Efficient detection algorithm of high frequency clutter data in geological exploration

Abstract

During the detecting process of geological exploration data, there is a variety of noise interferences, resulting in inaccurate data detection. When the current detection algorithm is used to detect high frequency clutter data, detection efficiency is low. To this end, a detection algorithm of high frequency clutter data based on optimized BP neural network particle swarm algorithm is proposed. First, improved wavelet threshold denoising algorithm is utilized for clutter data denoising. Then, an optimized BP neural network particle swarm algorithm is employed to detect the clutter data. Experimental results show that the proposed algorithm improves the accuracy of data detection.

Keywords:

• Geological exploration
• High frequency clutter data
• Wavelet threshold
• Particle swarm optimization algorithm