Understanding Complications in Near-Surface Geophysical Inverse Problems II Posters
Presiding: P S Routh, Boise State University; G A Oldenborger, Boise State University
NS23A-01 1330h
Simultaneous Inversion of Resistivity and Magnetic Permeability from MT Data
MT ( Magnetotellurics Method ) is one of most important geophysical prospecting methods. Traditionally, MT data inversion is carried out based on the supposition that the rocks are of magnetic permeability equaling to that of free-space. However, lava and other a few rocks often have higher magnetic permeability than that in free-space. It has been proved that the rocks with a relative magnetic permeability of large than 1.005 may cause remarkable effect on the magnetotellurics responses. We proved, by forward modeling, that the pseudo-resistivity may increase by 5 ohm when the relative magnetic permeability is large than 1.01. There are a few rocks, including lava, that are often of a relative magnetic permeability of larger than 1.01. So, if we do MT investigation in the area where there are rocks with higher magnetic permeability, we should take the magnetic parameter into account in the MT inversion, i.e. we should carry out. simultaneous inversion on resistivity and magnetic permeability. Based on Randall Mackie's MT inversion strategy, we develop a new MT data inversion method that reconstructs the resistivity and magnetic permeability simultaneously. Cases study showed that the new inversion strategy could improve the precision of the resistivity image reconstructed and supply magnetic feature information of the rocks, which, in the case study, is very important for the determinative judgement to be carry out. Simultaneous inversion of MT data for resistivity and magnetic permeability is a difficulty non-linear inverse problem. There are a few factors that may affect the image reconstruction, and there is room in our inversion program to be improved. The work was supported by NSFC under grant No.40274019 and 49894190.
NS23A-02 1330h
Survey Design Optimization Using Resolution Measure of Point Spread Function
One of the goal in geophysical survey design is to obtain enhanced information in certain regions of the model. These regions could be static in nature i.e. location of targets in the ground such as contaminants or utilities or dynamic in nature i.e. tracking movements of contaminants in subsurface. Depending upon the goal, the objective is to determine optimal survey parameters, such as position of sources/receivers and possibly frequencies in EM experiments, that would provide 'better' model resolution in a region of interest. We pose this as an inverse problem by maximizing a resolution measure, the point spread function. The goal is to adjust the survey parameters so that the point spread function (PSF) is as delta-like as possible. This is solved as a nonlinear optimization problem with constraints on the parameters. Due to the highly nonlinear nature of the problem we examine two approaches for its solution. The first is a local, (Newton) strategy that use a primal interior point method to incorporate bounds on the parameters; the second is global method, simulated annealing. We examine different objective function crietria to solve this problem. The choice of these objective functions are crucial to impose logistical constraints in the problem. In addition to optimization problem we will also show how these PSF measures can be used to compare between different surveys. This is particularly useful when dealing with very large scale problems. We present examples from seismic tomography and controlled source EM.
http://cgiss.boisestate.edu/~routh/research.html
NS23A-03 1330h
Electrical Resistivity Inversion Constrained by Resistivity Cone Penetrometry (RCPT): Application to Landfill Site Characterisation
Areas of clay-rich tills in the UK are attractive sites for landfills. The relatively impermeable clays act as a barrier between landfill contents and surrounding permeable materials, but tills often contain sand and gravel deposits, which may be water-bearing and/or hydraulically connected to aquifers or surface water bodies. The sands and gravels may be missed by borehole and trial-pit led site investigations but cause flooding of landfill excavations or act as leachate flow routes. Electrical resistance tomography (ERT) is potentially an appropriate subsurface imaging tool for landfill site characterisation, due to its ability to distinguish permeable sand and gravel from clays.. However, engineers want more accurate identification of interface depth than standard ERT inversions supply. Here, the use of reference geoelectrical models based on Resistivity Cone Penetrometry (RCPT) data is investigated. The use of RCPT data can potentially guide the inversion algorithm towards improved solutions. A series of synthetic electrical resistivity and RCPT data from model profiles with various shaped sand bodies within a clay background were generated, and inverted with and without RCPT constraints. The ability of the guided inversion to reconstruct the original geoelectrical model was assessed quantitatively. Preliminary results indicate that using RCPT data as a constraint can significantly improve interfacial depth accuracy in the inverted data. The use of depth information to constrain 1-D layer models in resistivity sounding is already well established. Hence, an alternative approach to the incorporation of depth information is construction of a pseudo-2D resistivity profile from 1-D soundings extracted from ERT data, inverted using depth-constrained layer models. The accuracy and limitations of this approach will be compared with the reference model approach described above. Future work will include the acquisition and processing of field RCPT and ERT data.
NS23A-04 1330h
Large-scale 3D inversion of frequency domain controlled-source electromagnetic data
Controlled Source Audio-Frequency Magnetotellurics (CSAMT) is a frequency domain EM sounding technique. The CSAMT source is a grounded horizontal electric dipole approximately one to two kilometers in length. This dipole source generates both inductive and galvanic currents so that the observed electric field arises due to both the static the vector potentials. At low frequencies, the behavior of the fields is similar to that observed in a geometric sounding such as a direct current experiment. At higher frequencies, the inductive character of the source modifies the behavior of the fields so that the experiment becomes more like a frequency sounding. Higher frequency data are useful for imaging near-surface features and lower frequency data are sensitive to deeper structure. Inversion of controlled source EM data provides a means to image the subsurface electrical conductivity structure. We consider a 3D CSAMT data set acquired over a known geothermal resource area in Western Idaho. The data are amplitudes and phases of the electric and magnetic fields acquired at 25 frequencies. The conductivity contrast between the geothermal fluid conduits and the resistive host material allows us to relate the inverted conductivity image to the distribution of fluid flow pathways in the geothermal system. Our 1D CSAMT inversion of the 3D data set indicates regions of conductive fluid pathways in the subsurface. Our next step is to invert these data using the full Maxwell's equations in 3D. Inversion of a single frequency data set at 2 Hz using the 3D frequency domain inversion algorithm (Haber et. al, 2004) shows regions of fluid circulation indicated by zones of higher conductivity. Comparing the images from different single frequency inversions allows us to identify persistent features in the conductivity image that adequately satisfy the data. With the aid of synthetic modeling we are investigating what frequencies? and what geometries? are appropriate to better resolve these targets. In this paper we will present a strategy to invert a large-scale EM data set. Haber, E., Ascher, U. and Oldenburg, D., 2004, Inversion of 3D electromagnetic data in frequency and time domain using an inexact all-at-once approach: Geophysics, Soc. of Expl. Geophys., 69, 1216-1228.