21. Development of Joint Inverse Methods for Improved Characterization and Assessment of Groundwater, Mineral, and Petroleum Resources

In geophysical imaging and groundwater or petroleum reservoir model calibration, inverse methods typically use a single type of data sensitive to a single physical property. Combining several types of data collected over the same region can reduce ambiguity and enhance inversion results. Combining different data types into an inversion can be important when relationships exist between the different property distributions. By inverting each data set individually, the recovered physical property models may be inconsistent with prior knowledge regarding relationships. Cooperative strategies may be employed to ensure consistency between the different models, but the models obtained are often biased towards the result of the first inversion or the survey with greater sensitivity. Another approach is to fit the data sets simultaneously in a joint inversion. Many investigators perform simultaneous inversions of data from different surveys sensitive to the same physical property. Others jointly invert data sets responsive to different physical properties between which there is an analytic relationship. However, little work has focused on the joint inversion of disparate data sets when there is no analytic relationship available between the properties. Research on the joint inversion of disparate data sets is likely to reveal new challenges regarding parameterization, potential inconsistencies between the data sets, relative weighting issues, and the introduction of an estimation bias due to increased systematic errors.

The focus of this Research Opportunity is developing joint inverse strategies for improved characterization and assessment of groundwater, mineral, and petroleum resources. This research opportunity will promote development through the integration of geologic, geophysical, and hydrogeologic information as measurement constraints during the simultaneous solution of multiple numerical models. Because the focus of this project is on the development of integrated joint inverse methods rather than a specific application, the scale and direction of research is to be determined by the postdoctoral fellow.

One example would be the joint inversion of gravity and magnetic responses over a draped surface to estimate the 3-dimensional geology using a Markov Monte Carlo approach. A second example would be the joint inversion of airborne and ground-based electromagnetic responses together with borehole geophysical and hydrogeologic mass and energy measurements to define a coastal fresh-salt water interface using a Levenberg-Marquardt approach. A third example would be the joint inversion of surface and borehole dc resistivity and spontaneous potential response together with subsurface variably saturated zone measurements to estimate ground-water recharge using a combination of genetic algorithm and Levenberg-Marquardt approaches. In these cases, special attention will be on the development and evaluation of an appropriate regularization strategy, as well as the estimation of uncertainty in model parameters and prediction of dependent variables. Regularization strategies could involve one or more analytical, petrophysical, and structural constraints applied as direct or soft prior information. Understanding the worth of combined information on reducing model and predictive uncertainty is an expected outcome of this research.

Proposed Duty Station: Denver, CO

Areas of Ph.D.: Geology, geophysics, hydrology, civil or geological engineering, mathematics, or physics

Qualifications: Applicants must meet one of the following qualifications: Research Geologist, Research Hydrologist, Research Geophysicist

(This type of research is performed by those who have backgrounds for the occupations stated above. However, other titles may be applicable depending on the applicant's background, education, and research proposal. The final classification of the position will be made by the Human Resources specialist.)

Research Advisor(s): Michael Friedel, (303) 236-7790, mfriedel@usgs.gov; David Fitterman (303) 236-1382, fitter@usgs.gov; Jeffrey Phillips, (303) 236-1206, jeff@usgs.gov; Seth Haines, (303) 236-5709, shaines@usgs.gov; Alyssa Dausman, (954) 377-5972, adausman@usgs.gov

Human Resources Office contact: Vanessa Chambless, (303) 236-9584, vchambless@usgs.gov

Summary of Opportunities