Three-dimensional geophysical scattered point data processing method and device
By using an inverse window filtering method, contour maps are generated to select high-quality data. Combined with three-dimensional kriging rules and the inversion method, three-dimensional geophysical scattered data are processed, which solves the problem of data deviation in filtering and achieves higher accuracy in data preservation and effective preservation of geological information.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2024-12-05
- Publication Date
- 2026-06-05
AI Technical Summary
Existing technologies for filtering 3D geophysical scatter data can easily lead to data bias, making it difficult to accurately preserve geological information and affecting the effectiveness of subsequent geological interpretation.
An inverse window filtering method is adopted, which selects high-quality data by generating contour maps, retains high-quality data in the target window, and performs smoothing filtering on data outside the window. The data processing is combined with three-dimensional kriging rules and reflection method.
Effectively filtering out abnormal data outside the window preserves more details of the original data, improves the accuracy of data processing, and is beneficial for geological interpretation.
Smart Images
Figure CN122151231A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of electromagnetic exploration data processing technology, and in particular to a method and apparatus for processing three-dimensional geophysical scatter data. Background Technology
[0002] Geophysical scatter data refers to data obtained through scatter measurement methods during geophysical exploration. When using 3D geophysical scatter data for geological interpretation or 3D mapping, it is generally necessary to filter the data to smooth it out. Directly filtering 3D geophysical scatter data can easily introduce significant biases, making the data processing results difficult to control.
[0003] Currently, filtering is typically applied to the entire 3D geophysical data. Although there are many improved filtering methods for data distortion points, the data processing effect is not accurate enough. Furthermore, regardless of the filtering method chosen, some local details in the original data may be smoothed out, key geological information may be ignored, and the subsequent interpretation of geological data may be affected.
[0004] In view of this, it is necessary to propose a three-dimensional geophysical scatter data processing scheme that does not destroy the local details of the original data when performing smoothing filtering on the three-dimensional geophysical data, thereby improving the accuracy of data processing. Summary of the Invention
[0005] This application discloses a method and apparatus for processing three-dimensional geophysical scatter data.
[0006] In a first aspect, this application discloses a method for processing three-dimensional geophysical scatter data, the method comprising:
[0007] Obtain the raw data of the three-dimensional geophysical scatter points and perform a first preprocessing on the raw data;
[0008] Generate a contour map of the data obtained after the first preprocessing, and select the target window region corresponding to the high-quality data from the contour map to obtain the first target window data;
[0009] The data obtained after the first preprocessing is subjected to a first filtering process based on a preset weighting algorithm.
[0010] Based on the node coordinate range of the target window region, extract the second target data outside the target window range from the data obtained after the first filtering process;
[0011] Merge the first target window data and the second target data into an ordered data volume;
[0012] Generate transition region data corresponding to the ordered data body and perform a second preprocessing on the transition region data;
[0013] The data obtained after the second preprocessing is merged with the ordered data volume to obtain the target data;
[0014] The target data is traversed to obtain the duplicate points in the target data. The duplicate point positions of the second preprocessed transition area data are selected and retained, and the remaining positions are deleted to obtain the data processing result corresponding to the original data.
[0015] Optionally, the step of acquiring the raw data of the three-dimensional geophysical scatter points and performing a first preprocessing on the raw data includes:
[0016] Obtain the raw data of 3D geophysical scatter points;
[0017] The original data is interpolated using the first grid based on the three-dimensional kriging rule;
[0018] The data obtained after interpolation of the first grid is processed by expanding the edges using the reflection method.
[0019] Optionally, the step of expanding the edges of the data obtained after interpolating the first grid using the reflection method includes:
[0020] Symmetrical filling is performed using the outermost edge of the data obtained after interpolation of the first grid as the axis;
[0021] The number of data nodes added outward is determined based on the number of nodes N in each direction of the filter window.
[0022] The edge expansion process is performed based on the determined number of nodes to be added outwards.
[0023] Optionally, the step of merging the first target window data and the second target data into an ordered data body includes:
[0024] The merge algorithm is used to merge the first target window data before the first filtering process and the second target data after the first filtering process into an ordered data volume.
[0025] Optionally, the step of generating transition region data corresponding to the ordered data body and performing a second preprocessing on the transition region data includes:
[0026] Based on the node coordinate range of the target window boundary in the target window region, acquire transition zone data within a preset grid spacing in three-dimensional space for the ordered data volume;
[0027] The transition region data is subjected to a second grid interpolation, and the data obtained after the second grid interpolation is subjected to a second filtering process.
[0028] Optionally, the step of performing a first filtering process on the preprocessed data based on a preset weighting algorithm includes:
[0029] The weights w(i,j,k) corresponding to the data obtained after the first preprocessing are calculated according to the following expression, and the data obtained after the first preprocessing is then subjected to a first filtering process.
[0030]
[0031] Among them, W X W represents the number of nodes in the filter window in the X direction. Y W represents the number of nodes in the filter window in the Y direction. Z denoted as the number of nodes in the filter window in the Z direction, and f as the filter coefficient.
[0032] Secondly, this application discloses a three-dimensional geophysical scatter data processing device, the device comprising:
[0033] The data acquisition module is used to acquire the raw data of the three-dimensional geophysical scatter points and perform a first preprocessing on the raw data;
[0034] The window data extraction module is used to generate a contour map of the data obtained after the first preprocessing, and select the target window area corresponding to the high-quality data from the contour map to obtain the first target window data;
[0035] The filtering module is used to perform a first filtering process on the data obtained after the first preprocessing based on a preset weighting algorithm;
[0036] The window data extraction module is used to extract second target data outside the target window range from the data obtained after the first filtering process, based on the node coordinate range of the target window region.
[0037] The data merging module is used to merge the data of the first target window and the data of the second target into an ordered data body;
[0038] The data generation module is used to generate transition region data corresponding to the ordered data body and perform a second preprocessing on the transition region data;
[0039] The data merging module is used to merge the data obtained after the second preprocessing with the ordered data body to obtain the target data;
[0040] The data optimization processing module is used to traverse the target data to obtain the duplicate points in the target data, select and retain the duplicate point positions of the second preprocessed transition area data and delete the remaining positions to obtain the data processing result corresponding to the original data.
[0041] Optionally, the data acquisition module is specifically used to acquire the original data of the three-dimensional geophysical scatter points; perform first grid interpolation on the original data based on the three-dimensional kriging rule; and perform edge expansion processing on the data obtained after first grid interpolation using the reflection method.
[0042] Optionally, the data acquisition module is specifically used to perform symmetrical filling with the outermost edge of the data obtained after interpolation of the first grid as the axis; and to determine the number of data nodes to be added outward based on the number of nodes N in each direction of the filtering window. The edge expansion process is performed based on the determined number of nodes to be added outwards.
[0043] Optionally, the data merging module is specifically used to merge the first target window data before the first filtering process and the second target data after the first filtering process into an ordered data body using a merge algorithm.
[0044] Optionally, the data generation module is specifically used to obtain transition zone data within a preset grid spacing in three-dimensional space from the target window boundary for the ordered data volume based on the node coordinate range of the target window boundary in the target window region; perform a second grid interpolation on the transition zone data; and perform a second filtering process on the data obtained after the second grid interpolation.
[0045] Optionally, the filtering module is specifically used to calculate the weight w(i,j,k) corresponding to the data obtained after the first preprocessing according to the following expression, and to perform a first filtering process on the data obtained after the first preprocessing.
[0046]
[0047] Among them, W X W represents the number of nodes in the filter window in the X direction. Y W represents the number of nodes in the filter window in the Y direction. Z denoted as the number of nodes in the filter window in the Z direction, and f as the filter coefficient.
[0048] Thirdly, this application discloses an electronic device comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to perform the method as described in any of the preceding aspects.
[0049] Fourthly, this application discloses a non-transitory computer-readable storage medium in which, when the instructions in the storage medium are executed by a processor of an electronic device, enable the electronic device to perform the methods described in any of the preceding aspects.
[0050] Fifthly, this application discloses a computer program product in which, when the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to perform the method described in any of the preceding aspects.
[0051] The technical solution provided in this application may include the following beneficial effects:
[0052] This application pertains to electromagnetic exploration data processing technology in the field of geophysical exploration, specifically a filtering method for processing three-dimensional scattered data. The application aims to provide a smoothing filtering scheme that selectively preserves high-quality original data from three-dimensional scattered data.
[0053] Conventional filtering methods typically perform filtering within a defined window. The innovation of this application lies in its "reverse window" filtering, which retains high-quality data within the target window while only smoothing and filtering data outside the window. This not only effectively filters out abnormal data outside the window but also preserves more detailed information from the original data, providing a targeted filtering solution for the target window region. Specifically, based on conventional filtering, a window range is set for the 3D geophysical scatter data, and abnormal regions are selectively chosen within this window, using the original data within that region. Furthermore, generating contour maps allows for the intuitive selection of high-quality original data, facilitating the preservation of high-quality data within the target window and ensuring high-precision reconstruction of the original data.
[0054] Furthermore, by filtering the three-dimensional scatter data of magnetotelluric data from a region in northwestern China, the results show that when applying the proposed method to smooth and filter the three-dimensional geophysical data, the geological details of the original data are preserved to the maximum extent, thereby improving the accuracy of data processing and making it more conducive to geological interpretation. Attached Figure Description
[0055] Figure 1 A flowchart of a three-dimensional geophysical scatter data processing method provided in this application.
[0056] Figure 2 The above is a contour map of an anomaly plane before filtering, provided in this application.
[0057] Figure 3 The contour map of an anomaly plane after conventional filtering provided in this application.
[0058] Figure 4 The inverse window filtering contour map of a certain layer of anomaly plane provided in this application.
[0059] Figure 5 The three-dimensional distribution map of anomalies before inverse window filtering provided in this application.
[0060] Figure 6 The three-dimensional distribution map of anomalies after inverse window filtering provided in this application.
[0061] Figure 7 A structural diagram of a three-dimensional geophysical scatter data processing device provided in this application.
[0062] Figure 8 A block diagram of an electronic device provided in this application.
[0063] Figure 9 A block diagram of another electronic device provided in this application. Detailed Implementation
[0064] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0065] To address the aforementioned issues, this application provides a method and apparatus for processing three-dimensional geophysical scatter data. The method for processing three-dimensional geophysical scatter data provided in this application will be described below.
[0066] Example 1
[0067] Reference Figure 1 The present application provides a flowchart of a three-dimensional geophysical scatter data processing method, which may include the following steps:
[0068] Step S101: Obtain the raw data of the three-dimensional geophysical scatter points and perform the first preprocessing on the raw data.
[0069] In one scenario, the raw data can be preprocessed as follows: obtain the raw data of three-dimensional geophysical scatter points; perform a first grid interpolation on the raw data based on the three-dimensional kriging rule; and perform edge expansion processing on the data obtained after the first grid interpolation using the reflection method.
[0070] Specifically, the data obtained after interpolation of the first grid can be expanded as follows: symmetrical filling is performed with the outermost edge of the data obtained after interpolation of the first grid as the axis; the number of data nodes added outward is determined based on the number of nodes N in each direction of the filtering window. The edge expansion process is performed based on the determined number of nodes to be added outwards.
[0071] Step S102: Generate a contour map of the data obtained after the first preprocessing, and select the target window region corresponding to the high-quality data from the contour map to obtain the first target window data.
[0072] Step S103: Perform a first filtering process on the data obtained after the first preprocessing based on a preset weighting algorithm.
[0073] Specifically, the weights w(i,j,k) corresponding to the data obtained after the first preprocessing can be calculated according to the following expression, and the data obtained after the first preprocessing can be subjected to the first filtering process.
[0074]
[0075] Among them, W X W represents the number of nodes in the filter window in the X direction. Y W represents the number of nodes in the filter window in the Y direction. Z denoted as the number of nodes in the filter window in the Z direction, and f as the filter coefficient.
[0076] Step S104: Based on the node coordinate range of the target window region, extract the second target data outside the target window range from the data obtained after the first filtering process.
[0077] Step S105: Merge the first target window data and the second target data into an ordered data body.
[0078] In one scenario, a merge algorithm can be used to combine the first target window data before the first filtering process with the second target data after the first filtering process into an ordered data volume.
[0079] Step S106: Generate transition region data corresponding to the ordered data body and perform a second preprocessing on the transition region data.
[0080] In one scenario, the transition zone data can be preprocessed as follows: based on the node coordinate range of the target window boundary within the target window region, transition zone data within a preset grid spacing in three-dimensional space is obtained for the ordered data volume; the transition zone data undergoes a second grid interpolation, and the data obtained after the second grid interpolation undergoes a second filtering process. Preferably, the preset grid spacing can be 1 to 3 times the grid spacing.
[0081] Step S107: Merge the data obtained after the second preprocessing with the ordered data volume to obtain the target data.
[0082] Step S108: Traverse the target data to obtain the duplicate points in the target data, select and retain the duplicate point positions of the second preprocessed transition area data and delete the remaining positions to obtain the data processing result corresponding to the original data.
[0083] This application pertains to electromagnetic exploration data processing technology in the field of geophysical exploration, specifically a filtering method for processing three-dimensional scattered data. The application aims to provide a smoothing filtering scheme that selectively preserves high-quality original data from three-dimensional scattered data.
[0084] Conventional filtering methods typically perform filtering within a defined window. The innovation of this application lies in its "reverse window" filtering, which retains high-quality data within the target window while only smoothing filtering is applied to data outside the window. This not only effectively filters out abnormal data outside the window but also preserves more detailed information from the original data, providing a targeted filtering solution for the target window region. Specifically, based on conventional filtering, a window range is set for the 3D geophysical scatter data, and anomaly regions are selectively chosen within this window, using the original data within that region. Furthermore, generating contour maps allows for the intuitive selection of high-quality original data, facilitating the preservation of high-quality data within the target window and ensuring high-precision reconstruction of the original data.
[0085] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions involved are not necessarily required by this application.
[0086] The following example illustrates the 3D geophysical scatter data processing method provided in this application. Please refer to [link / reference]. Figures 2 to 6 The following is an application example of this solution in the process of processing three-dimensional scattered magnetotelluric data in a region in northwestern my country.
[0087] (1) Obtain the original data and denote it as data A. Perform three-dimensional kriging regular grid interpolation on data A. The interpolation grid distance is less than or equal to the original data point interval. The grid node density after interpolation is about 1 to 3 times that of the original data. The search radius is 2 to 3 times that of the original data point interval, and data B is obtained.
[0088] (2) The data B is expanded using the reflection method. The data is symmetrically filled with the outermost edge of the data body as the axis. The number of data nodes to be added is determined according to the number of nodes in each direction of the filter window (usually selected as 3, 5, 7, etc.). After the expansion, the data C is obtained.
[0089] (3) For data C, in one case, it can be displayed on the contour map, and the part with better data quality can be delineated in a targeted manner. The data within the window range can be obtained by obtaining the grid node coordinate range in the X, Y and Z directions. In another case, the grid node coordinate range in the X, Y and Z directions of the target area can be directly selected to obtain the window data D of the target area.
[0090] (4) Filter the data C to obtain the filtered data E.
[0091] The filtering method used is Gaussian filtering, and the filtering window is W. X W Y and W Z Among them, W X W represents the number of nodes in the filter window in the X direction. Y W represents the number of nodes in the filter window in the Y direction. Z The number of filter window nodes in the Z direction is 3, 5, or 7. The number of filter window nodes in the three directions is generally selected as 3, 5, or 7, and should not exceed 7. f is the filter coefficient, which is between 0.01 and 2.0.
[0092] Specifically, the weight w(i,j,k) of each data point within the filter window can be calculated using the following expression:
[0093]
[0094] Among them, W X W represents the number of nodes in the filter window in the X direction. Y W represents the number of nodes in the filter window in the Y direction. Z denoted as the number of nodes in the filter window in the Z direction, and f as the filter coefficient.
[0095] (5) For data E, obtain data F outside the window based on the coordinate range of the selected target window area nodes.
[0096] (6) Use a merge algorithm to merge the data D inside the window before filtering and the data F outside the window after filtering into an ordered data volume G.
[0097] (7) For data G, based on the range of grid node coordinates in the X, Y, and Z directions of the target window boundary, obtain data H within 1 to 3 times the grid spacing in three-dimensional space, i.e., transition zone data.
[0098] (8) Perform three-dimensional kriging regular grid interpolation on the data H. It should be noted that the interpolation parameters here are the same as in step (1), and the data I is obtained.
[0099] (9) Perform three-dimensional filtering on data I to obtain data J. The filtering method used is median filtering, and the filtering parameters used are:
[0100] The filter window is W X W Y and W Z Among them, W X W represents the number of nodes in the filter window in the X direction. Y W represents the number of nodes in the filter window in the Y direction. Z This represents the number of filter window nodes in the Z direction. The number of filter window nodes in the three directions is generally selected as 3, 5, 7, etc., and should not exceed 7.
[0101] (10) Merge the data J after the interpolation and filtering of the transition region with the data G. The merging method is the same as step (6) to obtain the data K.
[0102] (11) For data K, by traversing the set of points, count the duplicate points, select and keep the duplicate point positions of data J and delete the remaining positions to obtain data M, which is the final processing result.
[0103] from Figures 2 to 6 It is clear that after inverse window filtering, the target anomaly information can be basically preserved, and more original geological information details are retained in a targeted manner, which is more conducive to geological interpretation.
[0104] Example 2
[0105] like Figure 7 The diagram shown is a structural diagram of a three-dimensional geophysical scatter data processing device provided in this application. The device includes:
[0106] Data acquisition module 21 is used to acquire the raw data of three-dimensional geophysical scatter points and perform a first preprocessing on the raw data;
[0107] Window data extraction module 22 is used to generate a contour map of the data obtained after the first preprocessing, and select the target window area corresponding to the high-quality data from the contour map to obtain the first target window data;
[0108] Filtering module 23 is used to perform a first filtering process on the data obtained after the first preprocessing based on a preset weighting algorithm;
[0109] The window data extraction module 22 is used to extract second target data outside the target window range from the data obtained after the first filtering process, based on the node coordinate range of the target window region.
[0110] Data merging module 24 is used to merge the first target window data and the second target data into an ordered data body;
[0111] Data generation module 25 is used to generate transition region data corresponding to the ordered data body and perform a second preprocessing on the transition region data;
[0112] The data merging module 24 is used to merge the data obtained after the second preprocessing with the ordered data body to obtain the target data.
[0113] The data optimization processing module 26 is used to traverse the target data to obtain the duplicate points in the target data, select and retain the duplicate point positions of the second preprocessed transition area data and delete the remaining positions to obtain the data processing result corresponding to the original data.
[0114] In one scenario, the data acquisition module 21 is specifically used to acquire the original data of the three-dimensional geophysical scatter points; perform first grid interpolation on the original data based on the three-dimensional kriging rule; and perform edge expansion processing on the data obtained after the first grid interpolation using the reflection method.
[0115] Specifically, the data acquisition module 21 is used to perform symmetrical filling with the outermost edge of the data obtained after interpolation of the first grid as the axis; and to determine the number of data nodes to be added outward based on the number of nodes N in each direction of the filtering window. The edge expansion process is performed based on the determined number of nodes to be added outwards.
[0116] In one scenario, the data merging module 24 is specifically used to merge the first target window data before the first filtering process and the second target data after the first filtering process into an ordered data body using a merge algorithm.
[0117] In one scenario, the data generation module 25 is specifically used to obtain transition zone data within a preset grid spacing in three-dimensional space from the target window boundary based on the node coordinate range of the target window boundary in the target window region; perform a second grid interpolation on the transition zone data; and perform a second filtering process on the data obtained after the second grid interpolation.
[0118] In one scenario, the filtering module 23 is specifically used to calculate the weight w(i,j,k) corresponding to the data obtained after the first preprocessing according to the following expression, and to perform a first filtering process on the data obtained after the first preprocessing.
[0119]
[0120] Among them, W X W represents the number of nodes in the filter window in the X direction. Y W represents the number of nodes in the filter window in the Y direction. Z denoted as the number of nodes in the filter window in the Z direction, and f as the filter coefficient.
[0121] This application pertains to electromagnetic exploration data processing technology in the field of geophysical exploration, specifically a filtering method for processing three-dimensional scattered data. The application aims to provide a smoothing filtering scheme that selectively preserves high-quality original data from three-dimensional scattered data.
[0122] Conventional filtering methods typically perform filtering within a defined window. The innovation of this application lies in its "reverse window" filtering, which retains high-quality data within the target window while only smoothing and filtering data outside the window. This not only effectively filters out abnormal data outside the window but also preserves more detailed information from the original data, providing a targeted filtering solution for the target window region. Specifically, based on conventional filtering, a window range is set for the 3D geophysical scatter data, and abnormal regions are selectively chosen within this window, using the original data within that region. Furthermore, generating contour maps allows for the intuitive selection of high-quality original data, facilitating the preservation of high-quality data within the target window and ensuring high-precision reconstruction of the original data.
[0123] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.
[0124] Example 3
[0125] Optionally, this application also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the various processes of the above method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.
[0126] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.
[0127] Figure 8 This application provides a block diagram of an electronic device 800. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.
[0128] Reference Figure 8 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.
[0129] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.
[0130] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, images, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.
[0131] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.
[0132] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
[0133] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
[0134] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.
[0135] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 may detect the on / off state of device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.
[0136] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, carrier networks (such as 2G, 3G, 4G, or 5G), or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast operation information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
[0137] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.
[0138] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of an electronic device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.
[0139] Example 4
[0140] Figure 9 A block diagram of another electronic device 1900 provided for this application. For example, electronic device 1900 may be provided as a server.
[0141] Reference Figure 9 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.
[0142] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.
[0143] Example 5
[0144] Fifthly, this application discloses a computer program product in which, when the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to perform the method described in any of the preceding aspects.
[0145] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0146] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0147] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.
[0148] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art 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.
[0149] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0150] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0151] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0152] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0153] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.
[0154] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A method for processing three-dimensional geophysical scatter data, characterized in that, The method includes: Obtain the raw data of the three-dimensional geophysical scatter points and perform a first preprocessing on the raw data; Generate a contour map of the data obtained after the first preprocessing, and select the target window region corresponding to the high-quality data from the contour map to obtain the first target window data; The data obtained after the first preprocessing is subjected to a first filtering process based on a preset weighting algorithm. Based on the node coordinate range of the target window region, extract the second target data outside the target window range from the data obtained after the first filtering process; Merge the first target window data and the second target data into an ordered data volume; Generate transition region data corresponding to the ordered data body and perform a second preprocessing on the transition region data; The data obtained after the second preprocessing is merged with the ordered data volume to obtain the target data; The target data is traversed to obtain the duplicate points in the target data. The duplicate point positions of the second preprocessed transition area data are selected and retained, and the remaining positions are deleted to obtain the data processing result corresponding to the original data.
2. The three-dimensional geophysical scatter data processing method according to claim 1, characterized in that, The step of acquiring the raw data of the three-dimensional geophysical scatter points and performing a first preprocessing on the raw data includes: Obtain the raw data of 3D geophysical scatter points; The original data is interpolated using the first grid based on the three-dimensional kriging rule; The data obtained after interpolation of the first grid is processed by expanding the edges using the reflection method.
3. The three-dimensional geophysical scatter data processing method according to claim 2, characterized in that, The step of expanding the edges of the data obtained after interpolating the first grid using the reflection method includes: Symmetrical filling is performed using the outermost edge of the data obtained after interpolation of the first grid as the axis; The number of data nodes added outward is determined based on the number of nodes N in each direction of the filter window. The edge expansion process is performed based on the determined number of nodes to be added outwards.
4. The three-dimensional geophysical scatter data processing method according to claim 1, characterized in that, The step of merging the first target window data and the second target data into an ordered data body includes: The merge algorithm is used to merge the first target window data before the first filtering process and the second target data after the first filtering process into an ordered data volume.
5. The three-dimensional geophysical scatter data processing method according to claim 1, characterized in that, The step of generating transition region data corresponding to the ordered data body and performing a second preprocessing on the transition region data includes: Based on the node coordinate range of the target window boundary in the target window region, acquire transition zone data within a preset grid spacing in three-dimensional space for the ordered data volume; The transition region data is subjected to a second grid interpolation, and the data obtained after the second grid interpolation is subjected to a second filtering process.
6. The three-dimensional geophysical scatter data processing method according to claim 1, characterized in that, The step of performing a first filtering process on the preprocessed data based on a preset weighting algorithm includes: The weights w(i,j,k) corresponding to the data obtained after the first preprocessing are calculated according to the following expression, and the data obtained after the first preprocessing is then subjected to a first filtering process. Among them, W X W represents the number of nodes in the filter window in the X direction. Y W represents the number of nodes in the filter window in the Y direction. Z denoted as the number of nodes in the filter window in the Z direction, and f as the filter coefficient.
7. A three-dimensional geophysical scatter data processing device, characterized in that, The device includes: The data acquisition module is used to acquire the raw data of the three-dimensional geophysical scatter points and perform a first preprocessing on the raw data; The window data extraction module is used to generate a contour map of the data obtained after the first preprocessing, and select the target window area corresponding to the high-quality data from the contour map to obtain the first target window data; The filtering module is used to perform a first filtering process on the data obtained after the first preprocessing based on a preset weighting algorithm; The window data extraction module is used to extract second target data outside the target window range from the data obtained after the first filtering process, based on the node coordinate range of the target window region. The data merging module is used to merge the data of the first target window and the data of the second target into an ordered data body; The data generation module is used to generate transition region data corresponding to the ordered data body and perform a second preprocessing on the transition region data; The data merging module is used to merge the data obtained after the second preprocessing with the ordered data body to obtain the target data; The data optimization processing module is used to traverse the target data to obtain the duplicate points in the target data, select and retain the duplicate point positions of the second preprocessed transition area data and delete the remaining positions to obtain the data processing result corresponding to the original data.
8. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the method as described in any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.
10. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device implements the method as described in any one of claims 1 to 6.