The decline of the number of newly discovered mineral deposits and increase in demand for different minerals in recent years has led exploration geologists to look for more efficient and innovative methods for processing different data types at each stage of mineral exploration. As a primary step, various features, such as lithological units, alteration types, structures, and indicator minerals, are mapped to aid decision-making in targeting ore deposits. Different types of remote sensing datasets, such as satellite and airborne data, make it possible to overcome common problems associated with mapping geological features. The rapid increase in the volume of remote sensing data obtained from different platforms has encouraged scientists to develop advanced, innovative, and robust data processing methodologies. Machine learning methods can help process a wide range of remote sensing datasets and determine the relationship between components such as the reflectance continuum and features of interest. These methods are robust in processing spectral and ground truth measurements against noise and uncertainties. In recent years, many studies have been carried out by supplementing geological surveys with remote sensing datasets, which is now prominent in geoscience research. This paper provides a comprehensive review of the implementation and adaptation of some popular and recently established machine learning methods for processing different types of remote sensing data and investigates their applications for detecting various ore deposit types. We demonstrate the high capability of combining remote sensing data and machine learning methods for mapping different geological features that are critical for providing potential maps. Moreover, we find there is scope for advanced methods such as deep learning to process the new generation of remote sensing data that provide high spatial and spectral resolution for creating improved mineral prospectivity maps.

近年来，新发现矿床数量的减少和对不同矿产需求的增加，促使勘探地质学家在矿产勘探的每个阶段寻找更有效和创新的方法来处理不同类型的数据。作为首要步骤，绘制各种特征，例如岩性单元、蚀变类型、构造和指示矿物，以帮助针对矿床的决策制定。不同类型的遥感数据集，例如卫星和机载数据，可以解决与绘制地质特征相关的常见问题。从不同平台获得的遥感数据量的迅速增加鼓励科学家开发先进、创新和稳健的数据处理方法。机器学习方法可以帮助处理范围广泛的遥感数据集，并确定组件之间的关系，例如反射连续谱和感兴趣的特征。这些方法在处理针对噪声和不确定性的频谱和地面实况测量方面具有鲁棒性。近年来，通过以遥感数据集补充地质调查的方式开展了许多研究，这在地球科学研究中占有重要地位。本文全面回顾了一些流行的和最近建立的用于处理不同类型遥感数据的机器学习方法的实施和适应，并研究了它们在检测各种矿床类型方面的应用。我们展示了结合遥感数据和机器学习方法来绘制不同地质特征的强大能力，这些地质特征对于提供潜在地图至关重要。此外，我们发现深度学习等先进方法可以处理新一代遥感数据，这些数据为创建改进的矿物远景图提供高空间和光谱分辨率。