Remote estimation of geologic composition using interferometric
synthetic-aperture radar in California's Central Valley
- URL: http://arxiv.org/abs/2212.04813v1
- Date: Sun, 4 Dec 2022 23:06:14 GMT
- Title: Remote estimation of geologic composition using interferometric
synthetic-aperture radar in California's Central Valley
- Authors: Kyongsik Yun, Kyra Adams, John Reager, Zhen Liu, Caitlyn Chavez,
Michael Turmon, Thomas Lu
- Abstract summary: Land in California's Central Valley is sinking at a rapid rate due to groundwater pumping.
In this study, we aim to identify specific regions with different temporal dynamics of land displacement.
Then, we aim to remotely estimate geologic composition using interferometric synthetic aperture radar (InSAR)-based land deformation data.
- Score: 1.5677136474147644
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: California's Central Valley is the national agricultural center, producing
1/4 of the nation's food. However, land in the Central Valley is sinking at a
rapid rate (as much as 20 cm per year) due to continued groundwater pumping.
Land subsidence has a significant impact on infrastructure resilience and
groundwater sustainability. In this study, we aim to identify specific regions
with different temporal dynamics of land displacement and find relationships
with underlying geological composition. Then, we aim to remotely estimate
geologic composition using interferometric synthetic aperture radar
(InSAR)-based land deformation temporal changes using machine learning
techniques. We identified regions with different temporal characteristics of
land displacement in that some areas (e.g., Helm) with coarser grain geologic
compositions exhibited potentially reversible land deformation (elastic land
compaction). We found a significant correlation between InSAR-based land
deformation and geologic composition using random forest and deep neural
network regression models. We also achieved significant accuracy with 1/4
sparse sampling to reduce any spatial correlations among data, suggesting that
the model has the potential to be generalized to other regions for indirect
estimation of geologic composition. Our results indicate that geologic
composition can be estimated using InSAR-based land deformation data. In-situ
measurements of geologic composition can be expensive and time consuming and
may be impractical in some areas. The generalizability of the model sheds light
on high spatial resolution geologic composition estimation utilizing existing
measurements.
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