Advanced methods for depth-to-basement estimation using gravity, magnetic, and electromagnetic data

There is a strong interest in developing effective methods to estimate the depth-to-basement. Potential field methods have already been widely used in this application by parameterizing the earth's subsurface into 3D cells. I introduce a new method of solving this problem based on the 3D Cauchy-type integral (CTI) method which makes it possible to represent the potential fields as surface integrals and the density or magnetization contrast surface needs to be discretized only for the calculation of the potential fields. Another significant objective is the development of a novel method for inversion of potential field data to recover the depth-to-basement using 3D Cauchy-type integral representation. Numerical studies show that the new method is much faster than the conventional method to compute the potential field. My synthetic model studies also show that the developed inversion algorithm is capable of recovering the geometry and depth of a sedimentary basin effectively with a complex density profile in the vertical direction. By nature, the recovered model from potential field inversion is usually very diffusive. Under these circumstances, one has to consider some other geophysical methods, such as electromagnetic (especially the magnetotelluric) methods, which have higher resolution and acceptable exploration cost. Conventional inversion of magnetotelluric (MT) data is aimed at determining the volumetric conductivity distribution. This dissertation develops a novel approach to 3D MT inversion for the depth-to-basement estimation. The key to this approach is selection of the depth-to-basement being the major unknown parameter. The inversion algorithm recovers both the thickness and the conductivities of a sedimentary basin. The sediment-basement interface is usually characterized by density, magnetization, and electrical conductivity contrasts. This makes realistic the joint inversion of potential field and MT data to recover the depth-to-basement. I have developed a joint inversion algorithm to recover the depth to-basement using MT and gravity data. The inversion can recover the physical properties and electrical conductivity simultaneously. The developed methods are illustrated on several realistic geological models. They have also been applied to the USGS field gravity data and synthetic MT data for the Big Bear Lake area.