Local and global optimization algorithms are used commonly in geophysical data inversion. Each type of algorithm has unique advantages and disadvantages. Here we propose several methods of combining the two algorithms such that we can overcome their drawbacks and make use of the salient features of the two methods. In particular, we combined a local conjugate gradient (CG) method with a global very fast simulated annealing (VFSA) approach to solve problems of geophysical interests. We conducted a systematic study to find an efficient strategy to combine CG and VFSA optimization schemes and recommend a couple of ways for future implementations.

The inversion of resistivity profiling data involves estimation of the spatial distribution of resistivities and thicknesses of rock layers from the apparent resistivity data values measured in the field as a function of electrode separation. The drawbacks of using traditional curve-matching techniques to solve this inverse problem have been overcome by iterative linear techniques but these require good starting models even if the shape of the causative body is asssumed known. In spite of the recent developments in inversion techniques, no robust method exists for the inversion of resistivity profiling data for the simple model of dikes and spheres which are the classical models of geophysical prospecting.We apply three different non-linear inversion schemes to invert synthetic resistivity profiling data for the classical models embedded in a uniform matrix of contrasting resistivity. The three non-linear algorithms used are called the Metropolis simulated annealing (SA), very fast simulated annealing (VFSA) and a genetic algorithm (GA). \7e compare the performance of the three algorithms using synthetic data for an outcropping vertical dike model. Although all three methods were successful in obtaining optimal solutions for arbitrary starting models, VFSA proved to be computationally the most efficient.

This study presents an effective technique for obtaining formation azimuthal shear‐wave anisotropy parameters from four‐component dipole acoustic array waveform data. The proposed technique utilizes the splitting of fast and slow principal flexural waves in an anisotropic formation. First, the principal waves are computed from the four‐component data using the dipole source orientation with respect to the fast shear‐wave polarization azimuth. Then, the fast and slow principal waves are compared for all possible receiver combinations in the receiver array to suppress noise effects. This constructs an objective function to invert the waveform data for anisotropy estimates. Finally, the anisotropy and the fast shear azimuth are simultaneously determined by finding the global minimum of the objective function. The waveform inversion procedure provides a reliable and robust method for obtaining formation anisotropy from four‐component dipole acoustic logging. Field data examples are used to demonstrate the application and features of the proposed technique. A comparison study using the new and conventional techniques shows that the new technique not only reduces the ambiguity in the fast azimuth determination but also improves the accuracy of the anisotropy estimate. Some basic quality indicators of the new technique, along with the anisotropy analysis results, are presented to demonstrate the practical application of the inversion technique.

Successful inversion of geophysical data depends on prior information, proper choice of inversion scheme, and on effective parameterization of the model space such that the model representation is appropriate and efficient. Inversion of resistivity data has long been recognized as a nonlinear or quasi‐linear problem. Traditionally, 2-D resistivity inversion has been performed by trial and error methods and with linear and iterative linear methods. The linear and iterative linear methods are limited because of the requirement of good prior knowledge of the subsurface. Unlike linear and iterative linear methods, most nonlinear inversion schemes do not depend strongly on the starting solution, but prior information helps to reduce the computational cost and to obtain geologically meaningful results. In the present study, we have applied a nonlinear optimization scheme called very fast simulated annealing (VFSA) in the inversion of 2-D dipole‐dipole resistivity data to image the subsurface. Unlike Metropolis simulated annealing (SA) in which each new model is drawn from a uniform distribution, VFSA draws a model from a Cauchy‐like distribution, which is also a function of a control parameter called temperature. The advantage of using such a scheme is that at high temperatures, the algorithm allows for searches far beyond the current position, while at low temperatures, it looks for improvement in the close vicinity of the current model. We have used the mean square error between the synthetics and original data as the error function to be minimized. The synthetic response for 2-D models was obtained by finite‐difference modeling, and cubic splines were used to parameterize the model space to get smooth images of the subsurface and to reduce computational cost. VFSA was used to estimate the conductivity at each spline node location. The inversion was applied to various synthetic data to study the influence of the starting solution and the location of the spline nodes. Finally, we applied it to real data collected over a disseminated sulfide zone at Safford, Arizona, and compared the results with those obtained from a linearized inversion and from a model based on geologic and well‐log data. The VFSA results are in good agreement with the previously published results.

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