TY  - JOUR
A1  - Mey, Jürgen
A1  - Scherler, Dirk
A1  - Zeilinger, Gerold
A1  - Strecker, Manfred
T1  - Estimating the fill thickness and bedrock topography in intermontane valleys using artificial neural networks
JF  - Journal of geophysical research : Earth surface
N2  - 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 Rhone 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 Rhone Valley, an overdeepened glacial valley in the Swiss Alps, yields a total estimated sediment volume of 9711km(3) 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.
KW  - sediment storage
KW  - sediment thickness
KW  - intermontane valleys
KW  - geomorphometry
KW  - artificial neural networks
Y1  - 2015
U6  - https://doi.org/10.1002/2014JF003270
SN  - 2169-9003
SN  - 2169-9011
VL  - 120
IS  - 7
SP  - 1301
EP  - 1320
PB  - American Geophysical Union
CY  - Washington
ER  -