(1. 浙江大學(xué) 海洋學(xué)院，舟山 316021;
2. 自然資源部第二海洋研究所 自然資源部海底科學(xué)重點(diǎn)實(shí)驗(yàn)室，杭州 310012;
3. 上海交通大學(xué) 海洋學(xué)院，上海 200240)
摘 要: 海底多金屬硫化物是未來可供開發(fā)利用的重要礦產(chǎn)資源。由于海底環(huán)境復(fù)雜，勘探成本巨大，利用成礦理論開展資源預(yù)測工作就顯得尤為重要。本文綜述了現(xiàn)有的海底多金屬硫化物成礦遠(yuǎn)景區(qū)預(yù)測方法，分析比較了各預(yù)測方法的特點(diǎn)，借鑒陸地火山成因塊狀硫化物的資源預(yù)測方法，并結(jié)合卡爾斯伯格脊的應(yīng)用實(shí)例，對(duì)多金屬硫化物資源預(yù)測工作進(jìn)行了探討：多金屬硫化物成礦預(yù)測方法需綜合考慮研究區(qū)的勘探程度、數(shù)據(jù)資料的精度、覆蓋范圍等實(shí)際情況，并結(jié)合各方法的特點(diǎn)及其適用性進(jìn)行合理選取；應(yīng)用知識(shí)驅(qū)動(dòng)與數(shù)據(jù)驅(qū)動(dòng)的組合預(yù)測方法和深度學(xué)習(xí)算法解決已知硫化物礦床(點(diǎn))不足、小樣本、數(shù)據(jù)缺失、數(shù)據(jù)耦合、主客觀誤差等問題，提高預(yù)測的準(zhǔn)確性和效率；通過綜合比較基于不同原理的預(yù)測方法獲得的結(jié)果進(jìn)行驗(yàn)證，提高資源預(yù)測的可靠性。
關(guān)鍵字: 多金屬硫化物；資源預(yù)測；預(yù)測方法；成礦遠(yuǎn)景區(qū)
（1. Ocean College, Zhejiang University, Zhoushan 316021, China;
2. Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China;
3. School of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, China）
Abstract:The seafloor polymetallic sulfides are important mineral resources available for human development and utilization in the future. Due to the complex submarine environment and tremendous exploration costs, it is significant to use metallogenic theory for resource prediction. In this study, the exciting methods for predicting prospective area of polymetallic sulfide were reviewed. Through analyzing the characteristics of each method and prediction methods for volcanic-hosted massive sulfide resources, the prediction of polymetallic sulfide resources with application of Carlsberg Ridge was discussed. First, the prediction methods of polymetallic sulfide resources should be selected reasonably, based on the degree of exploration, the accuracy and coverage of data, as well as the characteristics and applicability of methods. Second, the combined prediction methods of knowledge-driven and data-driven, and deep learning algorithm can be considered for polymetallic sulfide resource prediction to improve the accuracy and efficiency by solving the problems of insufficient known deposits, small samples, data missing, data coupling, subjective and objective errors. Third, the reliability of polymetallic sulfide resource prediction can be evaluated by comparing the results of methods with different principles for predicting metallogenic prospective area.
Key words: polymetallic sulfide; resource prediction; prediction method; metallogenic prospective area
