Processing-analysis And Quantitative Assessment Methods On The Data Related To Deep-sea Solid Mineral Resources

As strategic mineral resources, deep‐sea solid mineral resources, such as polymetallic nodules, cobalt‐rich crusts, and hydrothermal sulfide, has drawn more attention and many countries have shifted their strategic vision to the vast and resource‐rich ocean, so deep‐sea mineral resources surveying and assessment become more and more important. However, due to the differences between deep‐sea and land mineral resources, such as the low level of seabed surveying, complex ore‐controlling factors and high uncertainty, lack of background data various metallogenic model of data and so on, quantitative estimation methods, playing an important role in land mineral assessment, has not been systematically applied in the estimation of deep‐sea mineral resources.At present, the deep‐sea mineral resources surveying and estimation is based on the existing knowledge or expert judgments to select the survey area, basically not using quantitative assessment technology to delineate target areas. With the development of deep‐sea mineral resources surveying techniques, it is time to introduce the theory and methods of land mineral resources quantitative evaluation to deep sea. This research work intends to focus on this point trying to introduce the theory and methods of land mineral resources quantitative evaluation to deep sea, and gives a more systemic quantitative assessment method, combining with the characteristic of deep‐sea mineral resources assessment. The systemic deep‐sea mineral resources quantitative assessment methods are proposed based on the thoughts of land solid mineral resources quantitative assessment, and are applied to manganese nodule resources in CC zone. Specific ideas are as follows:First, collect, classify and summarize the mineral data and knowledge in deep‐sea target mining area (or typical mining area), then collected, classify the background data and knowledge on typical mining area and its surrounding areas. The knowledge of metallogenic regularity can be obtained by collecting, classifying and summarizing the results of previous studies on typical mining area, to provide a priori knowledge for establishing the estimated model. The collection and classification of the information on the typical mining area and its surrounding areas can provide evidence layers for generalization of estimated model on typical mining areaSecond, preprocess data. Data processing methods include removing abnormal data, data gridding, data transformation and estimating unit selection and so on. Through the preprocessing, the impact of abnormal data is excluded, the inconsistencies of data units and magnitude is overcome, while irregular data is transformed into a uniform the data matrix, meeting the needs of model calculation. Direct impact on the level of data processing and analysis impacts directly the effect of modeling.Third, feature extraction after data preprocessing. There are many ways for feature extraction, and here only the information extraction methods for deep‐sea mineral resources data are introduced,. These include methods for extracting spatial field features, statistical features and the qualitative fractures. Information extraction technology can provide more evidence layers and independent variable information.Finally, introduce two new assessment models for experiment area. They are "weight of evidence regression model" and "Fuzzy ARTMAP model." Weight of evidence regression model realize simultaneously positioning and quantitative estimation. Modified Fuzzy ARTMAP element model, which can estimate the metallic grade of many kinds of metal. The former is a combination of two existing algorithms, and the latter is used in the work for the first time. The two models get good results in the practical application, which proves to be suitable for quantitative assessment of deep‐sea mineral resources.