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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLII-4/W18
https://doi.org/10.5194/isprs-archives-XLII-4-W18-733-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLII-4-W18-733-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
18 Oct 2019
| 18 Oct 2019
INSAR TIME SERIES INVESTIGATION OF LAND SURFACE DEFORMATION IN AZAR OIL FIELD
Z. Mirzaii
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran
M. Hasanlou
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran
S. Samieie-Esfahany
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran
M. Rojhani
Department of Civil Engineering, Faculty of Engineering, Shahed University, Iran
P. Ajourlou
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran
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Short summary
Short summary
A relatively recent advance in glacial isostatic adjustment modelling has been the development of models that include 3D Earth structure, as opposed to 1D structure. However, a major limitation is the computational expense. We have developed a method using artificial neural networks to emulate the influence of 3D Earth models to affordably constrain the viscosity parameter space. Our results indicate that the misfits are of a scale such that useful predictions of relative sea level can be made.
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