Parallel computation of optimized arrays for 2-D electrical imaging surveys

Modern automatic multi-electrode survey instruments have made it possible to use non-traditional arrays to maximize the subsurface resolution from electrical imaging surveys. Previous studies have shown that one of the best methods for generating optimized arrays is to select the set of array configurations that maximizes the model resolution for a homogeneous earth model. The Sherman–Morrison Rank-1 update is used to calculate the change in the model resolution when a new array is added to a selected set of array configurations. This method had the disadvantage that it required several hours of computer time even for short 2-D survey lines. The algorithm was modified to calculate the change in the model resolution rather than the entire resolution matrix. This reduces the computer time and memory required as well as the computational round-off errors. The matrix–vector multiplications for a single add-on array were replaced with matrix–matrix multiplications for 28 add-on arrays to further reduce the computer time. The temporary variables were stored in the double-precision Single Instruction Multiple Data (SIMD) registers within the CPU to minimize computer memory access. A further reduction in the computer time is achieved by using the computer graphics card Graphics Processor Unit (GPU) as a highly parallel mathematical coprocessor. This makes it possible to carry out the calculations for 512 add-on arrays in parallel using the GPU. The changes reduce the computer time by more than two orders of magnitude. The algorithm used to generate an optimized data set adds a specified number of new array configurations after each iteration to the existing set. The resolution of the optimized data set can be increased by adding a smaller number of new array configurations after each iteration. Although this increases the computer time required to generate an optimized data set with the same number of data points, the new fast numerical routines has made this practical on commonly available microcomputers

Optimized arrays for 2-D resistivity survey lines with a large number of electrodes

Previous studies show that optimized arrays generated using the ‘Compare R’ method have significantly better resolution than conventional arrays. This method determines the optimum set of arrays by selecting those that give the maximum model resolution. The number of possible arrays (the comprehensive data set) increases with the fourth power of the number of electrodes. The optimization method faces practical limitations for 2-D survey lines with more than 60 electrodes where the number of possible arrays exceeds a million. Several techniques are proposed to reduce the calculation time for such survey lines. A single-precision version of the ‘Compare R’ algorithm using a new ranking function reduces the calculation time by two to eight times while providing results similar to the double-precision version. Recent improvements in computer GPU technology can reduce the calculation time by about seven times. The calculation time is reduced by half by using the fact that arrays that are symmetrical about the center of the line produce identical changes in the model resolution values. It is further reduced by more than thirty times by calculating the Sherman–Morrison update for all the possible two-electrode combinations, which are then used to calculate the model resolution values for the four-electrode arrays. The calculation time is reduced by more then ten times by using a subset of the comprehensive data set consisting of only symmetrical arrays. Tests with a synthetic model and field data set show that optimized arrays derived from this subset produce inversion models with differences of less than 10% from those derived using the full comprehensive data set. The optimized data sets produced models that are more accurate than the Wenner–Schlumberger array data sets in all the tests

Adaptive time-lapse optimized survey design for electrical resistivity tomography monitoring

Adaptive optimal experimental design methods use previous data and results to guide the choice and design of future experiments. This paper describes the formulation of an adaptive survey design technique to produce optimal resistivity imaging surveys for time-lapse geoelectrical monitoring experiments. These survey designs are time-dependent and, compared to dipole–dipole or static optimized surveys that do not change over time, focus a greater degree of the image resolution on regions of the subsurface that are actively changing. The adaptive optimization method is validated using a controlled laboratory monitoring experiment comprising a well-defined cylindrical target moving along a trajectory that changes its depth and lateral position. The algorithm is implemented on a standard PC in conjunction with a modified automated multichannel resistivity imaging system. Data acquisition using the adaptive survey designs requires no more time or power than with comparable standard surveys, and the algorithm processing takes place while the system batteries recharge. The results show that adaptively designed optimal surveys yield a quantitative increase in image quality over and above that produced by using standard dipole–dipole or static (time–independent) optimized surveys

Adaptive time-lapse optimized survey design for electrical resistivity tomography monitoring

Adaptive optimal experimental design methods use previous data and results to guide the choice and design of future experiments. This paper describes the formulation of an adaptive survey design technique to produce optimal resistivity imaging surveys for time-lapse geoelectrical monitoring experiments. These survey designs are time-dependent and, compared to dipole-dipole or static optimized surveys that do not change over time, focus a greater degree of the image resolution on regions of the subsurface that are actively changing. The adaptive optimization method is validated using a controlled laboratory monitoring experiment comprising a well-defined cylindrical target moving along a trajectory that changes its depth and lateral position. The algorithm is implemented on a standard PC in conjunction with a modified automated multichannel resistivity imaging system. Data acquisition using the adaptive survey designs requires no more time or power than with comparable standard surveys, and the algorithm processing takes place while the system batteries recharge. The results show that adaptively designed optimal surveys yield a quantitative increase in image quality over and above that produced by using standard dipole-dipole or static (time-independent) optimized survey

The inversion of data from very large three‐dimensional electrical resistivity tomography mobile surveys

New developments in mobile resistivity meter instrumentation have made it possible to survey large areas with dense data coverage. The mobile system usually has a limited number of electrodes attached to a cable that is pulled along behind an operator so that a large area can be covered within a short time. Such surveys can produce three‐dimensional datasets with hundreds of thousands of electrodes positions and data points. Similarly, the inverse model used to interpret the data can have several hundred thousand cells. It is impractical to model such large datasets within a reasonable time on microcomputers used by many small companies employing standard inversion techniques. We describe a model segmentation technique that subdivides the finite‐element mesh used to calculate the apparent resistivity and Jacobian matrix values into a number of smaller meshes. A fast technique that optimizes the calculation of the Jacobian matrix values for multi‐channel systems was also developed. A one‐dimensional wavelet transform method was then used to compress the storage of the Jacobian matrix, in turn reducing the computer time and memory required to solve the least‐squares optimization equation to determine the inverse model resistivity values. The new techniques reduce the calculation time and memory required by more than 80% while producing models that differ by less than 1% from that obtained using the standard inversion technique with a single mesh. We present results using a synthetic model and a field dataset that illustrates the effectiveness of the proposed techniques

2D characterization of near-surface V P/V S: surface-wave dispersion inversion versus refraction tomography

International audienceThe joint study of pressure (P-) and shear (S-) wave velocities (Vp and Vs ), as well as their ratio (Vp /Vs), has been used for many years at large scales but remains marginal in near-surface applications. For these applications, and are generally retrieved with seismic refraction tomography combining P and SH (shear-horizontal) waves, thus requiring two separate acquisitions. Surface-wave prospecting methods are proposed here as an alternative to SH-wave tomography in order to retrieve pseudo-2D Vs sections from typical P-wave shot gathers and assess the applicability of combined P-wave refraction tomography and surface-wave dispersion analysis to estimate Vp/Vs ratio. We carried out a simultaneous P- and surface-wave survey on a well-characterized granite-micaschists contact at Ploemeur hydrological observatory (France), supplemented with an SH-wave acquisition along the same line in order to compare Vs results obtained from SH-wave refraction tomography and surface-wave profiling. Travel-time tomography was performed with P- and SH- wave first arrivals observed along the line to retrieve Vtomo p and Vtomo s models. Windowing and stacking techniques were then used to extract evenly spaced dispersion data from P-wave shot gathers along the line. Successive 1D Monte Carlo inversions of these dispersion data were performed using fixed Vp values extracted from Vtomo p the model and no lateral constraints between two adjacent 1D inversions. The resulting 1D Vsw s models were then assembled to create a pseudo-2D Vsw s section, which appears to be correctly matching the general features observed on the section. If the pseudo-section is characterized by strong velocity incertainties in the deepest layers, it provides a more detailed description of the lateral variations in the shallow layers. Theoretical dispersion curves were also computed along the line with both and models. While the dispersion curves computed from models provide results consistent with the coherent maxima observed on dispersion images, dispersion curves computed from models are generally not fitting the observed propagation modes at low frequency. Surface-wave analysis could therefore improve models both in terms of reliability and ability to describe lateral variations. Finally, we were able to compute / sections from both and models. The two sections present similar features, but the section obtained from shows a higher lateral resolution and is consistent with the features observed on electrical resistivity tomography, thus validating our approach for retrieving Vp/Vs ratio from combined P-wave tomography and surface-wave profiling