基于多源地学数据的深部找矿预测应用探微

Exploring the application of deep mineral exploration prediction based on multi-source geological data

在寻求提高地下矿产资源勘查效率的过程中,本文通过综合物探、化探及遥感数据,并运用卷积神经网络(CNN)与生成式对抗网络(GAN)等深度学习技术,显著提升了找矿预测的准确性。这种方法不仅突破了传统找矿技术的限制,优化了数据处理流程,还提高了模型对复杂地学信息的解读能力。研究结果表明,融合多源数据与深度学习算法的模型在铜矿探寻中展示出显著优势,为地质勘查技术的进步和革新奠定了基础,开辟了新的研究和应用方向。

In the process of seeking to improve the efficiency of underground mineral exploration,this paper significantly improves the accuracy of mineral exploration prediction by integrating geophysical,geochemical,and remote sensing data,and using deep learning techniques such as convolutional neural networks(CNN)and generative adversarial networks(GAN).This method not only breaks through the limitations of traditional prospecting techniques,optimizes data processing processes,but also improves the model’s ability to interpret complex geological information.The research results indicate that the model integrating multi-source data and deep learning algorithms has shown significant advantages in copper exploration,laying the foundation for the progress and innovation of geological exploration technology,and opening up new research and application directions.