Random noise reduction method of complex exploration based on a fractional optimal control network

Abstract

In field seismic prospecting, the existence of complex random noise strongly influences the detection of effective reflected signals and imposes adverse effects on the subsequent process of the data. With the exploration of unconventional petroleum resources, high-quality seismic records are needed, and the performance of conventional methods in processing needs to be further improved. To solve this problem, a fractional optimal control network (FOC-NET) is utilized to cope with complex noise reduction. Unlike conventional DnCNN, FOC-NET has a hierarchical architecture. It can cope with the feature loss caused by a single-scale feature extraction strategy. Then, the potential feature of the analyzed data can be accurately captured in combination with the different-scale information. Notably, the multi-scale fusion capability can also improve the recovery of weak reflection events and complex noise attenuation. Both synthetic and field experimental results demonstrate that FOC-NET can effectively suppress random noise and restore the desired signals, even under low-SNR conditions.

Keywords

• seismic exploration
• random noise reduction
• low-snr
• convolutional neural networks
• fractional optimal control network
• fractional