Fast Seismic Inversion Methods Using Ant Colony Optimization Algorithm

Abstract

This letter presents ACO R -V , a new computationally efficient ant-colony-optimization-based algorithm, tailored for continuous-domain problems. The ACO R -V algorithm is well suited for application in seismic inversion problems, owing to its intrinsic features, such as heuristics in generating the initial solution population and its facility to deal with multiobjective optimization problems. Here, we show how the ACO R -V algorithm can be applied in two methodologies to obtain 3-D impedance maps from poststack seismic amplitude data. The first methodology pertains to the traditional method of forward convolution of a reflectivity model with the estimated wavelet, where ACO R -V is used to guess the appropriate wavelet as the reflectivity model. In the second methodology, we propose an even faster inversion algorithm based on inverse filter optimization, where ACO R -V optimizes the inverse filter that is deconvolved with the seismic traces and results in a reflectivity model similar to that found in well logs. This modeled inverse filter is then deconvolved with the entire 3-D seismic volume. In experiments, both the methodologies are applied to a synthetic 3-D seismic volume. The results validate their feasibility and the suitability of ACO R -V as an optimization algorithm. The results also show that the second methodology has the advantages of a much higher convergence speed and effectiveness as a seismic inversion tool.

Vertical electrical sounding data inversion using continuous ant colony optimization algorithm: A case study from Hassi R'Mel, Algeria

“…Although in our case the solution space is continuous, so we used a continuous domain (or real coded) extended version of ACO, called ACOR, according to the guidelines suggested by Socha (2004). This algorithm has been used to solve multiple problems (Khalidji et al, 2009;Fetanat and Shafipour, 2011;Conti et al, 2013;Fetanat and Khorasaninejad, 2015;Omran and Al-Sharhan, 2019). The algorithm shifts from using a discrete probability (related to pheromone concentration) to a continuous probability density function (PDF).…”

Optimal Seismic Reflectivity Inversion: Data-Driven $\ell_p$ -Loss-$\ell_q$ -Regularization Sparse Regression

“…We propose to use the cross-validation (CV) [21] approach to choose the optimal value of damping factor, which works for parameter selection according to its statistical performance. Cross-validation provides the optimal bias-variance tradeoff under the setting of equation (4). The K-fold CV is adopted roughly as: First, randomly split the data into K equal clusters; Second, the kth cluster (k = 1, 2, ⋯, K) is used for testing the optimization model fitted using the data from other cluster; Third, the CV prediction error for each λ is calculated; Then, repeat the random selection; In the end, combine all the K prediction errors together to obtain the CV estimate:…”

“…Likewise, [3] extended seismic spectrum using a non-stationary wavelet estimation. A seismic reflectivity obtained from seismic amplitude inversion has been used in the applications with little/weak well controls [4]. For regularized inversion, besides the ℓ 2 norm constraint, ℓ 1 norm regularization is more popular in sparse spike inversion [5], [6].…”

Ant Colony Optimization Inversion Using the L1 Norm in Advanced Tunnel Detection

“…e core of this behavior is the indirect communication between ants via the chemical pheromone trails, which allow them to find shortcuts between the nest and a food source through their cooperation. ACO algorithm has been widely used in optimization problems, which has advantages of positive feedback, parallelism, and robustness [17]. Since the observed data generated by different electrode intervals is related to the buried depth [18], the data weighting factor is introduced into the ACO to optimize the algorithm.…”