Conformal Combination Excitation Parameter Parallel Algorithm Research

In an area where geological condition is complicate. The contradiction between the existing combination technology and exploration object is more and more prominent:On the one hand, the signal-to-noise ratio and resolution of seismic data from complex areas tend to be low. It need to apply the combination technology to improve the quality of the data, especially. On the other hand, this kind of region surface relief fluctuations tend to be very intense. Combination of assumptions is difficult to set up. In order to ensure the combination of complex surface conditions to achieve the goals of multiple shooting and receiving that need to face all kinds of complicated optimization problem. Mainly reflects in the objectives and constraints to the complexity of complex, more extreme value, high dimension, goals, and there are many uncertain factors. These modeling method and algorithm innovation brings serious challenges. How to build a proper optimization model and create practical and efficient algorithm is the key to solve the problem.In this paper, on the basis of the predecessors, the main research contents and achievements include three aspects. (1) The improvement of the irregular surface under the condition of seismic array theory, including combination of response function of the characterization. The combined Multi-frequency body wave response regularity. Combination of seismic wave, the directionality and focusing capability of problems. These problems are to establish a total forms the basis of the theory of conformal combination source array. (2) Irregular surface under the condition of combination parameter optimization design technology, including:The coefficient of the objective function to establish, weighted algorithm, element interval algorithm, Combined global search optimization algorithm, etc. Selected balance calculation accuracy and efficiency of the best global optimization numerical algorithm (3) Based on MPI parallel particle swarm optimization algorithm. The PSO evolution strategy is introduced into the greedy algorithm optimization search process. Using particle swarm group intelligence features improve the global search performance of Greedy algorithm. The experiments for solving time, solving the complexity, The respect such as parallel speedup comparing and analysis, validate the feasibility and effectiveness of the algorithm.