Intelligent seismic data anti-aliasing method and system based on u-net network

Abstract

The present invention relates to a kind of intelligent seismic data anti-aliasing method and system based on U-Net network, the method comprises the following steps: (1) build the U-Net network f for seismic data anti-aliasing; (2) obtain Simulated data training pair: including simulated non-aliased seismic data and aliased seismic data; (3) using simulated aliased seismic data as input and non-aliased seismic data as training labels to train the U‑Net network to obtain the trained Network parameter θ * (4) Based on the transfer learning method, using part of the actual aliasing seismic data with labels, the trained network parameters θ * Perform fine-tuning to obtain optimized network parameters (5) use the optimized U-Net network to perform cyclic iterations on the seismic data to be processed to obtain separated seismic data. Compared with the prior art, the present invention avoids assumptions about data linearity, sparsity, and low rank, and has high anti-aliasing processing efficiency, good stability, and high precision.

Description

Technical field [0001] The present invention relates to an seismic data derived method and system, in particular, to an intelligent seismic data derived method and system based on a U-NET network. Background technique [0002] Earthquake data mixed collection is one of the methods effectively improves the efficiency of seismic data acquisition, but the existence of mixed noise, a certain challenge to subsequent seismic data processing and offset imaging, so the aliasing method is widely concerned. [0003] Traditional derriation method generally includes a method of filtering type and inversion type: filter type method includes median filtering, improved median filtering, FX counterattack, etc., inversion type method includes based on sparse transformation, dictionary learning and other threshold iterations Shrink method. Traditional dealing methods generally have a certain premise assumption, such as linear, sparsiness or low rank; in addition, as the scale of seismic data incre...

AI Technical Summary

Problems solved by technology

Traditional anti-aliasing methods generally have certain prerequisites for seismic data, such as linearity, sparsity, or low rank; in addition, as the scale of seismic data increases, the amount of calculation increases rapidly, and the optimization parameters corresponding to different data are different. Manual adjustment is required, which further increases the calculation time

Images