Estimating the fill thickness and bedrock topography in intermontane valleys using artificial neural networks
Estimating the fill thickness and bedrock topography in intermontane valleys using artificial neural networks
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DOI：
10.1002/2014jf003270
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发表时间：
2015-07
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影响因子：
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通讯作者：
J. Mey;D. Scherler;G. Zeilinger;M. Strecker
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文献类型：
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作者：
J. Mey;D. Scherler;G. Zeilinger;M. Strecker
在以前的冰川山脉中，山间山谷中厚的沉积填充物很常见，但难以量化。然而，对填充物厚度分布的了解可以帮助估计山带的泥沙收支，并解释储存的物质在调节从造山带到前陆的泥沙通量中的作用。本文提出了一种基于景观几何特征的人工神经网络估算山谷填充物厚度和基岩地形的新方法。我们按照四层程序测试了这种方法的潜力。首先，对合成的、理想化的景观进行的实验表明，地表坡度变异性的增加需要越来越复杂的网络配置。其次，在人工填充自然景观的实验中，我们发现填充体积的估计误差在20%以下。第三，在具有陡峭斜坡的山谷填充面的自然例子中，例如瑞士阿尔卑斯山脉的Unteraar冰川和Rhône冰川，测量的和模拟的山谷填充的横截面积的平均偏差分别为26%和27%。最后，将该方法应用于瑞士阿尔卑斯山脉的一个过深冰川谷Rhône谷，得到的沉积物总量估计为97±11 km3，测量值与模型估计值之间的平均横截面积偏差为21.5%。我们的新方法可以快速评估山间山谷的沉积物体积，同时消除了基于数字高程模型进行基岩重建的其他方法中通常固有的大部分主观性。
Thick sedimentary fills in intermontane valleys are common in formerly glaciated mountain ranges but difficult to quantify. Yet knowledge of the fill thickness distribution could help to estimate sediment budgets of mountain belts and to decipher the role of stored material in modulating sediment flux from the orogen to the foreland. Here we present a new approach to estimate valley fill thickness and bedrock topography based on the geometric properties of a landscape using artificial neural networks. We test the potential of this approach following a four‐tiered procedure. First, experiments with synthetic, idealized landscapes show that increasing variability in surface slopes requires successively more complex network configurations. Second, in experiments with artificially filled natural landscapes, we find that fill volumes can be estimated with an error below 20%. Third, in natural examples with valley fill surfaces that have steeply inclined slopes, such as the Unteraar and the Rhône Glaciers in the Swiss Alps, for example, the average deviation of cross‐sectional area between the measured and the modeled valley fill is 26% and 27%, respectively. Finally, application of the method to the Rhône Valley, an overdeepened glacial valley in the Swiss Alps, yields a total estimated sediment volume of 97 ± 11 km3 and an average deviation of cross‐sectional area between measurements and model estimates of 21.5%. Our new method allows for rapid assessment of sediment volumes in intermontane valleys while eliminating most of the subjectivity that is typically inherent in other methods where bedrock reconstructions are based on digital elevation models.