Manifold regression for subsurface contaminant characterization Conference Paper uri icon

abstract

  • Characterization of sites contaminated by chemicals such as trichloroethylene, perchloroethylene, and other dense non-aqueous phase liquids (DNAPLs) is a necessary first step in the design and implementation of successful remediation strategies. In this paper, we develop a machine learning-based approach for estimating characteristics of a source zone related to the distribution of contaminant mass in highly saturated pool regions and more diffuse ganglia based on observations of down-gradient concentration images. After extracting a set of morphological features from training images, Laplacian Eigenmaps is employed to embed these features with the known source zone metric in a low dimensional manifold. A spectral regression scheme is used to embed the test data into the same manifold after which a Bayesian approach is employed to estimate the associated metric as well as a confidence interval. Results based upon simulated data demonstrate the potential effectiveness of the overall approach. 2012 IEEE.

name of conference

  • IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium

published proceedings

  • 2012 IEEE International Geoscience and Remote Sensing Symposium

author list (cited authors)

  • Zhang, H., Mendoza-Sanchez, I., Abriola, L., & Miller, E. L.

citation count

  • 4

complete list of authors

  • Zhang, Hao||Mendoza-Sanchez, Itza||Abriola, Linda||Miller, Eric L

publication date

  • July 2012

publisher