The detection and up-picking of the seismic events are critical for seismic data analysis and interpretation. Events picking can be used for sequence stratigraphic analysis, reservoir feature extraction, the determining of the subsequent reflection interface, the improving of the SNR and the storage prediction. The research of the events picking is very significant for the seismic exploration. In order to overcome the existing events picking methods have the same sensitivity to noise, we propose a non-classical receptive field visual cognitive method for the events picking UP. Vision is an important functional organ for human beings to observe and recognize the world. About 80% of the information obtained by human beings from the outside world comes from the visual system, which fully shows that visual information is huge_ and also shows that human beings have a high utilization rate of visual information. How to transfer some typical information processing mechanism and target recognition function of human vision to machine is one of the most important and urgent tasks in the field of computer vision and artificial intelligence. The introduction of computer vision technology into geophysical prospecting is still in its infancy in the field of seismic exploration, our research fill the blank of this field, where the use of visual features to improve the seismic data processing and rapid realization of oil and gas exploration, will become the vane of the future direction of research and development. As a basic research work in the crossing field, this paper has made a breakthrough in the research methods and ideas, and the research content can be summarized as the following four aspects: 1. The proposed method implements the function of environmental suppression and spatial enhancement of the bio-visual primary visual cortex, which is applies to the ore-stack seismic data, as ore-stack seismic data contains abundant information such as amplitude and frequency to reflect tiny structures of the formation. 2. The seismic data is preprocessed to obtain the wavelet fusion of the envelope peak instantaneous frequency (EPIF) and the slant stack peak amplitude (SSPA), which can maximum the limit to provide optimal quality data. 3. An adaptive Gabor filter direction selection method is proposed to provide a reliable angle range and improve the recognition rate of filter decomposition. In addition. by adopting an anisotropic environmental suppression method, our method can detect edge variability more accurately than the isotropic method. 4. With the enhanced contour aggregation, cocircular constraint is adopted and combined with the characteristics of low curvature and continuous changing curvature, which is unique to the seismic events, to establish a consistent spatial structure perception model. The events picked by our method is more continuous and accurate than the existing methods, and doesn't require human interaction, which is beneficial for subsequent seismic interpretation and reservoir prediction
