Modeling and inversion of self-potential data

This dissertation presents data processing techniques relevant to the acquisition, modeling, and inversion of self-potential data. The primary goal is to facilitate the interpretation of self-potentials in terms of the underlying mechanisms that generate the measured signal. The central component of this work describes a methodology for inverting self-potential data to recover the three-dimensional distribution of causative sources in the earth. This approach is general in that it is not specific to a particular forcing mechanism, and is therefore applicable to a wide variety of problems. Self-potential source inversion is formulated as a linear problem by seeking the distribution of source amplitudes within a discretized model that satisfies the measured data. One complicating factor is that the potentials are a function of the earth resistivity structure and the unknown sources. The influence of imperfect resistivity information in the inverse problem is derived, and illustrated through several synthetic examples. Source inversion is an ill-posed and non-unique problem, which is addressed by incorporating model regularization into the inverse problem. A non-traditional regularization method, termed "minimum support," is utilized to recover a spatially compact source model rather than one that satisfies more commonly used smoothness constraints. Spatial compactness is often an appropriate form of prior information for the inverse source problem. Minimum support regularization makes the inverse problem non-linear, and therefore requires an iterative solution technique similar to iteratively re-weighted least squares (IRLS) methods.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Earth, Atmospheric, and Planetary Sciences.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Minsley, Burke J

Advisor dc:contributor.advisor

• Frank Dale Morgan.

• Earth, Atmospheric, and Planetary Sciences.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/40863

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/40863