Dr. Zhimin Cao Profile

Dr. Zhimin Cao

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Proceedings Article | 23 January 2024 Paper

Logging curve restoration based on correlation information mining

Lechuan Hao, Zhimin Cao, Jian Han, Jing Li, Xiaowei Zhang

Proceedings Volume 12978, 129782Y (2024) https://doi.org/10.1117/12.3019517

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In the application of reservoir geological description using well logging data, some well logging curves are often distorted or missing. For this reason, well logging curve restoration has always been a hotspot and difficulty in related research fields. The current methods, which use the traditional signal restoration method and the restoration method based on machine learning such as neural network, do not sufficiently express and utilize the correlation information between different logging curves in the same well, and with poor cross-well adaptability. For these problems, a logging curve restoration method based on graph representation for spatio-temporal correlation information mining is proposed. The proposed method firstly describes the graph structure between different logging curves based on the commonly used graph representation learning in the field of signal processing, so that the logging curves can establish a nonlinear mapping relationship. And then, the hierarchical structure of the deep forest is introduced to realize the representation of the longitudinal information of the logging curve. Through the experimental verification of the proposed method, the prediction results of multiple well logging curves from different wellheads are obtained.

Proceedings Article | 7 September 2018 Paper

A building edge extraction method based on dual-scale classification with decision fusion for satellite image

Lechuan Hao, Ye Zhang, Zhimin Cao, Qingle Guo

Proceedings Volume 10764, 107641Z (2018) https://doi.org/10.1117/12.2320398

KEYWORDS: Buildings, Feature extraction, Image classification, Satellite imaging, Satellites, Edge detection, Feature selection

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Building edge or boundary extraction is always one of the most important issues for earth observation, city planning, and other applications. However, for accurately extracting building edge, there are commonly two difficult challenges. Firstly, unwanted strong edges from road and other things can be hardly avoided to be recognized. Secondly, it is more serious that many low or very low contrast weak edges will be not detected. In order to deal with these two issues to a certain extent, in this paper, based on sparse SVM with dual-scale features, we propose a Building Edge Extraction method in a Dual-scale Classification way with Decision Fusion embedded (DC-BEE). Specifically, with global linearity information as priori knowledge, training samples are selected automatically at first. Next, a sparse SVM classifier is trained using the dual-scale local edge features of the training samples. And then, the trained sparse SVM is employed to classify all extracted edges. Finally, the dual-scale decision fusion strategy is performed for final building edge extraction. Visual analysis and quantitative analysis of the experimental results from different style city regions illustrated that the proposed DC-BEE method can efficiently fulfill the building edge extraction task automatically.

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