Simulation of seismic-prospecting random noise in the desert by a Brownian-motion-based parametric modeling algorithm

Abstract Random noise has a negative impact on seismic-prospecting record processing. An important step to improve the methods aimed at the attenuation of random noise is to scientifically characterize the properties of the noise. Numerical modeling is useful to understand the nature of the random noise. In this study, we present a Brownian-motion-based parametric modeling algorithm for the simulation of seismic-prospecting random noise in the desert. The optimal Hurst exponent required to implement the method can be determined by comparing the spectral properties related to the noise data and the simulated results. The data used to analyze the properties of the noise were acquired in the Tarim Basin (Northwest of China). We verify the performance of the modeling algorithm by comparing the results obtained after the simulation with the real noise data in both the time domain and the spatio-temporal domain. The experimental results thus obtained prove the accuracy and efficiency of the proposed modeling algorithm. This study can be used as a basis to investigate the seismic-prospecting random noise characteristics and thus contribute to its mitigation.

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