一种基于多源地球物理数据融合的海砂自动识别方法
By constructing a dual-channel deep learning model that integrates drilling, single-channel seismic, and shallow seismic profile data, the problems of low efficiency and insufficient accuracy in marine sand identification were solved, enabling efficient and accurate automatic identification of marine sand resources.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INST OF GEOMECHANICS
- Filing Date
- 2026-01-26
- Publication Date
- 2026-07-17
AI Technical Summary
In existing technologies, sea sand identification methods are inefficient and highly subjective. Single seismic data are easily affected by noise, shallow seismic profile data have not been effectively fused, and the sparsity of drilling data limits the effectiveness of model training, making it difficult to meet the needs of accurate exploration of sea sand in large-scale sea areas.
By acquiring drilling data, single-channel seismic data, and shallow seismic profile data, a dual-channel deep learning model was constructed. Spatial registration and scale normalization were performed, and deep learning technology was combined to achieve efficient and accurate automatic identification of sea sand, thereby enhancing the model's ability to identify complex geological features.
It has significantly improved the accuracy and efficiency of marine sand resource identification, solved the technical problems of inconsistent spatial benchmarks and large scale differences in multi-source data in traditional methods, and improved the accuracy and efficiency of marine sand identification.
Smart Images
Figure CN122017970B_ABST