A method for exploring heavy mineral sand resources in shallow sea and related equipment
By collecting passive source micromotion data in shallow sea heavy mineral placer exploration, constructing spectral ratio curves and inverting bedrock surface depth, and combining geological model analysis, the problem of difficulty in balancing exploration depth and resolution in existing technologies has been solved, achieving high-precision resource potential assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGZHOU MARINE GEOLOGICAL SURVEY
- Filing Date
- 2026-02-02
- Publication Date
- 2026-06-05
AI Technical Summary
Existing technologies face challenges in shallow sea heavy mineral placer exploration, including interference with data quality due to complex sea conditions, difficulty in balancing detection depth and resolution, insufficient penetration capability of shallow stratigraphic profiles, limited ability of single-channel seismic analysis to identify thin or deep sand bodies, severe ambiguity in sand-mud interface identification, and difficulty in characterizing three-dimensional morphology under sparse boreholes, resulting in low accuracy in resource assessment.
By acquiring preliminary research data, passive source micromotion data were collected using a submarine seismograph, and horizontal and vertical spectral ratio curves were constructed for spectral separation to obtain the low-frequency curve of the bedrock resonance peak. A paleotopographic map was constructed by combining the bedrock surface depth inversion and then overlaid with a regional geological model to assess the potential of heavy mineral resources.
It effectively overcomes the shortcomings of existing technologies, utilizes natural field sources to adapt to complex coastal and shallow marine environments, penetrates deep interfaces, reveals paleogeomorphic units, and improves the targeting of exploration and the accuracy of resource potential assessment.
Smart Images

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