A method, device, and storage medium for earthquake epicenter prediction based on satellite cloud images.

Abstract

This invention provides a method, apparatus, and storage medium for earthquake epicenter prediction based on satellite cloud images, relating to the field of earthquake prediction technology. The method includes steps S1 to S4: S1. Acquire a satellite cloud image. S2. Extract features from the satellite cloud image and determine if a circular edge feature exists. S3. When a circular edge feature is detected in the satellite cloud image, identify the center of the circular edge feature. S4. Mark the center of the circle as the earthquake epicenter. This invention not only accurately predicts earthquakes but also provides high accuracy in locating the epicenter's latitude and longitude coordinates. Furthermore, direct identification based on satellite cloud images eliminates the need to process massive amounts of data, improving earthquake prediction efficiency.

Description

Technical Field

[0001] This invention relates to the field of earthquake prediction technology, and more specifically, to a method, apparatus, and storage medium for predicting earthquake epicenters based on satellite cloud images. Background Technology

[0002] Earthquakes are among the greatest natural disasters; powerful earthquakes can instantly destroy buildings and pose a significant threat to life. Earthquake prediction remains a major global challenge. The appearance of thermal anomalies before earthquakes is a relatively common natural phenomenon. Currently, with the development of satellite remote sensing technology, using this technology to monitor pre-earthquake thermal anomalies is a new area of ​​research in earthquake prediction.

[0003] In advanced technologies, people use thermal infrared remote sensing data and its inversion parameters to detect and analyze thermal changes before and after earthquakes. They have discovered abnormal increases in thermal infrared or ground temperature before earthquakes in a large number of earthquakes, thus enabling short-term and medium- to long-term earthquake prediction.

[0004] Satellite remote sensing has advantages such as wide observation range, large information volume, and fast information acquisition. However, due to the influence of actual topography, atmospheric temperature, and nimbostratus clouds, it is difficult to accurately distinguish which areas' anomalies are affected by earthquakes and which are affected by normal temperature and cloud cover when using satellite remote sensing methods to extract thermal infrared anomaly information. Therefore, it is impossible to accurately establish the correlation between remote sensing data anomalies and earthquake occurrence, making it difficult to locate the epicenter before an earthquake. Currently, there are few successful cases of earthquake prediction using infrared remote sensing methods.

[0005] In view of this, the applicant hereby submits this application after studying the existing technology. Summary of the Invention

[0006] The present invention provides a method, apparatus and storage medium for earthquake epicenter prediction based on satellite cloud images, in order to improve at least one of the above-mentioned technical problems.

[0007] First aspect

[0008] This invention provides a method for earthquake epicenter prediction based on satellite cloud images, which includes steps S1 to S4.

[0009] S1. Obtain satellite cloud images.

[0010] S2. Based on the satellite cloud image, perform feature extraction and determine whether there are arc edge features in the satellite cloud image.

[0011] S3. When it is determined that there is a circular arc edge feature in the satellite cloud image, identify the center of the circular arc edge feature.

[0012] S4. Mark the center of the circle as the earthquake epicenter.

[0013] In an optional embodiment, step S2 specifically includes steps S21 to S22:

[0014] S21. Based on the satellite cloud image, use an edge extraction algorithm to extract the edge features in the satellite cloud image.

[0015] S22. Determine whether there are circular edge features in the satellite cloud image based on edge characteristics.

[0016] In an optional embodiment, step S3 specifically includes steps S31 to S33:

[0017] S31. When it is determined that there are arc edge features in the satellite cloud image, at least two chords are obtained based on the arc edge features.

[0018] S32. Obtain the perpendicular bisectors of at least two chords respectively.

[0019] S33. Obtain the center of the arc edge feature based on the perpendicular bisectors of at least two chords.

[0020] In an optional embodiment, the number of strings is greater than 2.

[0021] In an optional embodiment, step S33 specifically includes steps S331 to S32:

[0022] S331. Obtain the coordinates of multiple intersection points between the perpendicular bisectors of at least two chords.

[0023] S332. Obtain the average of the coordinates of multiple intersection points as the center of the arc edge feature.

[0024] In an optional embodiment, step S31 specifically includes steps S311 to S313:

[0025] S311. When it is determined that there are arc edge features in the satellite cloud image, mark the arc edge features.

[0026] S312. Segment the marked arc edge features to obtain at least two small arc segments.

[0027] S313. Obtain the chords of the small arcs respectively, so as to obtain at least two chords.

[0028] In one optional embodiment, the satellite cloud image is satellite image data acquired by the Himawari-8 satellite.

[0029] The second aspect

[0030] This invention provides an earthquake epicenter prediction device based on satellite cloud images, comprising:

[0031] The satellite cloud image acquisition module is used to acquire satellite cloud images.

[0032] The feature extraction module is used to extract features from satellite cloud images and determine whether there are arc edge features in the satellite cloud images.

[0033] The center of the circle recognition module is used to identify the center of the arc edge feature when it is determined that there is an arc edge feature in the satellite cloud image.

[0034] The epicenter marking module is used to mark the center of a circle as the earthquake epicenter.

[0035] In an optional embodiment, the feature extraction module includes:

[0036] The feature extraction unit is used to extract edge features from satellite cloud images using an edge extraction algorithm.

[0037] The feature recognition unit is used to determine whether there are arc edge features in the satellite cloud image based on edge features.

[0038] In an optional embodiment, the center recognition module includes:

[0039] The string extraction unit is used to obtain at least two strings based on the arc edge features when the presence of arc edge features is detected in the satellite cloud image.

[0040] The bisector acquisition unit is used to acquire the perpendicular bisectors of at least two chords respectively.

[0041] The center acquisition unit is used to obtain the center of the arc edge feature based on the perpendicular bisectors of at least two chords.

[0042] In an optional embodiment, the number of chords is greater than 2. The center acquisition unit includes:

[0043] The intersection point coordinate acquisition sub-unit is used to obtain the coordinates of multiple intersection points between the perpendicular bisectors of at least two chords.

[0044] The center acquisition sub-unit is used to obtain the average of the coordinates of multiple intersection points as the center of the arc edge feature.

[0045] In an optional embodiment, the string extraction unit includes:

[0046] The marker subunit is used to mark the arc edge features when they are detected in the satellite cloud image.

[0047] Segmentation sub-units are used to segment the marked arc edge features to obtain at least two small arc segments.

[0048] The string extraction subunit is used to extract the strings of the small arcs separately to obtain at least two strings.

[0049] Third aspect

[0050] This invention provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the earthquake epicenter prediction method based on satellite cloud imagery as described in any paragraph of the first aspect.

[0051] By adopting the above technical solution, the present invention can achieve the following technical effects:

[0052] The earthquake epicenter prediction method based on satellite cloud images, as described in this invention, not only accurately predicts earthquakes but also achieves high precision in locating the epicenter's latitude and longitude coordinates. Furthermore, it directly identifies epicenters from satellite cloud images, eliminating the need to process massive amounts of data and thus improving earthquake prediction efficiency.

[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating the earthquake epicenter prediction method provided in the first embodiment of the present invention.

[0056] Figure 2 This is an example diagram of the earthquake epicenter prediction method provided in the first embodiment of the present invention.

[0057] Figure 3 This is a schematic diagram of the earthquake epicenter prediction device provided in the second embodiment of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0060] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0061] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0062] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0063] The use of "first" and "second" in the embodiments is merely to distinguish similar objects and does not represent a specific ordering of objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permitted. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0064] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0065] Example 1:

[0066] Please see Figure 1 and Figure 2 The first embodiment of the present invention provides a method for earthquake epicenter prediction based on satellite cloud images, which can be executed by an earthquake epicenter prediction device based on satellite cloud images (hereinafter referred to as: prediction device). In particular, it is executed by one or more processors in the prediction device to implement steps S1 to S4.

[0067] S1. Obtain satellite cloud images.

[0068] Specifically, this patent uses conventional satellite meteorological data instead of traditional satellite thermal infrared remote sensing data, and can achieve earthquake prediction and accurate epicenter location simply by identifying the arc features of satellite meteorological images.

[0069] Preferably, in this embodiment of the invention, the satellite cloud image is satellite image data collected by the Himawari-8 satellite. It should be noted that in other embodiments, the satellite cloud image can be satellite cloud image taken by any other meteorological satellite, and this invention does not specifically limit this.

[0070] It is understood that the prediction device may be an electronic device with computing power, such as a portable laptop computer, desktop computer, server, smartphone or tablet computer.

[0071] S2. Based on the satellite cloud image, perform feature extraction and determine whether there are arc edge features in the satellite cloud image.

[0072] Specifically, through extensive research, the inventors discovered that whenever an arc-shaped cloud layer / cloud mass appears in a satellite cloud image, there is a high probability of an earthquake occurring, and the epicenter of the earthquake is highly likely to be located at the center of the arc. Therefore, they proposed an earthquake epicenter prediction method based on satellite meteorological cloud images.

[0073] Based on the above embodiments, in an optional embodiment of the present invention, step S2 specifically includes steps S21 to S22:

[0074] S21. Based on the satellite cloud image, use an edge extraction algorithm to extract the edge features in the satellite cloud image.

[0075] Preferably, the edge extraction algorithm is the Canny edge extraction algorithm. In other embodiments, any existing edge extraction algorithm can be used, and the present invention does not specifically limit it.

[0076] S22. Determine whether there are circular edge features in the satellite cloud image based on edge characteristics.

[0077] S3. When an arc edge feature is detected in the satellite cloud image, the center of the arc edge feature is identified. Specifically, in this embodiment, the center is located by obtaining the perpendicular bisector of the arc. In other embodiments, any existing method for locating the center can be used, and this invention does not specifically limit this method.

[0078] Based on the above embodiments, in an optional embodiment of the present invention, step S3 specifically includes steps S31 to S33:

[0079] S31. When a circular arc edge feature is detected in the satellite cloud image, at least two chords are obtained based on the circular arc edge feature. Specifically, step S31 includes steps S311 to S313:

[0080] S311. When it is determined that there are arc edge features in the satellite cloud image, mark the arc edge features.

[0081] S312. Segment the marked arc edge features to obtain at least two small arc segments.

[0082] S313. Obtain the chords of the small arcs respectively, so as to obtain at least two chords.

[0083] In this embodiment, the arc edge feature is segmented, and then the chord of each segment is obtained. Therefore, the small arcs corresponding to the chords are independent. In other embodiments, any two points can be directly extracted from the arc edge feature to obtain the chord on the arc edge feature. The small arcs corresponding to the chords may overlap. This invention does not impose specific limitations on this.

[0084] S32. Obtain the perpendicular bisectors of at least two chords respectively.

[0085] Specifically, to find the perpendicular bisector, you only need to find the midpoint of the chord and then draw a perpendicular line from the midpoint to the chord.

[0086] S33. Obtain the center of the arc edge feature based on the perpendicular bisectors of at least two chords.

[0087] It is understandable that the intersection of the perpendicular bisectors of the chords of the arc is the center of the arc. It should be noted that in actual meteorological cloud maps, the shape of clouds is not necessarily a perfectly regular circle. Therefore, in this embodiment, the number of chords is greater than 2. The position of the center is obtained using the mean method. Preferably, based on the above embodiment, in an optional embodiment of the present invention, step S33 specifically includes steps S331 to S32:

[0088] S331. Obtain the coordinates of multiple intersection points between the perpendicular bisectors of at least two chords.

[0089] S332. Obtain the average of the coordinates of multiple intersection points as the center of the arc edge feature.

[0090] Specifically, by taking the average of the coordinates of the intersection points between multiple perpendicular bisectors, a more accurate center of the arc edge feature can be obtained, which has great practical significance.

[0091] S4. Mark the center of the circle as the earthquake epicenter.

[0092] The earthquake epicenter prediction method based on satellite cloud images, as described in this invention, not only accurately predicts earthquakes but also achieves high precision in locating the epicenter's latitude and longitude coordinates. Furthermore, it directly identifies epicenters from satellite cloud images, eliminating the need to process massive amounts of data and thus improving earthquake prediction efficiency.

[0093] To facilitate understanding of the present invention, a specific example is given below to illustrate the application of this embodiment. For example... Figure 2As shown, the data uses the magnitude 4.5 earthquake that occurred in the sea area of ​​Pingtung County, Taiwan, on March 23, 2022, at 01:06:55 as an example. The actual epicenter is circled in the figure, with latitude: 22.12°, longitude: 121.37°, and depth: 19 km. The predicted epicenter, obtained from the meteorological cloud image, is the intersection of the perpendicular bisectors, which is 23 km away from the actual epicenter.

[0094] Example 2

[0095] like Figure 2 As shown, this embodiment of the invention provides an earthquake epicenter prediction device based on satellite cloud images, which includes:

[0096] Satellite cloud image acquisition module 1 is used to acquire satellite cloud images.

[0097] Feature extraction module 2 is used to extract features based on satellite cloud images and determine whether there are arc edge features in the satellite cloud images.

[0098] The center of the circle identification module 3 is used to identify the center of the arc edge feature when it is determined that there is an arc edge feature in the satellite cloud image.

[0099] Epicenter marking module 4 is used to mark the center of a circle as the earthquake epicenter.

[0100] In an optional embodiment, the feature extraction module 2 includes:

[0101] The feature extraction unit is used to extract edge features from satellite cloud images using an edge extraction algorithm.

[0102] The feature recognition unit is used to determine whether there are arc edge features in the satellite cloud image based on edge features.

[0103] In an optional embodiment, the center recognition module 3 includes:

[0104] The string extraction unit is used to obtain at least two strings based on the arc edge features when the presence of arc edge features is detected in the satellite cloud image.

[0105] The bisector acquisition unit is used to acquire the perpendicular bisectors of at least two chords respectively.

[0106] The center acquisition unit is used to obtain the center of the arc edge feature based on the perpendicular bisectors of at least two chords.

[0107] In an optional embodiment, the number of chords is greater than 2. The center acquisition unit includes:

[0108] The intersection point coordinate acquisition sub-unit is used to obtain the coordinates of multiple intersection points between the perpendicular bisectors of at least two chords.

[0109] The center acquisition sub-unit is used to obtain the average of the coordinates of multiple intersection points as the center of the arc edge feature.

[0110] In an optional embodiment, the string extraction unit includes:

[0111] The marker subunit is used to mark the arc edge features when they are detected in the satellite cloud image.

[0112] Segmentation sub-units are used to segment the marked arc edge features to obtain at least two small arc segments.

[0113] The string extraction subunit is used to extract the strings of the small arcs separately to obtain at least two strings.

[0114] Example 3

[0115] This invention provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the earthquake epicenter prediction method based on satellite cloud images as described in any paragraph of Embodiment 1.

[0116] In the several embodiments provided in this invention, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0117] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0118] If the aforementioned functions are implemented as software functional modules 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 the present invention, or the part that contributes to the prior art, or a part 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, electronic device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. 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. In the absence of further restrictions, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0119] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for earthquake epicenter prediction based on satellite cloud images, characterized in that, Include: Obtain satellite cloud images; Based on the satellite cloud image, feature extraction is performed, and it is determined whether there are arc edge features in the satellite cloud image; When a circular arc edge feature is detected in the satellite cloud image, the center of the circular arc edge feature is identified. The center of the circle is marked as the earthquake epicenter; When a circular edge feature is detected in the satellite cloud image, the center of the circular edge feature is identified, specifically including: When it is determined that there are arc edge features in the satellite cloud image, at least two chords are obtained based on the arc edge features; Obtain the perpendicular bisectors of the at least two chords respectively; The center of the arc edge feature is obtained based on the perpendicular bisectors of the at least two chords.

2. The earthquake epicenter prediction method based on satellite cloud images according to claim 1, characterized in that, Based on the satellite cloud image, feature extraction is performed, and it is determined whether there are arc edge features in the satellite cloud image, specifically including: Based on the satellite cloud image, an edge extraction algorithm is used to extract edge features from the satellite cloud image; The presence of arc-shaped edge features in the satellite cloud image is determined based on the edge features described.

3. The earthquake epicenter prediction method based on satellite cloud images according to claim 1, characterized in that, The number of strings is greater than 2; The center of the arc edge feature is obtained based on the perpendicular bisectors of the at least two chords, specifically including: Obtain the coordinates of multiple intersection points between the perpendicular bisectors of the at least two chords; The average value of the coordinates of the multiple intersection points is used as the center of the arc edge feature.

4. The earthquake epicenter prediction method based on satellite cloud images according to claim 1, characterized in that, When a circular arc edge feature is detected in the satellite cloud image, at least two chords are obtained based on the circular arc edge feature, specifically including: When a circular arc edge feature is detected in the satellite cloud image, the circular arc edge feature is marked. The marked arc edge features are segmented to obtain at least two small arc segments; Obtain the chords of the small arcs separately to obtain the at least two chords.

5. The earthquake epicenter prediction method based on satellite cloud images according to any one of claims 1 to 4, characterized in that, The satellite cloud image mentioned is satellite image data collected by the Sunflower-8 satellite.

6. An earthquake epicenter prediction device based on satellite cloud images, characterized in that, Include: Satellite cloud image acquisition module, used to acquire satellite cloud images; The feature extraction module is used to extract features based on the satellite cloud image and determine whether there are arc edge features in the satellite cloud image; The center of the circle recognition module is used to identify the center of the arc edge feature when it is determined that there is an arc edge feature in the satellite cloud image; An epicenter marking module is used to mark the center of the circle as the earthquake epicenter; The center-of-circle identification module includes: The chord extraction unit is used to obtain at least two chords based on the arc edge features when it is determined that there are arc edge features in the satellite cloud image. A bisecting line acquisition unit is used to acquire the perpendicular bisectors of the at least two chords respectively; The center acquisition unit is used to acquire the center of the arc edge feature based on the perpendicular bisectors of the at least two chords.

7. The earthquake epicenter prediction device based on satellite cloud imagery according to claim 6, characterized in that, The feature extraction module includes: The feature extraction unit is used to extract edge features from the satellite cloud image using an edge extraction algorithm. The feature recognition unit is used to determine whether there are arc edge features in the satellite cloud image based on the edge features.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the earthquake epicenter prediction method based on satellite cloud imagery as described in any one of claims 1 to 5.

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