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.

中文翻译：

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通过保留边缘的几何模式分解增强地震数据

实时地震信号在产生、采集和传输过程中与不同类型的噪声交织在一起。高分辨率的增强数据有助于更准确地解释和分析记录。在本文中，我们提出了一种基于最近开发的几何模态分解（GMD）和自适应自引导滤波器（ASGF）的数学方法来衰减二维地震数据中的噪声。 GMD 首先用于将二维地震数据分解为许多带限固有模式函数。该算法能够将线性和非线性地震事件分离为线性模式，并优化幅频模式内的线性模式。噪声模式由自适应自引导滤波器选择和衰减。 GMD方法经过实验验证，具有很强的理论背景，可以解决图像的方向特征。 ASGF 是一种特殊的边缘保留滤波器，因此混合算法可以利用这两种方法的优点。更高的信噪比、提高图像的分辨率并保留地震事件的方向特性和边缘是该模型的首要特征。对合成地震数据和真实地震数据的模拟结果证明，与现有方法相比，该技术更有前景。