium is provided, in which at least one computer program is stored. The at least one computer program is loaded and executed by a processor to implement the method for identifying a geological body in the embodiment of the present application.

[0050] On the other hand, a computer program is provided, including a computer program, which, when executed by a processor, implements the method for identifying a geological body as described in the embodiments of the present application.

[0051] The embodiment of the present application provides a method for identifying a geological body. According to the category of the geological body to be identified, multiple reflection coefficients are adjusted so that the obtained reflection data body can accurately reflect the reflection characteristics of the geological body in the three-dimensional space; then, the reflection data body is processed by a three-dimensional point spread function so that the obtained sample seismic data body can accurately reflect the characteristics of the geological body in the three-dimensional space, that is, the data of the geological body in the three-dimensional space can be simulated more accurately with higher authenticity; then, a geological body recognition model is trained by the sample seismic data body so that the geological body recognition model can fully learn the characteristics of the geological body, thereby improving the recognition performance of the geological body recognition model, and then when predicting the target seismic data body obtained by exploration based on the trained geological body recognition model, the position of the geological body can be more accurately identified, and compared with manual interpretation, the position of the geological body can be identified more efficiently by using the trained geological body recognition model. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 is a flow chart of a method for identifying a geological body provided according to an embodiment of the present application;

[0054] Figure 2 is a schematic diagram of a wrinkle deformation provided according to an embodiment of the present application;

[0055] Figure 3 is a schematic diagram of a reflection data body for simulating a cave according to an embodiment of the present application;

[0056] Figure 4 It is an effect diagram of processing data using a one-dimensional point spread function and a three-dimensional point spread function respectively provided in an embodiment of the present application;

[0057] Figure 5 It is an imaging effect diagram of a different three-dimensional point spread function provided according to an embodiment of the present application;

[0058] Figure 6 A collapsed sample seismic data volume is provided according to an embodiment of the present application;

[0059] Figure 7 is a schematic diagram of a seismic data feature provided according to an embodiment of the present application;

[0060] Figure 8is a schematic diagram of a geological body identification model provided according to an embodiment of the present application;

[0061] Fig. 9 is a schematic diagram of a residual layer provided according to an embodiment of the present application;

[0062] Fig.10 This is a cave identification effect diagram provided according to an embodiment of the present application;

[0063] Fig.11 It is a collapse identification effect diagram provided according to an embodiment of the present application;

[0064] Fig.12 is a structural schematic diagram of a geological body identification device provided according to an embodiment of the present application;

[0065] Fig.13 is a schematic structural diagram of another geological body identification device provided according to an embodiment of the present application;

[0066] Fig.14 is a structural block diagram of a terminal provided according to an embodiment of the present application;

[0067] Fig.15 It is a structural block diagram of a server provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0068] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0069] In this application, the terms "first", "second", etc. are used to distinguish identical or similar items with substantially the same effects and functions. It should be understood that there is no logical or temporal dependency between "first", "second", and "nth", nor is there any limitation on quantity and execution order.

[0070] In the present application, the term "at least one" means one or more, and the term "plurality" means two or more.

[0071] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions. For example, the target seismic data bodies involved in this application are all obtained with full authorization.

[0072] Figure 1 is a flow chart of a method for identifying a geological body provided in an embodiment of the present application, see Figure 1 In the present application, the method for identifying a geological body includes the following steps:

[0073] 101. The terminal obtains a plurality of reflection coefficients, and the plurality of reflection coefficients are used to simulate the properties of reflected seismic wavelets at a plurality of positions in a plurality of strata in a three-dimensional space.

[0074] In the embodiment of the present application, the reflection coefficient refers to the amplitude ratio between the incident seismic wavelet and the reflected seismic wavelet. A seismic wavelet is a signal with a certain starting time, limited energy and a certain continuation length, and is a basic unit in seismic data. A seismic wavelet can be defined by its amplitude spectrum and phase spectrum. Multiple reflection coefficients include reflection coefficients of multiple strata. The reflection coefficient of each stratum is composed of reflection coefficients at multiple positions in the stratum. It can be seen that the multiple reflection coefficients in the embodiment of the present application are used to simulate the reflection coefficients at various positions in the three-dimensional space, and are not only the reflection coefficients in a certain stratum, nor are they only the emission coefficients in a certain stratum profile. In the stratum of three-dimensional space, the reflection coefficients at different positions may be the same or different, and the embodiment of the present application does not limit this.

[0075] In some embodiments, the geological components in the same stratum are roughly the same, and the geological components in different strata are basically different. For example, some strata are mainly limestone, and some strata are mainly dolomite. The properties of seismic wavelets reflected by different geological components are different. Optionally, in the process of obtaining multiple reflection coefficients, the terminal is configured to simulate that the reflection coefficients at different positions in the same stratum are the same, and to simulate that the reflection coefficients at different positions in different strata are different. The solution provided in the embodiment of the present application, by setting the reflection coefficients in the same stratum to be the same and the reflection coefficients in different strata to be different, is not only simple to operate, but can also more accurately reflect the reflection characteristics of different strata, which is conducive to the subsequent simulation of more accurate seismic data.

[0076] In some embodiments, in addition to being randomly generated, multiple reflection coefficients can also be generated according to the real strata to be explored. Accordingly, the terminal can perform acoustic logging on the real strata to be explored. Then, the terminal calculates multiple reflection coefficients based on the acoustic wave and density logging curves. The solution provided in the embodiment of the present application obtains reflection coefficients by exploring the real strata, so that the multiple reflection coefficients can more accurately reflect the reflection characteristics of the strata, which is conducive to the subsequent simulation of more accurate seismic data.

[0077] 102. The terminal adjusts a plurality of reflection coefficients based on the geological body category to obtain a reflection data volume, where the reflection data volume is used to simulate reflection characteristics of a geological body belonging to the geological body category.

[0078] In the embodiment of the present application, the geological body category can be divided into caves and collapses, etc., which are not limited in the embodiment of the present application. Different geological bodies have different reflection characteristics. The terminal can adjust the multiple reflection coefficients generated previously according to the geological body category to generate a reflection data body that can simulate the geological body of the corresponding category, in preparation for the subsequent generation of data for training the geological body recognition model. Among them, the reflection data body refers to the data used to describe the reflection characteristics at multiple positions in the stratum in three-dimensional space.

[0079] Since the real strata are generally horizontal when they are formed, and the real strata will bend due to the force under the action of tectonic movement. A bend can be called a fold, and if a series of wavy bending deformations occur, it can be called a wrinkle. Therefore, in general, the real strata are not flat, but the shape after wrinkling. That is, the positions of multiple reflection coefficients corresponding to the same stratum are not completely aligned. Accordingly, in order to more accurately simulate the geological body in the stratum, the terminal can first perform wrinkle deformation on the multiple reflection coefficients generated previously, and then adjust them according to the geological body category. Accordingly, the terminal adjusts the multiple reflection coefficients based on the geological body category to obtain the reflection data body. The process includes: the terminal performs wrinkle deformation on the multiple reflection coefficients to obtain the intermediate data body. Then, the terminal adjusts the multiple target reflection coefficients in the intermediate data body based on the geological body category to obtain the reflection data body. Among them, the intermediate data body is used to simulate the reflection characteristics of the real stratum structure. The position of the target reflection coefficient is used to indicate the position of the geological body belonging to the geological body category. The position of the target reflection coefficient can be determined randomly or according to the correlation between the geological body category and the stratum structure. For example, some geological bodies tend to appear in places where the stratum is wrinkled and deformed, and some geological bodies tend to appear in places where the stratum is flat, etc. The embodiment of the present application does not limit this. The solution provided in the embodiment of the present application performs wrinkle deformation on multiple reflection coefficients so that the multiple reflection coefficients can more accurately simulate the structure of the real stratum, and then adjusts the target reflection coefficient among the multiple reflection coefficients based on the geological body category, so that the obtained reflection data body can more accurately simulate the reflection characteristics of the geological body, which is conducive to the subsequent training of a geological body recognition model with better recognition performance.

[0080] For example, Figure 2 is a schematic diagram of a wrinkle deformation provided according to an embodiment of the present application. Figure 2 , Figure 2 (a) in FIG. 1 shows an example of an initial reflection data volume, that is, multiple reflection coefficients before adjustment. The multiple reflection coefficients of each layer are located horizontally. Then, the terminal can perform fold deformation on the initial reflection data volume shown in (a) to obtain an intermediate data volume, see Figure 2(b) in the figure. The position of the reflection coefficient of the same layer in the intermediate data volume is not horizontal, but has fold deformation, which is more consistent with the structure of the real stratum. The terminal can also simulate at least one of the inclined structure and the fault structure based on the intermediate data volume, so that the intermediate data volume can more accurately simulate the structure of the real stratum.

[0081] In the process of adjusting multiple target reflection coefficients in the intermediate data volume based on the geological body category to obtain the reflection data volume, there are differences in the process of adjusting the multiple target reflection coefficients by the terminal for different geological body categories.

[0082] In some embodiments, the geological body category is a cave. Accordingly, the terminal adjusts multiple target reflection coefficients in the intermediate data body based on the geological body category, and the process of obtaining the reflection data body includes: when the geological body category is a cave, the terminal obtains multiple first target reflection coefficients and multiple second target reflection coefficients. Then, the terminal adjusts the multiple first target reflection coefficients, and the value of any adjusted first target reflection coefficient is different from the value of other reflection coefficients of the same stratum. Then, the terminal adjusts the values ​​of the multiple second target reflection coefficients to zero. Then, the terminal obtains the reflection data body based on the adjusted multiple first target reflection coefficients and multiple second target reflection coefficients. Among them, the position of the first target reflection coefficient is used to represent the edge of the cave. The position of the second target reflection coefficient is used to represent the inside of the cave, and the multiple first target reflection coefficients surround the multiple second target reflection coefficients. The solution provided in the embodiment of the present application adjusts the reflection coefficients in the intermediate data body according to the structure of the cave and the reflection properties of the cave, so that the obtained reflection data body can more accurately simulate the reflection characteristics of the cave, which is conducive to the subsequent training of a model that can more accurately identify the cave.

[0083] The positions of multiple first target reflection coefficients used to simulate the same cave can form an ellipsoid, and the multiple second target reflection coefficients surrounded by the ellipsoid are zero. That is, the ellipsoid is the simulated cave. The embodiment of the present application does not limit the specific position of the cave, that is, it does not limit the positions of the multiple first target reflection coefficients. Specifically, the terminal can add an irregular ellipsoid with abnormal amplitudes of random spatial distribution on the basis of the intermediate data body to simulate the cave structure. The abnormal amplitude refers to a reflection coefficient that is different from other reflection coefficients in the same layer, or a reflection coefficient that is significantly different from other reflection coefficients in the same layer. The embodiment of the present application does not limit this.

[0084] For example, Figure 3 is a schematic diagram of a reflection data volume for simulating a cave according to an embodiment of the present application. Figure 3 , Figure 3 The cave in the reflection data volume is shown as an example, for example, section 301.

[0085] In some embodiments, the geological body category is collapse. Accordingly, the terminal adjusts multiple target reflection coefficients in the intermediate data body based on the geological body category, and the process of obtaining the reflection data body includes: when the geological body category is collapse, the terminal obtains multiple third target reflection coefficients. Then, the terminal adjusts the positions of the multiple third target reflection coefficients downward by a preset distance to obtain a reflection data body. Among them, the position of the third target reflection coefficient is used to represent the edge of the collapse. The preset distance is used to represent the depth of the collapse. That is, the terminal can simulate the collapse structure by recessing the reflection coefficient of a certain depth at a random position on the basis of the intermediate data body. The embodiment of the present application does not limit the specific position of the collapse, that is, it does not limit the position of the multiple third target reflection coefficients. The solution provided by the embodiment of the present application adjusts the reflection coefficient in the intermediate data body according to the structure of the collapse and the reflection properties of the collapse, so that the obtained reflection data body can more accurately simulate the reflection characteristics of the collapse, which is conducive to the subsequent training of a model that can more accurately identify the collapse.

[0086] 103. The terminal processes the reflection data volume based on the three-dimensional point spread function to obtain a sample seismic data volume. The three-dimensional point spread function is used to represent the propagation properties of seismic wavelets in time and space. The sample seismic data volume is used to simulate the characteristics of the geological body belonging to the geological body category.

[0087] In an embodiment of the present application, a point spread function (PSF) is a function used to evaluate the minimum spatial resolution distance of an imaging system. Point spread refers to the dispersion of light energy after imaging of a point light source. Assuming that we regard an object as consisting entirely of a point, then the image of the object is at least the width of an image point. However, usually the image of an object is composed of spots composed of many image points, and the central part of the spot is the brightest, and the surrounding brightness decreases. Obviously, the more concentrated the spot, the better the imaging effect will be. For the field of seismic exploration, the point spread function refers to the seismic imaging response of a single diffraction point underground, which is the basis for seismic imaging data. When exploring a certain point (position) in the stratum, the terminal can calculate through the point spread function to obtain the seismic data of this point.

[0088] Point spread functions can be divided into two types: one-dimensional point spread function and three-dimensional point spread function. The one-dimensional point spread function mainly represents the properties of the seismic wavelet when it propagates in the stratum along the time direction. The time direction refers to the direction perpendicular to the stratum and downward. The three-dimensional point spread function mainly represents the properties of the seismic wavelet when it propagates in the stratum along the time direction and the spatial direction. The spatial direction refers to the direction parallel to the stratum. Because when the seismic wavelet propagates in the stratum, it not only propagates downward perpendicular to the stratum, but also spreads in the horizontal direction of the stratum. Compared with the one-dimensional point spread function, the embodiment of the present application uses a three-dimensional point spread function to process the reflection data body, so that the obtained sample seismic data body can more accurately reflect the properties of the geological body in three-dimensional space, thereby facilitating the subsequent training of a geological body recognition model with better recognition performance.

[0089] For example, Figure 4 1 is a diagram showing the effect of processing data using a one-dimensional point spread function and a three-dimensional point spread function respectively according to an embodiment of the present application. Figure 4 , Figure 4 (a) in the figure exemplarily shows the sample seismic data obtained by processing the reflection data volume using a one-dimensional point spread function. 401 in the figure (a) is the karst cave structure obtained by processing using a one-dimensional point spread function. Figure 4 (b) in the figure shows, by way of example, a sample seismic data volume obtained by processing a reflection data volume using a three-dimensional point spread function. 402 in the figure (b) is a karst cave structure obtained by processing using a three-dimensional point spread function. It can be seen that the scope of the karst cave structure obtained by processing using a three-dimensional point spread function is smaller, that is, the location of the karst cave is more accurate.

[0090] In some embodiments, the three-dimensional point spread function includes an aperture parameter and a frequency parameter. The aperture parameter and the frequency parameter can affect the imaging effect of the three-dimensional point spread function, that is, can affect the exploration effect of the geological body. The terminal can determine the three-dimensional point spread function based on the aperture parameter and the frequency parameter, so as to simulate the seismic data volume of the geological body. Accordingly, the terminal processes the reflection data volume based on the three-dimensional point spread function to obtain the sample seismic data volume. The process includes: the terminal determines the three-dimensional point spread function based on the aperture parameter and the frequency parameter. Then, the terminal convolves the reflection data volume based on the three-dimensional point spread function to obtain the sample seismic data volume. Among them, the aperture parameter and the frequency parameter are used to reflect the exploration effect of the three-dimensional point spread function on each position in the stratum. The aperture parameter and the frequency parameter can be randomly generated, or they can be empirical parameters, or they can be determined according to the geological body category, and the embodiment of the present application does not limit this. The solution provided in the embodiment of the present application determines the three-dimensional point spread function through aperture parameters and frequency parameters, and then convolves the reflection data volume through the three-dimensional point spread function, so that the obtained sample seismic data volume can more accurately reflect the properties of the geological body in three-dimensional space, thereby facilitating the subsequent training of a geological body recognition model with better recognition performance.

[0091] In some embodiments, the terminal can also adjust the aperture parameter or the frequency parameter to obtain different three-dimensional point spread functions, thereby simulating different sample seismic data volumes. Accordingly, the terminal adjusts at least one of the aperture parameter and the frequency parameter to obtain a new three-dimensional point spread function. Then, the terminal convolves the reflection data volume based on the new three-dimensional point spread function to obtain a new sample seismic data volume. The solution provided in the embodiment of the present application can obtain different three-dimensional point spread functions by adjusting at least one of the aperture parameter and the frequency parameter, thereby simulating different sample seismic data volumes, providing rich training data for subsequent training of the geological body recognition model, which is beneficial to further improve the generalization ability of the geological body recognition model, so that the geological body recognition model can adapt to seismic data and complex geological conditions under different circumstances.

[0092] For example, Figure 5 is an imaging effect diagram of a different three-dimensional point spread function provided according to an embodiment of the present application. Figure 5 , Figure 5 (a) in the figure exemplarily shows the imaging effect of a small aperture and wide-band three-dimensional point spread function; Figure 5 (b) in the figure exemplarily shows the imaging effect of a large aperture and broadband three-dimensional point spread function; Figure 5 (c) in the figure exemplarily shows the imaging effect of a three-dimensional point spread function with a large aperture and narrow frequency band; Figure 5(d) in the figure shows the imaging effect of a three-dimensional point spread function with a small aperture and a narrow frequency band. It can be seen that the imaging effect of a three-dimensional point spread function with a larger aperture parameter and a larger frequency band parameter is better, that is, a certain point (position) can be observed more clearly, and the position after imaging is more accurate.

[0093] The terminal uses a three-dimensional point spread function to process the reflection data volume to obtain a sample seismic data volume. The sample seismic data volume is a three-dimensional tensor. Taking the geological body category of collapse as an example, the terminal uses a three-dimensional point spread function to process the reflection data volume of the collapse to obtain a sample seismic data volume of the collapse. Figure 6 , Figure 6 is a collapsed sample seismic data volume provided according to an embodiment of the present application. Figure 6 601 in the figure is a collapse. The terminal can adopt the method in steps 101 to 103 to obtain multiple sample training data to prepare for subsequent model training.

[0094] 104. The terminal trains a geological body recognition model based on the sample seismic data body, and the trained geological body recognition model is used to identify the location of the geological body belonging to the geological body category in the stratum.

[0095] In an embodiment of the present application, the terminal inputs the sample seismic data body into the geological body recognition model to train the geological body recognition model. Specifically, the process of training the geological body recognition model based on the sample seismic data body by the terminal includes: the terminal inputs the sample seismic data body into the geological body recognition model to obtain seismic data features. The seismic data feature is a three-dimensional tensor. Then, the terminal identifies the position of the geological body belonging to the geological body category in the sample seismic data body based on the seismic data features. Then, the terminal trains the geological body recognition model based on the difference between the position of the identified geological body and the marked position of the sample seismic data body. Wherein, the marked position is determined based on the adjusted reflection coefficient. The solution provided by the embodiment of the present application trains the geological body recognition model through the sample seismic data body, so that the geological body recognition model can fully learn the characteristics of the geological body, thereby improving the recognition performance of the geological body recognition model, and then based on the trained geological body recognition model, when predicting the target seismic data body obtained by exploration, the position of the geological body can be more accurately identified, and compared with manual interpretation, the position of the geological body is identified by the trained geological body recognition model, which is more efficient.

[0096] For example, Figure 7 is a schematic diagram of a seismic data feature provided according to an embodiment of the present application. Figure 7 , Figure 7 (a) in the figure shows the seismic data characteristics of a karst cave by way of example. 701 in the figure (a) shows a karst cave by way of example. Figure 7 (b) in the figure shows the earthquake data characteristics of collapse by way of example. 702 in the figure (b) shows a collapse by way of example.

[0097] The embodiment of the present application does not limit the structure of the geological body recognition model. The end-to-end network is derived from the encoder-decoder model, which is often used for semantic segmentation problems. Cave and collapse detection can be considered as a segmentation problem. Optionally, the geological body recognition model is an end-to-end convolutional neural network.

[0098] For example, Figure 8 Schematic diagram of a geological body recognition model provided according to an embodiment of the present application. The geological body recognition model includes a convolution layer, an activation layer, a batch normalization layer, a pooling layer, an upsampling layer, a concatenation layer, and a residual layer. Figure 8 , 1, 3, 5, 7, 10, 12, 14 and 15 in the geological body recognition model are residual blocks, which include convolutional layers, batch normalization layers and activation layers, etc.; 2, 4, 6, 8, 9, 11 and 13 in the geological body recognition model are convolutional blocks, which include convolutional layers. The residual blocks and convolutional blocks can be connected by a maximum pooling layer, such as between 2 and 3, between 4 and 5, and between 6 and 7. Convolutional block 8 and convolutional block 9 can be connected by an upsampling layer; residual block 10 and convolutional block 11 can be connected by an upsampling layer; residual block 12 and convolutional block 13 can be connected by an upsampling layer. Convolutional block 2 and convolutional block 13 can be connected by a connection layer; convolutional block 4 and convolutional block 11 can be connected by a connection layer; convolutional block 6 and convolutional block 8 can be connected by a connection layer.

[0099] In order to further improve the feature extraction capability of the geological body recognition model, the embodiment of the present application may use multiple residual layers. The residual layer can avoid the problem of gradient disappearance. By adding the residual layer, the number of layers of the geological body recognition model can be greatly increased to improve the recognition accuracy of the model.

[0100] For example, Fig. 9 is a schematic diagram of a residual layer provided according to an embodiment of the present application. Fig. 9 , the residual layer includes convolution layer 1, convolution layer 2 and convolution layer 3. Among them, the convolution kernels in convolution layer 1 and convolution layer 2 are 3*3 convolution kernels; the convolution kernel in convolution layer 3 is 1*1 convolution kernel. The terminal adds the output of convolution layer 2 and the output of convolution layer 3, and activates them using an activation function to obtain the output of the residual layer. The activation function can be ReLU (Rectified Linear Unit), which is not limited in the embodiment of the present application.

[0101] 105. The terminal predicts the target seismic data volume based on the trained geological body recognition model to obtain the location of the geological body belonging to the geological body category in the target stratigraphic area. The target seismic data volume is obtained based on the exploration of the target stratigraphic area.

[0102] In an embodiment of the present application, the terminal uses a trained geological body recognition model to predict the geological body of a cave (or collapse) for the surveyed target seismic data body, and obtains a three-dimensional probability body. The three-dimensional probability body includes multiple probability values. Each probability value is used to represent the probability that the corresponding position in the three-dimensional space is a geological body of a cave (or collapse). Multiple probability values ​​can be arranged according to the corresponding positions in the three-dimensional space, thereby obtaining a three-dimensional probability body.

[0103] In the case of caves identified, see Fig.10 , Fig.10 This is a cave identification effect diagram provided according to an embodiment of the present application. Fig.10 (a) in the figure shows the three-dimensional probability body of the predicted cave. The darker the color, the greater the probability of the existence of the cave. The terminal can also use the three-dimensional display or carving function to process the three-dimensional probability body of the cave to display the cave, thereby realizing the interpretation of the cave. See Fig.10 In (b), 1001 is the identified cave.

[0104] In the case of identified collapse, see Fig.11 , Fig.11 This is a collapse identification effect diagram provided according to an embodiment of the present application. Fig.11 (a) in the figure shows the target seismic data volume explored by way of example; Fig.11 (b) in the figure shows the identification result of the geological body identification model by way of example. 1101 is the identified collapse. Fig.11 (c) in the figure shows an example of a top view of the collapsed ground. The position indicated by the arrow is the collapsed position.

[0105] The embodiment of the present application provides a method for identifying a geological body. According to the category of the geological body to be identified, multiple reflection coefficients are adjusted so that the obtained reflection data body can accurately reflect the reflection characteristics of the geological body in the three-dimensional space; then, the reflection data body is processed by a three-dimensional point spread function so that the obtained sample seismic data body can accurately reflect the characteristics of the geological body in the three-dimensional space, that is, the data of the geological body in the three-dimensional space can be simulated more accurately with higher authenticity; then, a geological body recognition model is trained by the sample seismic data body so that the geological body recognition model can fully learn the characteristics of the geological body, thereby improving the recognition performance of the geological body recognition model, and then when predicting the target seismic data body obtained by exploration based on the trained geological body recognition model, the position of the geological body can be more accurately identified, and compared with manual interpretation, the position of the geological body can be identified more efficiently by using the trained geological body recognition model.

[0106] Fig.12 is a schematic diagram of a geological body identification device according to an embodiment of the present application. The device is used to execute the steps of the geological body identification method. Fig.12 The device includes: an acquisition module 1201, a first adjustment module 1202, a processing module 1203, a training module 1204 and a prediction module 1205.

[0107] An acquisition module 1201 is used to acquire a plurality of reflection coefficients, where the plurality of reflection coefficients are used to simulate the properties of reflected seismic wavelets at a plurality of positions in a plurality of strata in a three-dimensional space;

[0108] A first adjustment module 1202 is used to adjust a plurality of reflection coefficients based on the geological body category to obtain a reflection data body, where the reflection data body is used to simulate the reflection characteristics of the geological body belonging to the geological body category;

[0109] A processing module 1203 is used to process the reflection data volume based on a three-dimensional point spread function to obtain a sample seismic data volume, wherein the three-dimensional point spread function is used to represent the propagation properties of seismic wavelets in time and space, and the sample seismic data volume is used to simulate the characteristics of a geological body belonging to a geological body category;

[0110] A training module 1204 is used to train a geological body recognition model based on a sample seismic data body, and the trained geological body recognition model is used to recognize the location of a geological body belonging to a geological body category in a stratum;

[0111] The prediction module 1205 is used to predict the target seismic data body based on the trained geological body recognition model to obtain the location of the geological body belonging to the geological body category in the target stratigraphic area. The target seismic data body is obtained based on the exploration of the target stratigraphic area.

[0112] In some embodiments, Fig.13 is a schematic diagram of the structure of another geological body identification device provided according to an embodiment of the present application. Fig.13 , the first adjustment module 1202 includes:

[0113] The deformation unit 12021 is used to perform fold deformation on multiple reflection coefficients to obtain an intermediate data body, and the intermediate data body is used to simulate the reflection characteristics of the real stratum structure;

[0114] The adjustment unit 12022 is used to adjust multiple target reflection coefficients in the intermediate data volume based on the geological body category to obtain a reflection data volume, and the position of the target reflection coefficient is used to indicate the position of the geological body belonging to the geological body category.

[0115] In some embodiments, see Fig.13 , adjustment unit 12022 is used to obtain multiple first target reflection coefficients and multiple second target reflection coefficients when the geological body category is a cave, the position of the first target reflection coefficient is used to represent the edge of the cave, the position of the second target reflection coefficient is used to represent the inside of the cave, and the multiple first target reflection coefficients surround the multiple second target reflection coefficients; adjust the multiple first target reflection coefficients, and the value of any adjusted first target reflection coefficient is different from the values ​​of other reflection coefficients of the same stratum; adjust the values ​​of the multiple second target reflection coefficients to zero; and obtain a reflection data body based on the adjusted multiple first target reflection coefficients and the multiple second target reflection coefficients.

[0116] In some embodiments, see Fig.13 The adjustment unit 12022 is used to obtain multiple third target reflection coefficients when the geological body category is collapse, and the position of the third target reflection coefficients is used to indicate the edge of the collapse; the positions of the multiple third target reflection coefficients are adjusted downward by a preset distance to obtain a reflection data body, and the preset distance is used to indicate the depth of the collapse.

[0117] In some embodiments, see Fig.13 The processing module 1203 is used to determine the three-dimensional point spread function based on the aperture parameter and the frequency parameter, and the aperture parameter and the frequency parameter are used to reflect the exploration effect of the three-dimensional point spread function on each position in the formation; based on the three-dimensional point spread function, the reflection data body is convolved to obtain a sample seismic data body.

[0118] In some embodiments, see Fig.13 , the device further comprises:

[0119] A second adjustment module 1206, configured to adjust at least one of an aperture parameter and a frequency parameter to obtain a new three-dimensional point spread function;

[0120] The processing module 1203 is further used to convolve the reflection data volume based on the new three-dimensional point spread function to obtain a new sample seismic data volume.

[0121] In some embodiments, see Fig.13 The training module 1205 is used to input the sample seismic data body into the geological body recognition model to obtain the seismic data characteristics; based on the seismic data characteristics, identify the position of the geological body belonging to the geological body category in the sample seismic data body; based on the difference between the position of the identified geological body and the marked position of the sample seismic data body, train the geological body recognition model, and the marked position is determined based on the adjusted reflection coefficient.

[0122] The embodiment of the present application provides a geological body identification device, which adjusts multiple reflection coefficients according to the category of the geological body to be identified, so that the obtained reflection data body can accurately reflect the reflection characteristics of the geological body in the three-dimensional space; then, the reflection data body is processed by a three-dimensional point spread function, so that the obtained sample seismic data body can accurately reflect the characteristics of the geological body in the three-dimensional space, that is, the data of the geological body in the three-dimensional space can be simulated more accurately with higher authenticity; then, the geological body identification model is trained by the sample seismic data body, so that the geological body identification model can fully learn the characteristics of the geological body, thereby improving the recognition performance of the geological body identification model, and then when predicting the target seismic data body obtained by exploration based on the trained geological body identification model, the position of the geological body can be more accurately identified, and compared with manual interpretation, it is more efficient to identify the position of the geological body through the trained geological body identification model.

[0123] It should be noted that, when the geological body identification device provided in the above embodiment runs the application program, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the geological body identification device provided in the above embodiment and the geological body identification method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0124] In the embodiments of the present application, the computer device can be configured as a terminal or a server. When the computer device is configured as a terminal, the terminal can be used as the execution subject to implement the technical solution provided in the embodiments of the present application. When the computer device is configured as a server, the server can be used as the execution subject to implement the technical solution provided in the embodiments of the present application. The technical solution provided in the present application can also be implemented through interaction between the terminal and the server. The embodiments of the present application are not limited to this.

[0125] Fig.14 This is a block diagram of a terminal 1400 provided according to an embodiment of the present application. The terminal 1400 may be a portable mobile terminal, such as a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer or a desktop computer. The terminal 1400 may also be referred to as a user device, a portable terminal, a laptop terminal, a desktop terminal or other names.

[0126] Typically, the terminal 1400 includes a processor 1401 and a memory 1402 .

[0127] The processor 1401 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1401 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 1401 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 1401 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 1401 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0128] The memory 1402 may include one or more computer-readable storage media, which may be non-transitory. The memory 1402 may also include a high-speed random access memory and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 1402 is used to store at least one computer program, which is used to be executed by the processor 1401 to implement the method for identifying a geological body provided in the method embodiment of the present application.

[0129] In some embodiments, the terminal 1400 may further optionally include: a peripheral device interface 1403 and at least one peripheral device. The processor 1401, the memory 1402 and the peripheral device interface 1403 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 1403 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 1404, a display screen 1405, a camera assembly 1406, an audio circuit 1407 and a power supply 1408.

[0130] The peripheral device interface 1403 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 1401 and the memory 1402. In some embodiments, the processor 1401, the memory 1402, and the peripheral device interface 1403 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 1401, the memory 1402, and the peripheral device interface 1403 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0131] The radio frequency circuit 1404 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 1404 communicates with the communication network and other communication devices through electromagnetic signals. The radio frequency circuit 1404 converts the electrical signal into an electromagnetic signal for transmission, or converts the received electromagnetic signal into an electrical signal. In some embodiments, the radio frequency circuit 1404 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The radio frequency circuit 1404 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 1404 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0132] The display screen 1405 is used to display a UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 1405 is a touch display screen, the display screen 1405 also has the ability to collect touch signals on the surface or above the surface of the display screen 1405. The touch signal can be input to the processor 1401 as a control signal for processing. At this time, the display screen 1405 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 1405 can be one, set on the front panel of the terminal 1400; in other embodiments, the display screen 1405 can be at least two, respectively set on different surfaces of the terminal 1400 or in a folding design; in other embodiments, the display screen 1405 can be a flexible display screen, set on a curved surface or a folding surface of the terminal 1400. Even, the display screen 1405 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 1405 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0133] The camera assembly 1406 is used to capture images or videos. In some embodiments, the camera assembly 1406 includes a front camera and a rear camera. Typically, the front camera is disposed on the front panel of the terminal, and the rear camera is disposed on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 1406 may also include a flash. The flash may be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0134] The audio circuit 1407 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 1401 for processing, or input them into the radio frequency circuit 1404 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal 1400. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 1401 or the radio frequency circuit 1404 into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 1407 may also include a headphone jack.

[0135] The power supply 1408 is used to power various components in the terminal 1400. The power supply 1408 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 1408 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery charged through a wired line, and a wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0136] In some embodiments, the terminal 1400 further includes one or more sensors 1409 . The one or more sensors 1409 include, but are not limited to: an acceleration sensor 1410 , a gyroscope sensor 1411 , a pressure sensor 1412 , an optical sensor 1413 , and a proximity sensor 1414 .

[0137] The acceleration sensor 1410 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the terminal 1400. For example, the acceleration sensor 1410 can be used to detect the components of gravity acceleration on the three coordinate axes. The processor 1401 can control the display screen 1405 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 1410. The acceleration sensor 1410 can also be used for collecting game or user motion data.

[0138] The gyro sensor 1411 can detect the body direction and rotation angle of the terminal 1400, and the gyro sensor 1411 can cooperate with the acceleration sensor 1410 to collect the user's 3D actions on the terminal 1400. The processor 1401 can implement the following functions based on the data collected by the gyro sensor 1411: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0139] The pressure sensor 1412 may be provided at the side frame of the terminal 1400 and / or the lower layer of the display screen 1405. When the pressure sensor 1412 is provided at the side frame of the terminal 1400, it may detect the user's holding signal of the terminal 1400, and the processor 1401 may perform left and right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 1412. When the pressure sensor 1412 is provided at the lower layer of the display screen 1405, the processor 1401 may control the operability control on the UI interface according to the user's pressure operation on the display screen 1405. The operability control includes at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0140] The optical sensor 1413 is used to collect the ambient light intensity. In one embodiment, the processor 1401 can control the display brightness of the display screen 1405 according to the ambient light intensity collected by the optical sensor 1413. Specifically, when the ambient light intensity is high, the display brightness of the display screen 1405 is increased; when the ambient light intensity is low, the display brightness of the display screen 1405 is reduced. In another embodiment, the processor 1401 can also dynamically adjust the shooting parameters of the camera assembly 1406 according to the ambient light intensity collected by the optical sensor 1413.

[0141] The proximity sensor 1414, also called a distance sensor, is usually arranged on the front panel of the terminal 1400. The proximity sensor 1414 is used to collect the distance between the user and the front of the terminal 1400. In one embodiment, when the proximity sensor 1414 detects that the distance between the user and the front of the terminal 1400 is gradually decreasing, the processor 1401 controls the display screen 1405 to switch from the screen-on state to the screen-off state; when the proximity sensor 1414 detects that the distance between the user and the front of the terminal 1400 is gradually increasing, the processor 1401 controls the display screen 1405 to switch from the screen-off state to the screen-on state.

[0142] Those skilled in the art will understand that Fig.14 The structure shown in the figure does not constitute a limitation on the terminal 1400, and the terminal 1400 may include more or less components than those shown in the figure, or combine some components, or adopt a different component arrangement.

[0143] Fig.15 It is a structural block diagram of a server provided according to an embodiment of the present application. The server 1500 may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) 1501 and one or more memories 1502, wherein the memory 1502 stores at least one computer program, and the at least one computer program is loaded and executed by the processor 1501 to implement the geological body identification method provided by the above-mentioned various method embodiments. Of course, the server 1500 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output. The server 1500 may also include other components for implementing device functions, which will not be repeated here.

[0144] The embodiment of the present application also provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is loaded and executed by a processor of a computer device to implement the operation performed by the computer device in the method for identifying a geological body in the above embodiment. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0145] The embodiment of the present application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. A processor of a computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program code, so that the computer device executes the geological body identification method provided in the above various optional implementations.

[0146] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0147] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for identifying a geological body, characterized in that: The method comprises: Acquire a plurality of reflection coefficients, wherein the plurality of reflection coefficients are used to simulate the properties of reflected seismic wavelets at a plurality of locations in a plurality of strata in a three-dimensional space; Based on the geological body category, the multiple reflection coefficients are adjusted to obtain a reflection data volume, wherein the reflection data volume is used to simulate the reflection characteristics of the geological body belonging to the geological body category; Processing the reflection data volume based on a three-dimensional point spread function to obtain a sample seismic data volume, wherein the three-dimensional point spread function is used to represent the propagation properties of seismic wavelets in time and space, and the sample seismic data volume is used to simulate the characteristics of a geological body belonging to the geological body category; Based on the sample seismic data body, a geological body recognition model is trained, and the trained geological body recognition model is used to identify the position of the geological body belonging to the geological body category in the stratum; Based on the trained geological body recognition model, a target seismic data body is predicted to obtain the position of a geological body belonging to the geological body category in a target stratigraphic region, wherein the target seismic data body is obtained based on the exploration of the target stratigraphic region.

2. The method according to claim 1, characterized in that: The step of adjusting the multiple reflection coefficients based on the geological body type to obtain a reflection data body includes: Performing wrinkle deformation on the multiple reflection coefficients to obtain an intermediate data volume, wherein the intermediate data volume is used to simulate the reflection characteristics of a real stratum structure; Based on the geological body category, a plurality of target reflection coefficients in the intermediate data body are adjusted to obtain the reflection data body, and the positions of the target reflection coefficients are used to indicate the positions of the geological bodies belonging to the geological body category.

3. The method according to claim 2, characterized in that The step of adjusting a plurality of target reflection coefficients in the intermediate data volume based on the geological volume category to obtain the reflection data volume includes: In the case where the geological body type is a karst cave, a plurality of first target reflection coefficients and a plurality of second target reflection coefficients are obtained, wherein the positions of the first target reflection coefficients are used to represent the edge of the karst cave, and the positions of the second target reflection coefficients are used to represent the inside of the karst cave, and the plurality of first target reflection coefficients surround the plurality of second target reflection coefficients; The plurality of first target reflection coefficients are adjusted, wherein the value of any adjusted first target reflection coefficient is different from the values ​​of other reflection coefficients of the same formation; adjusting the values ​​of the plurality of second target reflection coefficients to zero; The reflection data volume is obtained based on the adjusted plurality of first target reflection coefficients and the plurality of second target reflection coefficients.

4. The method according to claim 2, characterized in that: The step of adjusting a plurality of target reflection coefficients in the intermediate data volume based on the geological volume category to obtain the reflection data volume includes: In the case where the geological body category is collapse, a plurality of third target reflection coefficients are obtained, and the positions of the third target reflection coefficients are used to represent the edge of the collapse; The positions of the plurality of third target reflection coefficients are adjusted downward by a preset distance to obtain the reflection data volume, where the preset distance is used to represent the depth of the collapse.

5. The method according to claim 1, characterized in that The processing of the reflection data volume based on the three-dimensional point spread function to obtain a sample seismic data volume includes: Determining the three-dimensional point spread function based on an aperture parameter and a frequency parameter, wherein the aperture parameter and the frequency parameter are used to reflect the exploration effect of the three-dimensional point spread function on various positions in the formation; The reflection data volume is convolved based on the three-dimensional point spread function to obtain the sample seismic data volume.

6. The method according to claim 5, characterized in that The method further comprises: Adjusting at least one of the aperture parameter and the frequency parameter to obtain a new three-dimensional point spread function; The reflection data volume is convolved based on the new three-dimensional point spread function to obtain a new sample seismic data volume.

7. The method according to claim 1, characterized in that The step of training a geological body recognition model based on the sample seismic data body includes: Inputting the sample seismic data body into the geological body identification model to obtain seismic data features; Based on the seismic data features, identifying the location of the geological body belonging to the geological body category in the sample seismic data volume; The geological volume recognition model is trained based on the difference between the position of the identified geological volume and the annotated position of the sample seismic data volume, the annotated position being determined based on the adjusted reflection coefficient.

8. A geological body identification device, characterized in that: The device comprises: An acquisition module, used for acquiring a plurality of reflection coefficients, wherein the plurality of reflection coefficients are used for simulating the properties of reflected seismic wavelets at a plurality of positions in a plurality of strata in a three-dimensional space; A first adjustment module is used to adjust the multiple reflection coefficients based on the geological body category to obtain a reflection data body, wherein the reflection data body is used to simulate the reflection characteristics of the geological body belonging to the geological body category; A processing module, used for processing the reflection data volume based on a three-dimensional point spread function to obtain a sample seismic data volume, wherein the three-dimensional point spread function is used to represent the propagation properties of seismic wavelets in time and space, and the sample seismic data volume is used to simulate the characteristics of a geological body belonging to the geological body category; A training module, used for training a geological body recognition model based on the sample seismic data body, wherein the trained geological body recognition model is used for identifying the position of a geological body belonging to the geological body category in a stratum; The prediction module is used to predict the target seismic data body based on the trained geological body recognition model to obtain the position of the geological body belonging to the geological body category in the target stratigraphic area, and the target seismic data body is obtained based on the exploration of the target stratigraphic area.

9. A computer device, characterized in that: The computer device comprises a processor and a memory, wherein the memory is used to store at least one computer program, and the at least one computer program is loaded by the processor to execute the method for identifying a geological body according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store at least one computer program, and the at least one computer program is used to execute the method for identifying a geological body according to any one of claims 1 to 7.

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