Multiple-wave self-adaptive subtraction algorithm based on filter form detection

A technology for predicting multiple waves and filters, applied in the field of geophysical exploration, can solve problems such as poor data adaptability

Active Publication Date: 2020-05-22
CHINA NAT OFFSHORE OIL CORP +1
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Problems solved by technology

Since the matched filter of the same shape is used for each data window, the traditional multiple wave adaptive subtraction algorithm based on the two-dimensional convolutional mixture model is not suitable for complex actual data.

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  • Multiple-wave self-adaptive subtraction algorithm based on filter form detection
  • Multiple-wave self-adaptive subtraction algorithm based on filter form detection
  • Multiple-wave self-adaptive subtraction algorithm based on filter form detection

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Embodiment Construction

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0034] combine figure 1 As shown, a multiple wave adaptive subtraction algorithm based on filter shape detection, the method includes the following steps:

[0035] S1, windowing the data;

[0036] S2, the one-dimensional cross-correlation between the predicted multiples and the original data in the time direction, and estimate the time shift operator δ(Δt), the time misalignment Δt between the predicted multiples and the real multiples;

[0037] Using a common me...

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Abstract

The invention discloses a multiple-wave self-adaptive subtraction algorithm based on filter form detection. The multiple-wave self-adaptive subtraction algorithm comprises the following steps of: S1,performing window partitioning on data; S2, predicting the one-dimensional cross-correlation of multiple waves and original data in the time direction, and performing estimation to obtain a time warpdelta t between the predicted multiple waves of a time shift operator and real multiple waves; S3, estimating a filter form; S4, estimating an optimal matching filter of the filter form constraint; S5, acquiring a primary wave; and S6, combining the different window data to obtain primary wave complete data. According to the method, a more accurate multiple-wave estimation result can be obtained by using the multiple-wave self-adaptive subtraction algorithm based on filter form detection, the primary wave and multiple-wave separation effect is better, and multiple-wave signals can be more effectively removed while the primary wave signals are better protected.

Description

technical field [0001] The invention belongs to the technical field of geophysical exploration, in particular to a multiple wave adaptive subtraction algorithm based on filter shape detection. Background technique [0002] Adaptive multiple subtraction is an important part of multiple suppression under the prediction-subtraction framework. The multiple adaptive subtraction method can eliminate the difference between the multiples predicted by the multiple prediction algorithm and the real multiples, so that the primary waves and multiples in the original data can be effectively separated. Since the matched filter of the same form is used for each data window, the traditional multiple wave adaptive subtraction algorithm based on the two-dimensional convolutional mixture model is not suitable for complex actual data. Therefore, there is a need for a method that can adaptively reflect and predict multiple wave structure information. Contents of the invention [0003] The ob...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01V1/28G01V1/36
CPCG01V1/282G01V1/364
Inventor 邓勇陆文凯赫建伟黎孝璋任婷鲁统祥王瑞敏刘磊
Owner CHINA NAT OFFSHORE OIL CORP
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