Remote estimation of geologic composition using interferometric synthetic-aperture radar in California's Central Valley

TL;DR

This study demonstrates that InSAR-based land deformation data, analyzed with machine learning models, can effectively estimate geologic composition in California's Central Valley, offering a cost-effective alternative to traditional methods.

Contribution

The paper introduces a novel approach combining InSAR data with machine learning to remotely estimate geologic composition, showing promising accuracy and generalizability.

Findings

Significant correlation between InSAR deformation and geology.

Effective estimation with sparse sampling reduces data needs.

Model generalizes well to different regions.

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.