MANTIS Ph.D. Student Xiaojun Qiao successfully defended his dissertation titled Assessment of Land Subsidence Along the Texas Gulf Coast. After several publications and presentations, Xiaojun now joins a prestigious list to have successfully gone through the Geospatial Computer Science doctoral program at Texas A&M University-Corpus Christi.
His presentation abstract read as follows:
The Texas Gulf Coast has been recognized as one of the subsidence hotspots in the United States, thereby presenting risks such as shoreline erosion and coastal flooding. The accurate estimation of subsidence and the identification of its underlying causes hold significant value for comprehending subsidence processes and guiding decision-making. To achieve this, the research integrated space-borne and terrestrial geodetic techniques, utilized multi-source observations, and applied machine learning (ML) methods for the estimation, modeling, and attribution of subsidence along the Texas Gulf Coast. First, two sea-level difference methods were designed to reconstruct displacement time series at tide gauge (TG) locations in Texas with observation periods exceeding ten years. In addition, synthetic aperture radar (SAR) imagery, continuously operating global navigation satellite system (cGNSS) observations, and sea-level measurements were harnessed to estimate the spatiotemporal patterns of subsidence spanning around three decades since the 1990s at the Eagle Point TG station, a prominent hotspot of sea-level rise in the United States. Moreover, the interferometric SAR (InSAR) was leveraged to generate a large-scale subsidence map along the Texas coastlines post-2016. Attribution analysis indicated that hydrocarbon extraction and groundwater withdrawal were the predominant factors responsible for identified subsidence hotspots in the Texas Gulf Coast. ML demonstrated an impressive performance (with an R2 of 0.56) in modeling the observed large-scale subsidence, by incorporating a range of features related to natural terrain variations and anthropogenic activities. Explainable artificial intelligence (XAI) methods provided quantitative estimates of feature contributions of the ML model, and the data-driven results revealed that the digital elevation model (DEM) and anthropogenic factors were contributing features in relation to subsidence.
