河南内黄—浚县一带重磁异常与深部磁铁矿靶区预测研究

Geophysical and Geochemical Exploration（2019）

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Abstract

河南内黄—浚县一带位于太古宙基底隆起区,且分布有较好的重、磁异常,具备寻找沉积变质型磁铁矿床的有利条件.然而20世纪70年代以来,受制于浅部找矿认识的局限性,一直未能取得找矿突破.笔者以深部磁铁矿找矿靶区为目标,通过中心识别技术(解析信号振幅ASM)、边缘识别技术(NVDR-THDR)获得了研究区隐伏磁性体的平面位置(中心及边界),利用欧拉反褶积获得了研究区隐伏磁性体的埋深信息,利用相关系数分析了该区重磁异常的同源性特征;结合已知钻孔对研究区重点磁异常进行了2.5D拟合反演,确定了隐伏磁性体的规模及空间展布特征,研究结果表明主要磁异常对应的隐伏磁性体埋深在500～1200 m,且深部含矿性均好于浅部,深部找矿潜力较大.综合地质矿产条件,预测了瓦岗—榆涧、南张保等2个磁铁矿深部找矿靶区,建议作为后续开展深部找矿的重点方向,以尽快实现该区的深部找矿突破.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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