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首页> 外文期刊>The Cryosphere >DeepBedMap: a deep neural network for resolving the bed topography of Antarctica
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DeepBedMap: a deep neural network for resolving the bed topography of Antarctica

机译：DeepbedMap：一个深层神经网络，用于解决南极床地形

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To resolve the bed elevation of Antarctica, we present DeepBedMap?– a novel machine learning method that can produce Antarctic bed topography with adequate surface roughness from multiple remote sensing data inputs. The super-resolution deep convolutional neural network model is trained on scattered regions in Antarctica where high-resolution (250 m) ground-truth bed elevation grids are available. This model is then used to generate high-resolution bed topography in less surveyed areas. DeepBedMap improves on previous interpolation methods by not restricting itself to a low-spatial-resolution (1000 m) BEDMAP2 raster image as its prior image. It takes in additional high-spatial-resolution datasets, such as ice surface elevation, velocity and snow accumulation, to better inform the bed topography even in the absence of ice thickness data from direct ice-penetrating-radar surveys. The DeepBedMap model is based on an adapted architecture of the Enhanced Super-Resolution Generative Adversarial Network, chosen to minimize per-pixel elevation errors while producing realistic topography. The final product is a four-times-upsampled (250 m) bed elevation model of Antarctica that can be used by glaciologists interested in the subglacial terrain and by ice sheet modellers wanting to run catchment- or continent-scale ice sheet model simulations. We show that DeepBedMap offers a rougher topographic profile compared to the standard bicubically interpolated BEDMAP2 and BedMachine Antarctica and envision it being used where a high-resolution bed elevation model is required.
机译：为了解决南极洲的床上高程，我们展示了DeepbedMap？ - 一种新型机器学习方法，可以产生来自多个遥感数据输入的足够表面粗糙度的南极地形。超分辨率的深卷积神经网络模型在南极洲的散射区域培训，其中高分辨率（250米）的地面真相床高度​​网格。然后使用该模型在较少的受访区域中产生高分辨率床地形。 DeepbedMap通过不限于其作为其先前图像的低空间分辨率（1000米）床罩2光栅图像来提高先前的插值方法。它需要额外的高空间分辨率数据集，例如冰面高度，速度和积雪，即使在没有直接冰透过雷达调查的情况下也能更好地通知床地形。 DeepBedMap模型基于增强的超分辨率生成对冲网络的适应性架构，选择以最小化每个像素高度误差在产生现实地形的同时。最终产品是一种四次上采样（250米）的南极洲床升高模型，可以由对底层地形和冰板制动家感兴趣的冰川学家使用，希望运行集水区或大陆冰块模型模拟。我们表明，与标准的双向插值的床罩2和床单南极相比，DeepbedMap提供了一种令人讨厌的地形概况，并在需要高分辨率床升高模型的情况下使用它的设想。

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