Enhancing seismic data by edge-preserving geometrical mode decomposition

Real-time seismic signals are intertwined with different types of noises during the generation, acquisition, and transmission process. The enhanced data with high resolution assists to interpret and analyze records more accurately. In this paper, we propose a mathematical approach based on recently developed geometrical mode decomposition (GMD) and adaptive self -guided filter (ASGF) to attenuate noise from two-dimensional seismic data. GMD is first applied to decompose the 2D seismic data into a number of band -limited intrinsic mode functions. This algorithm is capable of separating the linear and non-linear seismic events into linear modes and optimizing the linear patterns within amplitude frequency modes. The noisy modes are selected and attenuated by an adaptive self -guided filter. The GMD method is experimentally verified with a strong theoretical background to address the directional features of the image. ASGF is an exceptional edge -preserving filter and hence the hybrid algorithm could utilize the advantages of both methods. Higher the signal-to-noise ratio, improving the resolution of the image and preserving the directional properties and edges of the seismic events are the paramount characteristics of the proposed model. The simulating results on both synthetic and real seismic data proved the technique is more promising compared to the existing methods.