Oil exploration method and system based on remote sensing technology, electronic equipment and storage medium

Abstract

The invention belongs to the field of oil exploration, and discloses an oil exploration method and system based on a remote sensing technology, electronic equipment and a storage medium, and the method comprises the steps: collecting multi-source remote sensing data of a target region, and carrying out the preprocessing; performing feature extraction on the processed data; constructing an oil exploration model based on the extracted features; and completing oil exploration of the target area by using the oil exploration model. According to the invention, through comprehensive utilization of multi-source remote sensing data and an intelligent algorithm, rapid and accurate detection of a potential petroleum resource area is realized. The method has the advantages of high efficiency, economy and environmental protection, and can be widely applied to oil exploration tasks under various topographic conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration, and in particular to an oil exploration method, system, electronic equipment and storage medium based on remote sensing technology. Background Art

[0002] As a vital energy source and industrial raw material, oil exploration has always been a global focus. Traditional oil exploration methods primarily include geological surveys, geophysical exploration (such as seismic exploration), and drilling. However, these methods often suffer from high costs, long lead times, and significant environmental impacts. Exploration is particularly challenging in complex terrain or remote areas.

[0003] Remote sensing technology, as a non-contact detection method, can rapidly acquire multidimensional surface and near-surface information, including topography, vegetation cover, soil properties, and potential signs of crustal activity. In recent years, with the development of satellite remote sensing, drone remote sensing, and radar technology, the potential for remote sensing applications in resource exploration has become increasingly apparent. However, specific methods for systematically applying remote sensing technology to oil exploration are currently lacking, particularly in data processing and target identification, which require further improvement. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method for oil exploration based on remote sensing technology, which aims to improve the efficiency and accuracy of oil resource detection while reducing exploration costs and environmental impacts.

[0005] To achieve the above object, the present invention provides a petroleum exploration method based on remote sensing technology, the method comprising:

[0006] Collect multi-source remote sensing data of the target area and perform preprocessing;

[0007] Perform feature extraction on the processed data;

[0008] Based on the extracted features, an oil exploration model is constructed;

[0009] The oil exploration model is used to complete oil exploration in the target area.

[0010] Preferably, the method for performing the pretreatment comprises:

[0011] Radiation correction:

[0012]

[0013] Among them, L λ Indicates the corrected radiance, L raw Indicates the original radiation value, K cal Indicates the calibration factor, Ksat Indicates the sensor saturation value;

[0014] Geometric correction:

[0015]

[0016] Where (x, y) represents the original image coordinates, (x′, y′) represents the corrected coordinates, and a1, a2, a3, b1, b2, and b3 represent the affine transformation parameters.

[0017] Atmospheric correction:

[0018]

[0019] Among them, L surface Represents the surface reflectivity, L λ Indicates the corrected radiance, L path represents the atmospheric path radiation, and τ represents the atmospheric transmittance.

[0020] Preferably, after the correction is completed, Gaussian filtering is used to remove random noise in the remote sensing data and principal component analysis is used to perform data fusion.

[0021] Preferably, the method for performing the feature extraction includes:

[0022] Texture feature extraction:

[0023]

[0024] Where P(i,j,d,θ) represents the gray-level co-occurrence matrix, f(x,y) represents the pixel value, d represents the distance between pixels, θ represents the direction, δ represents the Kronecker function, and N represents the total number of pixels.

[0025] Shape feature extraction:

[0026]

[0027] Where P represents the boundary length of the target area; A represents the area of ​​the target area; C represents the compactness; N represents the total number of pixels; (x i ,y i ) represents the coordinates of the i-th pixel.

[0028] Preferably, the oil exploration model is constructed based on the random forest algorithm, and each decision tree is split according to the Gini impurity minimization principle. The Gini impurity calculation formula is:

[0029]

[0030] Where D is the current node dataset; p kis the proportion of samples in the kth category; K is the total number of samples.

[0031] Preferably, the model generates prediction results through a voting mechanism of multiple decision trees, and the formula is as follows:

[0032]

[0033] in, represents the prediction result; χ represents the input feature vector; h t (x) represents the prediction result of the t-th tree, t = 1, 2, 3…T; T represents the total number of trees; mode represents the mode function.

[0034] The present invention also provides a petroleum exploration system based on remote sensing technology, the system is used to implement the above method, including: an acquisition module, an extraction module, a construction module and an exploration module;

[0035] The acquisition module is used to collect multi-source remote sensing data of the target area and perform preprocessing;

[0036] The extraction module is used to extract features from the processed data;

[0037] The construction module is used to construct an oil exploration model based on the extracted features;

[0038] The exploration module is used to complete oil exploration in a target area using the oil exploration model.

[0039] Preferably, the workflow of the acquisition module includes:

[0040] Radiation correction:

[0041]

[0042] Among them, L λ Indicates the corrected radiance, L raw Indicates the original radiation value, K cal Indicates the calibration factor, K sat Indicates the sensor saturation value;

[0043] Geometric correction:

[0044]

[0045] Where (x, y) represents the original image coordinates, (x′, y′) represents the corrected coordinates, and a1, a2, a3, b1, b2, and b3 represent the affine transformation parameters.

[0046] Atmospheric correction:

[0047]

[0048] Among them, L surface Represents the surface reflectivity, L λ Indicates the corrected radiance, L path represents the atmospheric path radiation, and τ represents the atmospheric transmittance.

[0049] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the program.

[0050] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the above method is implemented.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] This method uses multi-source remote sensing data and intelligent algorithms to rapidly and accurately detect potential oil resource areas. This method is highly efficient, economical, and environmentally friendly, and can be widely applied to oil exploration tasks in various terrain conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 Schematic diagram of a method flow in an embodiment of the present invention;

[0055] Figure 2 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention.

[0056] Description of reference numerals:

[0057] 1010 , processor; 1020 , memory; 1030 , input / output interface; 1040 , communication interface; 1050 , bus. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0059] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should have the usual meanings understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the described object changes, the relative position relationship may also change accordingly.

[0060] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] Example 1

[0062] As can be seen from the background technology, existing methods often have problems such as high cost, long cycle, and great environmental impact. Especially in complex terrain or remote areas, exploration is more difficult.

[0063] The embodiment of the present invention provides a method for oil exploration based on remote sensing technology, such as Figure 1 As shown, the steps include:

[0064] S1. Collect multi-source remote sensing data of the target area and perform preprocessing.

[0065] Select appropriate remote sensing equipment and sensor types based on the geological conditions and environmental characteristics of the target area. For example, in open areas, high-resolution satellite remote sensing equipment (such as Landsat and Sentinel satellites) can be used to obtain large-scale surface imagery. In areas with dense vegetation or complex terrain, drones equipped with multispectral, thermal infrared, or synthetic aperture radar (SAR) sensors can be used for localized, detailed detection. Furthermore, to ensure the comprehensiveness and accuracy of the data, it is necessary to combine multi-temporal remote sensing data to reflect surface variation characteristics, and to calibrate the spatial position and scale of the remote sensing images using ground control points, thereby providing reliable basic data support for subsequent analysis.

[0066] After remote sensing data is collected, it is preprocessed. The preprocessing steps include radiation correction, geometric correction, atmospheric correction, noise filtering and data fusion.

[0067] Radiation correction is to eliminate the effects of sensor characteristic changes, solar radiation changes, and atmospheric scattering on remote sensing data:

[0068]

[0069] Among them, L λ Indicates the corrected radiance, L raw Indicates the original radiation value, K cal Indicates the calibration factor, K sat Indicates the sensor saturation value.

[0070] Then geometric correction is used to correct the spatial position deviation of the remote sensing image to make it consistent with the actual geographic coordinates:

[0071]

[0072] Where (x, y) represents the original image coordinates, (x′, y′) represents the corrected coordinates, and a1, a2, a3, b1, b2, and b3 represent the affine transformation parameters, which are calculated using ground control points (GCPs).

[0073] Atmospheric correction is used to eliminate the effects of atmospheric scattering and absorption on remote sensing data:

[0074]

[0075] Among them, L surface Represents the surface reflectivity, L λ Indicates the corrected radiance, L path represents the atmospheric path radiation, and τ represents the atmospheric transmittance.

[0076] Use Gaussian filtering to remove random noise in remote sensing data:

[0077]

[0078] Among them, I filtered (x, y) represents the filtered pixel value, I(x+i, y+j) represents the original pixel value, and G(i, j) represents the Gaussian kernel function.

[0079] Finally, data fusion is to integrate multi-source remote sensing data (such as optical images and radar images) to improve the richness and resolution of information. This embodiment uses the principal component analysis method including:

[0080] Y=X·W

[0081] Among them, Y represents the principal component result, X represents the original data matrix, and W represents the principal component weight matrix, which is obtained by eigendecomposition of the covariance matrix.

[0082] S2. Perform feature extraction on the processed data.

[0083] First, multi-dimensional feature analysis is performed on the pre-processed remote sensing data, including the extraction of texture features and shape features. The following is the specific feature extraction method:

[0084] (1) Texture feature extraction

[0085] Texture features are used to describe the uniformity, roughness and other characteristics of surface cover. A commonly used method is texture analysis based on the gray level co-occurrence matrix (GLCM). The calculation formula of the gray level co-occurrence matrix is:

[0086]

[0087] Among them, P(i,j,d,θ) represents the gray-level co-occurrence matrix, f(x,y) represents the pixel value, d represents the distance between pixels, θ represents the direction, δ represents the Kronecker function, and N represents the total number of pixels.

[0088] (2) Shape feature extraction

[0089] Shape features are used to describe the geometric characteristics of the target area. The boundary information of the target area is extracted through the edge detection algorithm, and the following shape features are calculated:

[0090] Target area boundary length:

[0091]

[0092] Where N represents the total number of pixels; (x i ,y i ) represents the coordinates of the i-th pixel.

[0093] Area of ​​target area:

[0094]

[0095] Compactness:

[0096]

[0097] The texture features and shape features are integrated to form a comprehensive feature vector.

[0098] S3. Build an oil exploration model based on the extracted features.

[0099] An oil exploration model is constructed based on the comprehensive feature vector extracted in step S2. This model uses the random forest algorithm, whose core is to predict oil resources in the target area by integrating multiple decision trees. The random forest is composed of several decision trees. Each tree extracts a training subset from the original data through bootstrap sampling and randomly selects a feature subset when splitting the node, thereby enhancing the model's generalization ability and resistance to overfitting. Each decision tree is split according to the principle of minimizing Gini impurity. The Gini impurity calculation formula is:

[0100]

[0101] Where D is the current node dataset; p k is the proportion of samples in the kth category; K is the total number of samples.

[0102] For input texture features (such as contrast and entropy of the gray-level co-occurrence matrix) and shape features (such as area and compactness), the model automatically selects key indicators through feature importance evaluation. For example, by calculating the average reduction of the feature's Gini impurity during the tree splitting process, the model determines its contribution weight to oil resource prediction. Ultimately, the model generates a prediction result through a voting mechanism among multiple decision trees, as shown in the following formula:

[0103]

[0104] in, represents the prediction result; χ represents the input feature vector; h t (x) represents the prediction result of the t-th tree, t = 1, 2, 3…T; T represents the total number of trees; mode represents the mode function.

[0105] S4. Use the oil exploration model to complete oil exploration in the target area.

[0106] The collected remote sensing maps are input into the oil exploration model. The model will analyze the geological conditions of the area based on the remote sensing maps, and thus inform the subsequent exploration and mining work of the staff.

[0107] The technical solution of this invention achieves rapid and accurate detection of potential oil resource areas by integrating multi-source remote sensing data with intelligent algorithms. This method is highly efficient, economical, and environmentally friendly, and can be widely applied to oil exploration tasks in various terrain conditions.

[0108] It should be noted that the method of the embodiments of the present disclosure can be performed by a single device, such as a computer or server. The method of the embodiments of the present disclosure can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the multiple devices will interact with each other to complete the method.

[0109] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, it should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

[0110] Example 2

[0111] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides an oil exploration system based on remote sensing technology, including: an acquisition module, an extraction module, a construction module and an exploration module; the acquisition module is used to acquire multi-source remote sensing data of the target area and perform preprocessing; the extraction module is used to extract features from the processed data; the construction module is used to construct an oil exploration model based on the extracted features; and the exploration module is used to use the oil exploration model to complete oil exploration in the target area.

[0112] The following will describe in detail how the present invention solves technical problems in practical work in conjunction with this embodiment.

[0113] First, the acquisition module is used to collect multi-source remote sensing data of the target area and perform preprocessing.

[0114] Select appropriate remote sensing equipment and sensor types based on the geological conditions and environmental characteristics of the target area. For example, in open areas, high-resolution satellite remote sensing equipment (such as Landsat and Sentinel satellites) can be used to obtain large-scale surface imagery. In areas with dense vegetation or complex terrain, drones equipped with multispectral, thermal infrared, or synthetic aperture radar (SAR) sensors can be used for localized, detailed detection. Furthermore, to ensure the comprehensiveness and accuracy of the data, it is necessary to combine multi-temporal remote sensing data to reflect surface variation characteristics, and to calibrate the spatial position and scale of the remote sensing images using ground control points, thereby providing reliable basic data support for subsequent analysis.

[0115] After remote sensing data is collected, it is preprocessed. The preprocessing process includes radiation correction, geometric correction, atmospheric correction, noise filtering and data fusion.

[0116] Radiation correction is to eliminate the effects of sensor characteristic changes, solar radiation changes, and atmospheric scattering on remote sensing data:

[0117]

[0118] Among them, L λ Indicates the corrected radiance, L raw Indicates the original radiation value, K cal Indicates the calibration factor, K sat Indicates the sensor saturation value.

[0119] Then geometric correction is used to correct the spatial position deviation of the remote sensing image to make it consistent with the actual geographic coordinates:

[0120]

[0121] Where (x, y) represents the original image coordinates, (x′, y′) represents the corrected coordinates, and a1, a2, a3, b1, b2, and b3 represent the affine transformation parameters, which are calculated using ground control points (GCPs).

[0122] Atmospheric correction is used to eliminate the effects of atmospheric scattering and absorption on remote sensing data:

[0123]

[0124] Among them, L surface Represents the surface reflectivity, L λ Indicates the corrected radiance, L path represents the atmospheric path radiation, and τ represents the atmospheric transmittance.

[0125] Use Gaussian filtering to remove random noise in remote sensing data:

[0126]

[0127] Among them, I filtered (x, y) represents the filtered pixel value, I(x+i, y+j) represents the original pixel value, and G(i, j) represents the Gaussian kernel function.

[0128] Finally, data fusion is to integrate multi-source remote sensing data (such as optical images and radar images) to improve the richness and resolution of information. This embodiment uses the principal component analysis method including:

[0129] Y=X·W

[0130] Among them, Y represents the principal component result, X represents the original data matrix, and W represents the principal component weight matrix, which is obtained by eigendecomposition of the covariance matrix.

[0131] The extraction module is used to extract features from the processed data.

[0132] First, multi-dimensional feature analysis is performed on the pre-processed remote sensing data, including the extraction of texture features and shape features. The following is the specific feature extraction method:

[0133] (1) Texture feature extraction

[0134] Texture features are used to describe the uniformity, roughness and other characteristics of surface cover. A commonly used method is texture analysis based on the gray level co-occurrence matrix (GLCM). The calculation formula of the gray level co-occurrence matrix is:

[0135]

[0136] Among them, P(i,j,d,θ) represents the gray-level co-occurrence matrix, f(x,y) represents the pixel value, d represents the distance between pixels, θ represents the direction, δ represents the Kronecker function, and N represents the total number of pixels.

[0137] (2) Shape feature extraction

[0138] Shape features are used to describe the geometric characteristics of the target area. The boundary information of the target area is extracted through the edge detection algorithm, and the following shape features are calculated:

[0139] Target area boundary length:

[0140]

[0141] Where N represents the total number of pixels; (x i ,y i ) represents the coordinates of the i-th pixel.

[0142] Area of ​​target area:

[0143]

[0144] Compactness:

[0145]

[0146] The texture features and shape features are integrated to form a comprehensive feature vector.

[0147] The building module constructs an oil exploration model based on the extracted features.

[0148] An oil exploration model is constructed based on the comprehensive feature vector extracted by the extraction module. This model utilizes the random forest algorithm, whose core goal is to predict oil resources in a target area by integrating multiple decision trees. The random forest is composed of several decision trees. Each tree extracts a training subset from the original data through bootstrap sampling, and randomly selects a feature subset at node splitting to enhance the model's generalization and resistance to overfitting. Each decision tree is split based on the principle of minimizing the Gini impurity, which is calculated as follows:

[0149]

[0150] Where D is the current node dataset; p k is the proportion of samples in the kth category; K is the total number of samples.

[0151] For input texture features (such as contrast and entropy of the gray-level co-occurrence matrix) and shape features (such as area and compactness), the model automatically selects key indicators through feature importance evaluation. For example, by calculating the average reduction of the feature's Gini impurity during the tree splitting process, the model determines its contribution weight to oil resource prediction. Ultimately, the model generates a prediction result through a voting mechanism among multiple decision trees, as shown in the following formula:

[0152]

[0153] in, represents the prediction result; χ represents the input feature vector; h t (x) represents the prediction result of the t-th tree, t = 1, 2, 3…T; T represents the total number of trees; mode represents the mode function.

[0154] Finally, the exploration module uses the oil exploration model to complete oil exploration in the target area.

[0155] The collected remote sensing maps are input into the oil exploration model. The model will analyze the geological conditions of the area based on the remote sensing maps, and thus inform the subsequent exploration and mining work of the staff.

[0156] The system of the above embodiment is used to implement a corresponding oil exploration method based on remote sensing technology in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0157] It should be noted that the above-mentioned oil exploration system based on remote sensing technology is embodied in the form of functional units. The term "module" here can be implemented in the form of software and / or hardware, and is not specifically limited to this.

[0158] For example, a "module" may be a software program, a hardware circuit, or a combination of the two that implements the aforementioned functionality. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (e.g., a shared processor, a dedicated processor, or a group of processors) and memory for executing one or more software or firmware programs, combined logic circuits, and / or other suitable components that support the described functionality.

[0159] Example 3

[0160] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, an oil exploration method based on remote sensing technology as described in any of the above embodiments is implemented.

[0161] Figure 2 10 is a schematic diagram showing a more specific hardware structure of an electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other within the device via the bus 1050.

[0162] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0163] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0164] The input / output interface 1030 is used to connect input / output modules to implement information input and output. The input / output modules can be configured as components within the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0165] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (e.g., USB (Universal Serial Bus), network cable, etc.) or a wireless method (e.g., mobile network, WIFI (Wireless Fidelity), Bluetooth, etc.).

[0166] The bus 1050 comprises a pathway for transmitting information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0167] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0168] The system of the above embodiment is used to implement a corresponding oil exploration method based on remote sensing technology in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0169] Example 4

[0170] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute a petroleum exploration method based on remote sensing technology as described in any of the above embodiments.

[0171] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0172] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute a petroleum exploration method based on remote sensing technology as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0173] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Within the scope of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0174] In addition, to simplify the description and discussion, and so as not to obscure the embodiments of the present disclosure, known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, devices may be shown in the form of block diagrams to avoid obscuring the embodiments of the present disclosure, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure are to be implemented (i.e., these details should be fully within the purview of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present disclosure may be implemented without these specific details or with variations in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0175] Although the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0176] Therefore, the units of each example described in the embodiments of this application can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0177] The embodiments of the present disclosure are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method for oil exploration based on remote sensing technology, characterized in that: The method comprises: Collect multi-source remote sensing data of the target area and perform preprocessing; Perform feature extraction on the processed data; Based on the extracted features, an oil exploration model is constructed; The oil exploration model is used to complete oil exploration in the target area.

2. The oil exploration method based on remote sensing technology according to claim 1, characterized in that: The method for performing the pretreatment comprises: Radiation correction: Among them, L λ Indicates the corrected radiance, L raw Indicates the original radiation value, K cal Indicates the calibration factor, K sat Indicates the sensor saturation value; Geometric correction: Where (x, y) represents the original image coordinates, (x′, y′) represents the corrected coordinates, and a1, a2, a3, b1, b2, and b3 represent the affine transformation parameters. Atmospheric correction: Among them, L surface Represents the surface reflectivity, L λ Indicates the corrected radiance, L path represents the atmospheric path radiation, and τ represents the atmospheric transmittance.

3. The oil exploration method based on remote sensing technology according to claim 2, characterized in that: After the correction is completed, Gaussian filtering is used to remove random noise in the remote sensing data and principal component analysis is used for data fusion.

4. The oil exploration method based on remote sensing technology according to claim 1, characterized in that: The method for performing the feature extraction includes: Texture feature extraction: Where P(i,j,d,θ) represents the gray-level co-occurrence matrix, f(x,y) represents the pixel value, d represents the distance between pixels, θ represents the direction, δ represents the Kronecker function, and N represents the total number of pixels. Shape feature extraction: Where P represents the boundary length of the target area; A represents the area of ​​the target area; C represents the compactness; N represents the total number of pixels; (x i ,y i ) represents the coordinates of the i-th pixel.

5. The oil exploration method based on remote sensing technology according to claim 1, characterized in that: The oil exploration model is constructed based on the random forest algorithm. Each decision tree is split according to the principle of minimizing the Gini impurity. The Gini impurity calculation formula is: Where D is the current node dataset; p k is the proportion of samples in the kth category; K is the total number of samples.

6. The oil exploration method based on remote sensing technology according to claim 5, characterized in that: The model generates prediction results through a voting mechanism of multiple decision trees. The formula is as follows: in, represents the prediction result; χ represents the input feature vector; h t (x) represents the prediction result of the t-th tree, t = 1, 2, 3…T; T represents the total number of trees; mode represents the mode function.

7. A petroleum exploration system based on remote sensing technology, the system being used to implement the method according to any one of claims 1 to 6, characterized in that: include: Acquisition module, extraction module, construction module and exploration module; The acquisition module is used to collect multi-source remote sensing data of the target area and perform preprocessing; The extraction module is used to extract features from the processed data; The construction module is used to construct an oil exploration model based on the extracted features; The exploration module is used to complete oil exploration in a target area using the oil exploration model.

8. The oil exploration system based on remote sensing technology according to claim 7, characterized in that: The workflow of the acquisition module includes: Radiation correction: Among them, L λ Indicates the corrected radiance, L raw Indicates the original radiation value, K cal Indicates the calibration factor, K sat Indicates the sensor saturation value; Geometric correction: Where (x, y) represents the original image coordinates, (x′, y′) represents the corrected coordinates, and a1, a2, a3, b1, b2, and b3 represent the affine transformation parameters. Atmospheric correction: Among them, L surface Represents the surface reflectivity, L λ Indicates the corrected radiance, L path represents the atmospheric path radiation, and τ represents the atmospheric transmittance.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 6 is implemented.

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