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 to process the new generation of remote sensing data for creating improved mineral prospectivity maps.
翻译:近年来,新发现的矿物矿藏数量减少,对不同矿物的需求增加,导致勘探地质学家在矿物勘探的每个阶段寻找更高效和创新的方法,处理不同种类的数据,作为第一步,绘制各种特征,如岩浆单位、改变类型、结构和指标矿物等,以协助在确定矿床时的决策; 不同类型的遥感数据集,如卫星和空中数据,能够克服与测绘地质特征有关的共同问题; 从不同平台获得的遥感数据迅速增加,鼓励科学家开发先进、创新和稳健的数据处理方法; 机器学习方法有助于处理各种遥感数据集,确定反映连续性和兴趣特征等组成部分之间的关系; 这些方法在处理光谱和地面对噪音和不确定性的测量方面十分健全; 近年来,许多研究是通过利用遥感先进数据集补充地质调查而完成的,而遥感数据集现已在地质科学研究中占有突出地位。 本文全面审查了某些流行和最近建立的遥感数据处理方法的实施和调整情况; 机床学习方法有助于处理各种遥感数据的连续和特征; 我们为遥感数据的不同类型提供各种遥感数据的遥感方法,为遥感数据的潜在学习范围提供各种遥感方法。