An undersea terrain quality evaluation system and method based on multi-dimensional indicators

Abstract

The present application provides a seabed topography quality evaluation system and method based on multi-dimensional indicators. The system includes: a ship-surveyed water depth accuracy verification module for refining the ship-surveyed water depth data, calculating the root mean square error between the seabed topography product to be evaluated and the ship-surveyed water depth data, and obtaining the accuracy index of the seabed topography inversion product; a topography structure similarity matching module for comparing the structural similarity index of the seabed topography product to be evaluated with the authoritative topography model, and obtaining the structural similarity index of the seabed topography inversion product; a geological boundary consistency comparison module: for spatially aligning the fault zone boundary predicted by the seabed topography inversion product with the geological structure label data, calculating the F1-score of the predicted fault zone boundary and the geological structure label, and obtaining the geological boundary consistency index of the seabed topography to be evaluated. The advantage of the present application is that it can obtain multi-dimensional, comprehensive, and integrated seabed topography quality evaluation information.

Description

Technical Field

[0001] This application belongs to the field of ocean remote sensing mapping, and specifically relates to a seabed terrain quality evaluation system and method based on multi-dimensional indicators. Background Art

[0002] Seabed topography is a crucial foundational data source for marine geographic information. Marine resource exploration, submarine pipeline construction, and navigation safety all require high-quality topographic data. Failure to do so can lead to erroneous decisions, such as inaccurate resource positioning, engineering accidents, or navigational hazards. Furthermore, seabed topography data plays a crucial role in climate change research and geological disaster early warning (such as tsunamis), and data quality directly impacts the accuracy of these models. According to the United Nations Decade of Ocean Science (2021-2030), approximately 40% of global ocean mapping data is not effectively utilized due to quality issues, highlighting the urgency of quality assessment.

[0003] Currently, seafloor topography quality assessment methods primarily rely on spot checks of localized areas using shipborne bathymetry data, lacking a comprehensive, systematic evaluation across the entire region. These methods also focus on absolute accuracy (e.g., RMSE) or resolution (e.g., grid size). This results in current seafloor topography model products focusing solely on numerical improvements in accuracy or resolution, resulting in poor performance on detailed geomorphic features such as topographic structure and geological boundaries. With the continuous advancement of seafloor topography exploration technologies (e.g., multi-beam bathymetry and high-resolution satellite altimetry gravity prediction), the quality of seafloor topography model products has gradually improved. To comprehensively measure the quality of seafloor topography products, a more systematic, multi-dimensional seafloor topography quality assessment system is needed. However, existing seafloor topography quality assessment methods often use a single accuracy metric without considering detailed feature indicators such as topographic structure similarity and geological boundary consistency. Consequently, a seafloor topography quality assessment system based on multi-dimensional metrics has yet to be established. Summary of the Invention

[0004] The purpose of this application is to overcome the shortcomings of the existing seabed topography quality evaluation that only uses a single precision indicator, and to establish a seabed topography quality evaluation system with multiple dimensions including topographic structure similarity and geological boundary consistency.

[0005] To achieve the above objectives, this application proposes a seabed terrain quality evaluation system based on multi-dimensional indicators, including:

[0006] The ship-surveyed water depth accuracy verification module is used to refine the ship-surveyed water depth data, calculate the root mean square error between the seabed topography product to be evaluated and the ship-surveyed water depth data, and obtain the accuracy index of the seabed topography inversion product;

[0007] The terrain structure similarity matching module is used to determine the comparison area based on the matching of the submarine terrain product to be evaluated with the global authoritative terrain model, compare the structural similarity index of the submarine terrain product to be evaluated with the authoritative terrain model, and obtain the structural similarity index of the submarine terrain inversion product;

[0008] Geological boundary consistency comparison module: used to align the fault zone boundary predicted by the seabed topography inversion product with the geological structure label data space, calculate the F1-score of the predicted fault zone boundary and the geological structure label, and obtain the geological boundary consistency index of the seabed topography to be evaluated.

[0009] As an improvement to the above system, the processing process of the ship water depth accuracy verification module includes:

[0010] Step A1: Based on the survey line time, longitude and latitude information, and effective water depth range, the ship-surveyed water depth data is subjected to long-wave error correction and reference unification to suppress noise, thereby obtaining refined ship-surveyed water depth data.

[0011] Step A2: Combine the sea and land mask information to calculate the root mean square error between the seabed topography product to be evaluated and the ship-measured water depth data to obtain the accuracy index of the seabed topography inversion product. :

[0012]

[0013] in, For the The ship-measured water depth values at each verification point; For the The seabed topography value of the product to be evaluated at each verification point; For the The land and sea mask values of the verification points; is the total number of verification points.

[0014] As an improvement to the above system, the processing of the terrain structure similarity matching module includes:

[0015] Step B1: Match the global authoritative terrain model to the same spatial range and resolution based on the latitude and longitude of the seabed topography product to be evaluated, ensuring that the assessment area completely overlaps;

[0016] Step B2: Using the authoritative terrain model as a reference, compare the structural similarity index of the inverted terrain and the reference terrain to obtain the structural similarity index of the seabed terrain inversion product. :

[0017]

[0018] in, Represents the inverted terrain and reference terrain The mean of Represents the inverted terrain and reference terrain The standard deviation of and the covariance between the two; is a stability constant.

[0019] As an improvement to the above system, the value of the stability constant is:

[0020] 、

[0021] in, It is the difference between the maximum and minimum values of the terrain in the assessment area.

[0022] As an improvement to the above system, the processing of the geological boundary consistency comparison module includes:

[0023] Step C1: Unify the coordinate system of the fracture zone boundaries predicted by the seafloor topography inversion product and the geological structure label data, resample and crop them to achieve consistency in spatial reference, resolution and range;

[0024] Step C2: Calculate the F1-score of the fault zone boundary predicted by the seabed topography inversion product and the geological structure label to obtain the geological boundary consistency index of the seabed topography to be evaluated:

[0025]

[0026] Where P represents the precision:

[0027]

[0028] R represents the recall rate:

[0029]

[0030] in, is the number of pixels predicted to be the boundary and whose true label is the boundary; is the number of pixels predicted as boundary but the true label is non-boundary; The number of pixels whose true label is boundary but predicted as non-boundary.

[0031] This application also provides a method for evaluating seabed topography quality based on multi-dimensional indicators, which is implemented based on the above system and includes:

[0032] Step 1: Based on the survey line time, longitude and latitude information, and effective range of water depth, the ship-surveyed water depth data is corrected for long-wave errors and benchmarked to suppress noise, obtaining refined ship-surveyed water depth data.

[0033] Step 2: Combine the sea and land mask information to calculate the root mean square error between the seabed topography product to be evaluated and the ship-measured water depth data to obtain the accuracy index of the seabed topography inversion product;

[0034] Step 3: Match the global authoritative terrain model to the same spatial range and resolution based on the latitude and longitude of the seabed topography product to be evaluated, ensuring that the assessment area completely overlaps;

[0035] Step 4: Using the authoritative terrain model as a reference, compare the structural similarity index of the inverted terrain and the reference terrain to obtain the structural similarity index of the seabed terrain inversion product;

[0036] Step 5: Unify the coordinate system of the fracture zone boundaries predicted by the seafloor topography inversion product and the geological structure label data, resample and crop them to achieve consistency in spatial reference, resolution and range;

[0037] Step 6: Calculate the F1-score of the fault zone boundary predicted by the seabed topography inversion product and the geological structure label to obtain the geological boundary consistency index of the seabed topography to be evaluated.

[0038] Compared with the prior art, the advantages of this application are:

[0039] 1. The submarine terrain quality evaluation system proposed in this application, based on multi-dimensional indicators, embeds a terrain structure similarity matching module and a geological boundary consistency comparison module to construct a quantitative and scalable submarine terrain quality evaluation system.

[0040] 2. The seabed terrain quality evaluation method based on multi-dimensional indicators proposed in this application introduces terrain detail characteristic indicators such as terrain structure similarity and geological boundary consistency in addition to traditional accuracy evaluation, which can obtain multi-dimensional, comprehensive and integrated seabed terrain quality evaluation information. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The figure shows the structure of the seabed topography quality evaluation system based on multi-dimensional indicators;

[0042] Figure 2 Shown is a flow chart of the seabed topography quality evaluation method based on multi-dimensional indicators. DETAILED DESCRIPTION

[0043] The technical solution of this application is described in detail below with reference to the accompanying drawings.

[0044] The seabed terrain quality evaluation system and method based on multi-dimensional indicators proposed in this application introduces terrain detail characteristic indicators such as terrain structure similarity and geological boundary consistency in addition to traditional accuracy evaluation, constructs a quantitative and scalable seabed terrain quality evaluation system, and can obtain multi-dimensional, comprehensive and integrated seabed terrain quality evaluation information.

[0045] Example 1

[0046] like Figure 1 As shown, this application proposes a seabed terrain quality evaluation system based on multi-dimensional indicators, including a ship-measured water depth accuracy verification module, a terrain structure similarity matching module, and a geological boundary consistency comparison module.

[0047] The design of each module is as follows:

[0048] 1. Ship-surveyed water depth accuracy verification module: used to refine the ship-surveyed water depth data, calculate the root mean square error between the seabed topography product to be evaluated and the ship-surveyed water depth data, and obtain the accuracy index of the seabed topography inversion product;

[0049] The processing of the ship depth accuracy verification module includes:

[0050] Step 1: Refine the ship-surveyed data. Based on the survey line time, longitude and latitude information, and effective water depth range, the ship-surveyed water depth data is corrected for long-wave errors and benchmarked to suppress noise, obtaining refined ship-surveyed water depth data.

[0051] Step 2: Calculate the accuracy index; Combine the sea and land mask information to calculate the root mean square error between the seabed topography product to be evaluated and the ship-measured water depth data to obtain the accuracy index of the seabed topography inversion product.

[0052]

[0053] Where, For the The ship-measured water depth values at each verification point; For the The seabed topography value of the product to be evaluated at each verification point; For the The land and sea mask value of each verification point, 0 for land and 1 for ocean; is the total number of verification points.

[0054] 2. Terrain structure similarity matching module: used to determine the comparison area based on the seabed terrain product to be evaluated matching the global authoritative terrain model, compare the structural similarity index of the seabed terrain product to be evaluated with the authoritative terrain model, and obtain the structural similarity index of the seabed terrain inversion product;

[0055] The processing of the terrain structure similarity matching module includes:

[0056] Step 1: Match the terrain comparison area: Match the global authoritative terrain model to the same spatial range and resolution based on the latitude and longitude of the seabed terrain product to be evaluated, ensuring that the assessment area completely overlaps;

[0057] Step 2: Compare the terrain structure similarity; using the authoritative terrain model as a reference, compare the structural similarity index of the inverted terrain and the reference terrain to obtain the structural similarity index of the seabed terrain inversion product.

[0058]

[0059] Where, Represents the inverted terrain and reference terrain The mean of Respectively and reference terrain the standard deviation and covariance of ; is the stability constant, usually set to 、 , is the dynamic range of terrain data, that is, the difference between the maximum and minimum values of the terrain in the evaluation area.

[0060] 3. Geological boundary fit comparison module: used to align the fault zone boundary predicted by the seabed topography inversion product with the geological structure label data space, calculate the F1-score of the predicted fault zone boundary and the geological structure label, and obtain the geological boundary fit index of the seabed topography to be evaluated.

[0061] The processing of the geological boundary consistency comparison module includes:

[0062] Step 1: Spatial alignment of geological boundaries: The fracture zone boundaries predicted by the seafloor topography inversion product and the geological structure label data are aligned in the same coordinate system, resampled, and cropped to achieve consistency in spatial reference, resolution, and extent.

[0063] Step 2: Boundary fit comparison: Calculate the F1-score of the fault zone boundary predicted by the seabed topography inversion product and the geological structure label to obtain the geological boundary fit index of the seabed topography to be evaluated.

[0064] Precision (P):

[0065]

[0066] Recall (R):

[0067]

[0068] F1-score:

[0069]

[0070] Where, is the number of pixels predicted to be the boundary and whose true label is the boundary; is the number of pixels predicted as boundary but the true label is non-boundary; The number of pixels whose true label is a boundary but predicted as a non-boundary pixel. The closer the F1-score is to 1, the more consistent the predicted boundary is with the USGS label, and the better the inversion accuracy.

[0071] Example 2

[0072] like Figure 2 As shown, this application also proposes a method for evaluating seabed topography quality based on multi-dimensional indicators, which is implemented based on the above system and includes:

[0073] Step 1) Refine the ship-surveyed data: Based on the survey line time, longitude and latitude information, and effective water depth range, perform long-wave error correction and benchmark unification on the ship-surveyed water depth data to suppress noise, thereby obtaining refined ship-surveyed water depth data.

[0074] Step 2) Calculate the accuracy index: Combine the sea and land mask information and calculate the root mean square error between the seabed topography product to be evaluated and the ship-measured water depth data to obtain the accuracy index of the seabed topography inversion product;

[0075] Step 3) Matching the terrain comparison area: Match the global authoritative terrain model to the same spatial extent and resolution based on the latitude and longitude of the seabed terrain product to be evaluated, ensuring that the assessment area completely overlaps;

[0076] Step 4) Comparing terrain structural similarity: Using the authoritative terrain model as a reference, the structural similarity index of the inverted terrain is compared with that of the reference terrain to obtain the structural similarity index of the seabed terrain inversion product;

[0077] Step 5) Spatial alignment of geological boundaries: The fracture zone boundaries predicted by the seafloor topography inversion product and the geological structure label data are aligned in the same coordinate system, resampled, and cropped to achieve consistency in spatial reference, resolution, and extent.

[0078] Step 6) Boundary fit comparison: Calculate the F1-score of the fault zone boundary predicted by the seafloor topography inversion product and the geological structure label to obtain the geological boundary fit index of the seafloor topography to be evaluated;

[0079] Step 7) Integrate multi-dimensional indicators to obtain a comprehensive evaluation of the seabed topography.

[0080] The present application may also provide a computer device comprising: at least one processor, memory, at least one network interface, and a user interface. The various components in the device are coupled together via a bus system. It will be understood that the bus system is used to enable communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus.

[0081] The user interface may include a display, a keyboard, or a pointing device, such as a mouse, a trackball, a touchpad, or a touch screen.

[0082] It is understood that the memory in the embodiments disclosed in the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memory.

[0083] In some embodiments, the memory stores the following elements, executable modules or data structures, or a subset or an extension thereof: an operating system and applications.

[0084] The operating system includes various system programs, such as the framework layer, core library layer, and driver layer, which are used to implement various basic services and handle hardware-based tasks. Application programs include various application programs, such as media players and browsers, which are used to implement various application services. The program that implements the method of the embodiment of the present disclosure can be included in the application program.

[0085] In the above embodiment, the processor may also call a program or instruction stored in the memory, specifically, a program or instruction stored in the application program, to:

[0086] Perform the steps of the above method.

[0087] The above method can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor or by software instructions. The above processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The above-disclosed methods, steps, and logic block diagrams can be implemented or executed. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the above-disclosed method can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0088] It is understood that the embodiments described herein may be implemented using hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit may be implemented in 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), general-purpose processors, controllers, microcontrollers, microprocessors, or other electronic units or combinations thereof for performing the functions described herein.

[0089] For software implementation, the technology of the present application can be implemented by executing the functional modules (e.g., procedures, functions, etc.) of the present application. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or external to the processor.

[0090] The present application may also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, each step in the above method embodiment can be implemented.

[0091] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of this application and are not intended to limit the scope of the present invention. Although this application has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions to the technical solutions of this application do not depart from the spirit and scope of the technical solutions of this application and should be encompassed by the claims of this application.

Claims

1. A seabed topography quality evaluation system based on multi-dimensional indicators, characterized by: include: The ship-surveyed water depth accuracy verification module is used to refine the ship-surveyed water depth data, calculate the root mean square error between the seabed topography product to be evaluated and the ship-surveyed water depth data, and obtain the accuracy index of the seabed topography inversion product; A terrain structure similarity matching module is used to determine the comparison area based on matching the seabed terrain product to be evaluated with the global authoritative terrain model, compare the structural similarity index of the seabed terrain product to be evaluated with the authoritative terrain model, and obtain the structural similarity index of the seabed terrain inversion product; and Geological boundary consistency comparison module: used to align the fault zone boundary predicted by the seabed topography inversion product with the geological structure label data space, calculate the F1-score of the predicted fault zone boundary and the geological structure label, and obtain the geological boundary consistency index of the seabed topography to be evaluated.

2. The seabed topography quality evaluation system based on multi-dimensional indicators according to claim 1 is characterized in that: The processing process of the ship water depth accuracy verification module includes: Step A1: Based on the survey line time, longitude and latitude information, and effective water depth range, the ship-surveyed water depth data is subjected to long-wave error correction and reference unification to suppress noise, thereby obtaining refined ship-surveyed water depth data. Step A2: Combine the sea and land mask information to calculate the root mean square error between the seabed topography product to be evaluated and the ship-measured water depth data to obtain the accuracy index of the seabed topography inversion product. : ； in, For the The ship-measured water depth values at each verification point; For the The seabed topography value of the product to be evaluated at each verification point; For the The land and sea mask values of the verification points; is the total number of verification points.

3. The seabed topography quality evaluation system based on multi-dimensional indicators according to claim 1 is characterized in that: The processing process of the terrain structure similarity matching module includes: Step B1: Match the global authoritative terrain model to the same spatial range and resolution based on the latitude and longitude of the seabed topography product to be evaluated, ensuring that the assessment area completely overlaps; Step B2: Using the authoritative terrain model as a reference, compare the structural similarity index of the inverted terrain and the reference terrain to obtain the structural similarity index of the seabed terrain inversion product. : ； in, Represents the inverted terrain and reference terrain The mean of Represents the inverted terrain and reference terrain The standard deviation of and the covariance between the two; is a stability constant.

4. The seabed topography quality evaluation system based on multi-dimensional indicators according to claim 3 is characterized in that: The value of the stability constant is: 、 ； in, It is the difference between the maximum and minimum values of the terrain in the assessment area.

5. The seabed topography quality evaluation system based on multi-dimensional indicators according to claim 1 is characterized in that: The processing process of the geological boundary consistency comparison module includes: Step C1: Unify the coordinate system of the fracture zone boundaries predicted by the seafloor topography inversion product and the geological structure label data, resample and crop them to achieve consistency in spatial reference, resolution and range; Step C2: Calculate the F1-score of the fault zone boundary predicted by the seabed topography inversion product and the geological structure label to obtain the geological boundary consistency index of the seabed topography to be evaluated: ； Where P represents the precision: ； R represents the recall rate: ； in, is the number of pixels predicted to be the boundary and whose true label is the boundary; is the number of pixels predicted as boundary but the true label is non-boundary; The number of pixels whose true label is boundary but predicted as non-boundary.

6. A method for evaluating seabed topography quality based on multi-dimensional indicators, implemented based on the system of any one of claims 1 to 5, comprising: Step 1: Based on the survey line time, longitude and latitude information, and effective range of water depth, the ship-surveyed water depth data is corrected for long-wave errors and benchmarked to suppress noise, obtaining refined ship-surveyed water depth data. Step 2: Combine the sea and land mask information to calculate the root mean square error between the seabed topography product to be evaluated and the ship-measured water depth data to obtain the accuracy index of the seabed topography inversion product; Step 3: Match the global authoritative terrain model to the same spatial range and resolution based on the latitude and longitude of the seabed topography product to be evaluated, ensuring that the assessment area completely overlaps; Step 4: Using the authoritative terrain model as a reference, compare the structural similarity index of the inverted terrain and the reference terrain to obtain the structural similarity index of the seabed terrain inversion product; Step 5: Unify the coordinate system of the fracture zone boundaries predicted by the seafloor topography inversion product and the geological structure label data, resample and crop them to achieve consistency in spatial reference, resolution and range; Step 6: Calculate the F1-score of the fault zone boundary predicted by the seabed topography inversion product and the geological structure label to obtain the geological boundary consistency index of the seabed topography to be evaluated.

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