The Laplace-domain waveform inversion is a full-waveform inversion method that recovers large-scale subsurface models. The inversion updates subsurface model parameters to minimize the differences between the modeled ...
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The Laplace-domain waveform inversion is a full-waveform inversion method that recovers large-scale subsurface models. The inversion updates subsurface model parameters to minimize the differences between the modeled and the observed wavefields in the Laplace domain. The inversion results can be used as an accurate initial model for subsequent high-resolution waveform inversions. Pure Laplace-domain wavefields can be obtained by transforming the time-domain signals using the Laplace transform of real variables. The real Laplace transform is mathematically identical to the Fourier transform using the imaginary angular frequency;however, the Laplace transform using only real variables is computationally more efficient than that using complex variables. The Laplace-transformed wavefields are real-valued signals, and thus, it is natural to use real values in the Laplace-domain waveform inversions. However, the real logarithm function in the logarithmic objective function cannot handle negative values. Inversions using complex logarithms can solve this problem, but they demand more memory and computations than those required for inversions using real variables only. We suggest a simple method to overcome the negative-value problem for the real logarithm in the objectivefunction. By taking the absolute values of the negative signals in the logarithmic objective function, we can obtain inversion results from inversions using real variables only that are equivalent to those from inversions using complex variables. We demonstrate the proposed method using the Society of Exploration Geophysicists (SEG)/European Associations of Geoscientists x0026;Engineers (EAGE) salt model and a field data set. The inversions using real variables only took less than 22x0025;of the time of the inversion using complex variables in the numerical examples.
3D laser scanning is becoming a standard technology to generate building models of a facility's as-is condition. Since most constructions are constructed upon planar surfaces, recognition of them paves the way for...
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ISBN:
(纸本)9781479959372
3D laser scanning is becoming a standard technology to generate building models of a facility's as-is condition. Since most constructions are constructed upon planar surfaces, recognition of them paves the way for automation of generating building models. This paper introduces a new logarithmically proportional objectivefunction that can be used in both heuristic and metaheuristic (MH) algorithms to discover planar surfaces in a point cloud without exploiting any prior knowledge about those surfaces. It can also adopt itself to the structural density of a scanned construction. In this paper, a metaheuristic method, genetic algorithm (GA), is used to test this introduced objectivefunction on a synthetic point cloud. The results obtained show the proposed method is capable to find all plane configurations of planar surfaces (with a wide variety of sizes) in the point cloud with a minor distance to the actual configurations.
Most frequent surface shapes of man-made constructions are planar surfaces. Discovering those surfaces is a big step toward extracting as-built/-is construction information from 3D point cloud. In this paper, a real-c...
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ISBN:
(纸本)9783319165486;9783319165493
Most frequent surface shapes of man-made constructions are planar surfaces. Discovering those surfaces is a big step toward extracting as-built/-is construction information from 3D point cloud. In this paper, a real-coded genetic algorithm (GA) formulation for planar surfaces recognition in 3D point clouds is presented. The algorithm developed based on a multistage approach;thereby, it finds one planar surface (part of solution) at each stage. In addition, the logarithmically proportional objectivefunction that is used in this approach can adapt itself to scale and spatial density of the point cloud. We tested the proposed application on a synthetic point cloud containing several planar surfaces with different shapes, positions, and with a wide variety of sizes. The results obtained showed that the proposed method is capable to find all plane's configurations of flat surfaces with a minor distance to the actual configurations.
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