Continental Slope Foot Point Recognition via Terrain Grid Derivation
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Solution Overview
Problem
Current methods lack a mature and automated technique for recognizing the foot point of the continental slope, which is crucial for accurate delimitation of the continental shelf beyond 200 nautical miles, affecting the precision of related boundaries and sediment thickness contours.
Innovation Solution
An automated method that generates a topography section line using a grid model, performs first and second simplifications, and second derivations to identify the foot point of the continental slope through integrated judgment based on slope, water depth, second derivative, concavity, and convexity features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis methods are used to identify the foot point of the continental slope, then flexibility and adaptability are maintained, but the recognition efficiency and consistency are insufficient
Solution Approach 1:
The system performs self-service by automatically conducting multiple derivations (first derivation, second derivation) and simplifications (first simplification, second simplification) on the topography data without human intervention. The algorithm independently identifies the foot point of the continental slope through integrated judgment of multiple features, eliminating the need for manual analysis while maintaining consistent and efficient recognition.
Solution Approach 2:
The patent replaces manual mechanical analysis with an automated computational system. The system uses mathematical operations (derivations, simplifications) and algorithmic judgment to substitute human expert analysis, achieving higher productivity and consistency in identifying the foot point of the continental slope.
2Productivity
If simplified analysis methods are used, then processing speed increases, but recognition accuracy decreases
Solution Approach 1:
The patent segments the complex recognition process into distinct stages: first derivation, second derivation, first simplification, and second simplification. Each stage processes specific features (slope, water depth, second derivative, concavity, convexity) separately, allowing the system to maintain high processing speed while ensuring recognition accuracy through systematic multi-feature analysis.
Solution Approach 2:
The patent transitions from two-dimensional topography data to multi-dimensional feature space by extracting five distinct features (slope, water depth, second derivative, concavity, convexity). This dimensional expansion enables comprehensive analysis that maintains accuracy while the automated computation preserves processing speed.
3Measurement precision
If multiple features are integrated for judgment, then recognition accuracy improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by conducting first and second derivations and simplifications before the final integrated judgment. These preliminary processing steps organize and prepare the five features (slope, water depth, second derivative, concavity, convexity) in advance, reducing the computational burden during the integration phase and managing overall complexity while maintaining high recognition accuracy.
4Reliability
If automated recognition is implemented, then consistency and reproducibility improve, but adaptability to varying coastal conditions may deteriorate
Solution Approach 1:
The patent achieves adaptability through parameter changes by analyzing five different features (slope, water depth, second derivative, concavity, convexity) that can capture varying coastal conditions. The integrated judgment system adjusts its analysis based on the specific characteristics present in different coastal environments, allowing the automated system to maintain both consistency in methodology and adaptability to diverse geological conditions.
Data Source
AI summary
An automatic recognition method of foot point of continental slope based on topography grid, comprising the steps of cutting a topography grid model through a straight line or a broken line to generate a two-dimensional topography section line, then carrying out first derivation on the two-dimensional topography section to generate a slope section line and a second derivative section line, then obtaining an extreme point of the second derivative section line, using a D-P algorithm to obtain a D-P topography section after second simplification, then carrying out second derivation on the D-P topography section and using a topography and slope judgment method to recognize and eliminate concave hull topography in the D-P section, and finally using judgment methods as slope, water depth, second derivation, concavity and convexity, continuity and segmentation based on the D-P topography, slope and second derivative section to form a recognition method.