Earth Sciences Division Staff: Jinsong Chen
Jinsong Chen, Ph.D.
Jinsong Chen is a geological staff scientist at the Lawrence Berkeley National Laboratory. He received a B.Eng. (1988) in water-resources engineering, a B.S. (1988) in applied mathematics, and an M.Eng. (1990) in hydrology from Tsinghua University in China. He also received an M.S. (1997) in environmental fluid mechanics from Georgia Institute of Technology, an M.A. (2001) in statistics, and a Ph.D. (2001) in environmental engineering from the University of California at Berkeley. He worked in Tsinghua University from 1990 to 1995 and was a postdoctoral fellow at the University of California at Berkeley and at the Lawrence Berkeley National Laboratory from 2002 to 2003. Since 2004, he has worked as a geological scientist in the Earth Sciences Division.
His research interests are in development and applications of mathematical and statistical methods for solving complex problems in earth and environmental sciences.
Jinsong Chen's research focus is on the development and application of statistical and mathematical models for solving complex problems in earth and environment sciences. His interests are interdisciplinary and across surface hydrology, hydrogeology, geophysics, and statistics. His current research areas include:
Hydrogeophysics/biogeophysics is the use of geophysical methods for imaging subsurface properties and for monitoring important processes related to hydrogeological and biogeochemical studies. One main challenge in hydrogeophysics/biogeophysics is the lack of effective methods for integrating multi-scale and multi-source information, such as geological, hydrogeological, geophysical, geochemical, and biological data. read more »
Geophysical inverse problems are the inferences of physical properties from geophysical measurements. They are typically ill-posed because of non-uniqueness of the solutions and nonlinearity of the forward modeling. The main tasks of my research in the area: (1) To formulate inverse problems properly within a Bayesian framework, (2) To develop efficient (often problem-specific) Markov chain Monte Carlo (MCMC) sampling strategies... read more »
Environmental Applied Statistics. Modern statistics provides a variety of methods for solving complex problems in earth and environmental sciences, such as spatial statistics (geostatistics), time series analysis, Bayesian statistics, multivariate data analysis, linear and nonlinear regression, and multiple testing techniques. Fast growing of computing power makes it possible to implement many computationally intensive methods. read more »
- Ph.D. (Environmental Engineering) and M.A. (Statistics) University of California, Berkeley, CA, 2001
- M.S. (Environmental Fluid Mechanics) Georgia Institute of Technology, Atlanta, GA, 1997
- M.Eng. (Hydrology and Water Resources) Tsinghua University, Beijing, China, 1990
- B.Eng. (Water Resources Engineering) and B.S. (Applied Mathematics) Tsinghua University, Beijing, China, 1988
- Geological scientist, Lawrence Berkeley National Laboratory, Berkeley, CA, 04/2004—Present
- Research Statistician, University of California, Berkeley, CA, 10/2005—8/2006
- Research Engineer, University of California, Berkeley, CA, 2003—2005
- Postdoctoral Fellow, Lawrence Berkeley National Laboratory, Berkeley, CA, 2003
- Postdoctoral Fellow, University of California, Berkeley, CA, 2002
- Research Scientist and Lecturer, Tsinghua University, Beijing, China, 1990—1995
Free Software Downloads
- Stochastic inversion of spectral induced polarization data for Cole-Cole parameters (SISIP): The free C++ source codes serve two purposes:
- Provide a tool for inverting laboratory or field spectral IP data for Cole-Cole parameters by specifying prior ranges rather than initial values. The inversion results provide extensive global information on unknown parameters, such as posterior marginal probability distributions, from which we can obtain better estimates and more accurate uncertainty bounds of unknowns.
- Provide an example for people who want to learn Markov chain Monte Carlo (MCMC) based Bayesian methods for inverse problems. The detailed mathematical derivations and algorithms were published in Geophysics (Chen et al., 2008), and the C++ implementation on Linux or Unix can be downloaded for academic and non-profit research under the BSD license (check it). The structure of the codes can be applied to stochastic inversion of other types of data.
Download C++ source codes for academic and non-profit research.
- Contact theTechnology Transfer and Intellectual Property Management (TTIPM) of Berkeley Lab at TTD@lbl.gov regarding access for other purposes.