The three-dimensional (3D) geological models are the typical and key data source in the 3D mineral prospecitivity modeling. Identifying prospectivity-informative predictor variables from the 3D geological models is a challenging and tedious task. Motivated by the ability of convolutional neural networks (CNNs) to learn the intrinsic features, in this paper, we present a novel method that leverages CNNs to learn 3D mineral prospectivity from the 3D geological models. By exploiting the learning ability of CNNs, the presented method allows for disentangling complex correlation to the mineralization and thus opens a door to circumvent the tedious work for designing the predictor variables. Specifically, to explore the unstructured 3D geological models with the CNNs whose input should be structured, we develop a 2D CNN framework in which the geometry of geological boundary is compiled and reorganized into multi-channel images and fed into the CNN. This ensures an effective and efficient training of CNNs while allowing the prospective model to approximate the ore-forming process. The presented method is applied to a typical structure-controlled hydrothermal deposit, the Dayingezhuang gold deposit, eastern China, in which the presented method was compared with the prospectivity modeling methods using hand-designed predictor variables. The results demonstrate the presented method capacitates a performance boost of the 3D prospectivity modeling and empowers us to decrease work-load and prospecting risk in prediction of deep-seated orebodies.

翻译：三维(3D)地质模型是3D矿物勘探模型中典型和关键的数据源。 确定3D地质模型中前景- 信息预测变量是一项具有挑战性和枯燥的任务。 受进化神经网络(CNNs)学习内在特征的能力的驱动,我们在本文件中提出了一个新颖的方法,利用CNN从3D地质模型中学习3D矿物前景。 通过利用CNN的学习能力,所提出的方法可以使与矿化的复杂关系脱钩,从而打开绕过设计预测变量的乏味工作。 具体地说,探索非结构化的3D地质模型与CNN的输入结构,我们开发了2DCNN框架,其中将地质边界的地理测量汇编和重组成多声道图像,并输入CNN。这确保了CNN的高效和高效培训模式,同时允许潜在模型与矿化进程相匹配。 所介绍的方法适用于典型的结构- 结构- 预测值- 设计预测值- 预测值- 预估量- 数据- 展示中国的模型- 模型- 模型- 模型- 预测- 展示- 方向- 展示