Hankel tensor decomposition-based seismic signal reconstruction method

Abstract

The invention discloses a Hankel tensor decomposition-based seismic signal reconstruction method, which comprises the steps of constructing a Hankel objective function; converting the Hankel objectivefunction into a matrix completion objective function; and solving the function by using an alternate minimization method in an alternating manner. According to the Hankel tensor decomposition-based seismic signal reconstruction method, the problem of overhigh low-rank requirements on data due to the fact that regularization is carried out only through low-rank tensor decomposition is solved through introducing low-rank tensor decomposition and effectively combining a Hankel structure with a low-rank tensor, meanwhile, the decomposition process of solving a singular value of the tensor is avoided through the low-rank tensor decomposition method and the solving speed is greatly improved.

Description

technical field [0001] The technical field of seismic data processing of the present invention particularly relates to a seismic signal reconstruction method based on Hankel tensor decomposition. Background technique [0002] With entering the 21st century, the global economy has entered a new period of development, and the demand for energy from all walks of life has become increasingly strong. However, new energy has not been able to replace the core position of oil and natural gas in the energy system. With the rapid development of the global economy, all countries are heavily dependent on oil and natural gas, and their demand is increasing. The domestic consumption of oil and natural gas, a resource that is related to people's livelihood, has reached hundreds of millions of tons. [0003] Although our country has a large land area, compared with the Middle East, our country is not on the oil belt. Only some parts of Tibet are on the tail of the global rocky belt. Insuff...

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Problems solved by technology

[0003] Although our country has a large land area, compared with the Middle East, our country is not on the oil belt. Only some parts of Tibet are on the tail of the global rocky belt. Insufficient gas: With continuous exploitation, the rock reserves of Daqing Oilfield, which we are proud of, can only be used for decades

However, due to the increase in the dimension of high-dimensional data, the general low-rank performance will be worse than that of two-dimensional data

If the low rank of the data itself is not very good, the recovery accuracy of TNN will also be greatly affected, and because the core of TNN is based on the tensor singular value decomposition algorithm, this leads to a very high computational cost of the algorithm

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