Reflection Signal Detection Algorithm for Desert Seismic Data Based on Gaussianity Testing
T. Zhong, Y. Li, B. Yang and S. Zhang
Event name: 2nd Conference on Geophysics for Mineral Exploration and Mining
Session: New Developments in Joint Modelling, Inversion and Integration of Geophysical Data A
Publication date: 09 September 2018
Info: Extended abstract, PDF ( 777.12Kb )
Price: € 20
The pathway for getting high accuracy seismic data is to effectively attenuate the random noise existing in the seismic records. However, in desert seismic data processing, the performances of the conventional noise attenuation methods sometimes are not as good as expected due to the low fundamental frequency of the random noise. Here, a novel reflection signal detection algorithm based on Gaussianity analysis is proposed. The basic idea of the proposed algorithm is to extract the efficient reflection information by utilizing the differences between the random noise and the reflection signals in terms of the Gaussianity. The Jarque-Bera test is used to investigate the Gaussianity of the seismic data. By analyzing the test statistics, the reflection signals could be detected. The experimental results indicate that the proposed algorithm can track the reflection signals well. The finding also has further implications for noise reduction and seismic signal processing algorithms.