Critical Zone research
The Earth’s critical zone (CZ) extends from the canopy of the trees down to the bottom of active groundwater circulation. CZ is the Earth's skin which supports the chemical, biological, physical and geological processes together to support life at the Earth's surface (Brantley et al., 2007).
My current research focuses on CZ's physical properties and I am also working on advanced CZ geophysical imaging technology.
Chen, H. and Niu, Q., 2022. Improving moisture content estimation from field resistivity measurements with subsurface structure information. Journal of Hydrology, p.128343.
Chen, H. and Niu, Q., 2021, December. Influence of subsurface heterogeneity on critical zone characterizations with electrical resistivity and seismic refraction tomography. In Sixth International Conference on Engineering Geophysics, Virtual, 25–28 October 2021 (pp. 213-216). Society of Exploration Geophysicists.
Chen, H. Exploring subsurface hydrology with electrical resistivity tomography. Nat Rev Earth Environ (2022). https://doi.org/10.1038/s43017-022-00350-4
The physical properties of the CZ soil and rock: Burnham research funds ($1500), "Laboratory investigation into the geophysical properties of the critical zone soil"
Figure credits Brantley et al., 2007.
Nuclear disposal monitoring
Salt possesses key attributes, such as tightness against fluid flow, high thermal conductivity, and the ability to seal fractures, making it an optimal medium for the permanent isolation of heat-generating radioactive waste. Understanding the thermal-hydrological-mechanical (THM) processes in salt formations is crucial for safe radioactive waste disposal. Electrical resistivity, being sensitive to permeability, brine content, and rock temperature, presents a promising method to provide spatial-temporal information on THM processes.
Chen, H., Chou, C., Borglin, S.E., Chang, C., Kneafsey, T.J., Nakagawa, S., Zheng, L., Birkholzer, J. and Wu, Y., 2022, December. The quantitative studies of time-lapse electrical resistivity data for characterizing coupled thermal-hydro-mechanical processes in bentonite. In AGU Fall Meeting Abstracts (Vol. 2022, pp. NS53A-04)
Near-surface geophysical research
See fieldwork
Rock physics
To be updated
Paper:
Chen, H. and Niu, Q., Influence of the Interfacial Polarization on Electrical Properties of Granular Materials. In AGU Fall Meeting 2020. AGU.
Grant:
GSA Graduate Student Research Grant ($1000), "Textural controls of the electrical properties of soils in the critical zone "
Magnetotelluric
To be updated
Paper:
Chen, H., Guo, R, Liu, J. (2018). Statistic properties for impedance estimation of MT data with noise distributions using the robust method. The 8th international conference on environmental and engineering geophysics.
Grants:
Chinese national College Students' Innovation and Entrepreneurship Project (￥10000), "Magnetotelluric Signal and Noise Identification"
The Undergraduate Training Programs for Innovation Entrepreneurship(￥1000), "Robust Estimation of Magnetotelluric Signals Based on Machine Learning"
Free codes: Work with Rongwen Guo: The code is used to calculate the apparent resistivity and phase from electromagnetic components, and related data errors using different methods, such as least square, robust, remote reference and robust remote reference method, it includes two Matlab classes: one for time series process, one for impedance estimate.
Machine learning
To be updated
Related work:
Chen, H., Guo, R., Liu, J., Wang, Y. and Lin, R., 2020, January. Magnetotelluric data denoising with recurrent neural network. In SEG 2019 Workshop: Mathematical Geophysics, Beijing, China, 5-7 November 2019 (pp. 116-118). Society of Exploration Geophysicists.
J. Xie, Y. Cui, R. Guo, H. Chen*, Y. Guo, Y. Luo, and J. Liu., 2022 A Deep learning algorithm for locating contaminant plumes from self-potential data: a laboratory perspective. IEEE Transactions on Geoscience and Remote Sensing
Geophysical forward modeling
Geophysical forward modeling is mainly related to the partial differential equation (pde) solution. However, in geophysical forward modeling, the governing equation is always complex and sometimes we need to do some special operator (e.g., Hankel integrals). Besides, with 3D geophysical inversion become popular nowadays, it is also important to consider how to improve the efficiency of the forward modeling. My forward modeling research mainly focuses on these two points.
1.3D large-scale electromagnetic forward modeling. The 3D electromagnetic methods (e.g., MT and CSEM) have been widely applied in tectonic studies, geohazard investigation, geothermal exploration, and mineral exploration. To improve the inversion process of electromagnetic methods, it is indeed to improve the efficiency and precision of 3D electromagnetic forward modeling. Our work is using some advanced solvers and space transformation tricks to improve 3D electromagnetic forward modeling.
Paper:
Guo, R., Wang, Y., Egbert, G.D., Liu, J., Liu, R., Pan, K., Li, J. and Chen, H., 2022. An efficient multigrid solver based on a 4-color cell-block Gauss-Seidel smoother for 3D Magnetotelluric forward modeling. Geophysics, 87(3), pp.1-56.
Wang, Y., Guo, R., Liu, J., Chen, H., Li, J. and Liu, R., 2021 Comparison the iterative solvers for large sparse matrix in 3D electromagnetic forward modeling. In IOP Conference Series: Earth and Environmental Science (p. 012066)
Li, J., Liu, J., Guo, R., Liu, R., Wang, X., Wang, Y. and Chen, H., 2021 A Comparison of Different Divergence-free Solutions for 3D Anisotropic CSEM Modeling Using Staggered Finite Difference Method. In IOP Conference Series: Earth and Environmental Science(p. 012133)
2. Hankel integrals. The evaluation of Hankel integration is an important part in the interpretation of electromagnetic (EM) data, especially in physical and geophysical applications. we apply the particle swarm algorithm to search for an optimal solution (spacing and shift parameters) to ensure the precision of Hankel integrals.
Paper:
Zeng, L., Li, J., Liu, J., Guo, R., Chen, H. and Liu, R., 2021. Efficient Filter Generation Based on Particle Swarm Optimization Algorithm. IEEE Access, 9, pp.22816-22823.
A 3D synthetic electrical model and its forward modeling response.
Relative errors of different digital filters of J0 for the calculation of analytical function pair.