Gossans are surficial deposits that form in host bedrock by the alteration of sulphides by acidic and oxidizing fluids. These deposits are typically a few meters to kilometers in size and they constitute important vectors to buried ore deposits. Hundreds of gossans have been mapped by field geologists in sparsely vegetated areas of the Canadian Arctic. However, due to Canada’s vast northern landmass, it is highly probable that many existing occurrences have been missed. In contrast, a variety of remote sensing data has been acquired in recent years, allowing for a broader survey of gossans from orbit. These include band ratioing or methods based on principal component analysis. Spectrally, the 809 gossans used in this study show no significant difference from randomly placed points on the Landsat 8 imageries. To overcome this major issue, we propose a deep learning method based on convolutional neural networks and relying on geo big data (Landsat-8, Arctic digital elevation model lithological maps) that can be used for the detection of gossans. Its application in different regions in the Canadian Arctic shows great promise, with precisions reaching 77%. This first order approach could provide a useful precursor tool to identify gossans prior to more detailed surveys using hyperspectral imaging.

中文翻译：

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在加拿大北极地区检测Gossan的深度学习方法

戈桑岩是通过酸性和氧化性流体改变硫化物而在宿主基岩中形成的表面沉积物。这些矿床的大小通常只有几米到几千米，它们是埋藏矿床的重要载体。野外地质学家在加拿大北极的植被稀疏地区绘制了数百个戈桑。但是，由于加拿大北部广阔的陆地，很可能错过了许多现有的事件。相比之下，近年来已经获得了各种遥感数据，从而可以对来自轨道的流星进行更广泛的调查。这些方法包括频段分配或基于主成分分析的方法。光谱上，本研究中使用的809戈桑与Landsat 8影像上随机放置的点没有显着差异。为了克服这个重大问题，我们提出了一种基于卷积神经网络并依赖于地理大数据（Landsat-8，北极数字高程模型岩性图）的深度学习方法，该方法可用于检测戈桑。它在加拿大北极地区不同地区的应用前景广阔，精度达到77％。这种一阶方法可以提供有用的先驱工具，以在使用高光谱成像进行更详细的调查之前识别戈桑。