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Data acquisition station (DAS) seismic data noise reduction method based on convolutional neural network

A convolutional neural network and seismic data technology, applied in seismic signal processing, etc., can solve problems such as incomplete noise suppression, effective signal attenuation, and long parameter adjustment time, and achieve obvious advantages of noise reduction, noise removal, and resolution Effects with Fidelity Preservation

Inactive Publication Date: 2019-07-26
JILIN UNIV
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Problems solved by technology

[0005] The present invention provides a DAS seismic data denoising method based on a convolutional neural network to solve the problems of incomplete noise suppression, serious effective signal attenuation, long parameter adjustment time, low efficiency, and poor adaptability in existing denoising methods. question

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  • Data acquisition station (DAS) seismic data noise reduction method based on convolutional neural network
  • Data acquisition station (DAS) seismic data noise reduction method based on convolutional neural network
  • Data acquisition station (DAS) seismic data noise reduction method based on convolutional neural network

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Embodiment Construction

[0051] Include the following steps:

[0052] (1) DAS seismic data acquisition

[0053] Laying optical cables along the optical fiber logging is different from the traditional seismic wave signal acquisition system that needs to arrange geophones at equal intervals. An optical cable is an intelligent multi-functional sensing system. Information such as sampling channel spacing can be set according to actual needs, and the source selection, etc. High hammer, rock knocking, etc., a single sensor information is collected by a high-speed oscilloscope and processed by a computer to obtain a seismic data waveform record. The length of the record is proportional to the receiving time. After combining N channels of records, a two-dimensional DAS seismic data;

[0054] (2) Network structure

[0055] figure 1 Shown is the constructed 16-layer denoising network model, which contains a total of 16 modules; the first module is composed of Conv and ReLU, the middle 14 modules are composed...

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Abstract

The invention relates to a DAS seismic data noise reduction method based on convolutional neural network, and belongs to the noise reduction method of two-dimensional DAS seismic data. On the basis ofthe convolutional neural network, a network structure is modified and parameters are set up to adapt to a noise reduction network model of DAS seismic data noise reduction processing, the convolutional neural network automatically extracts characteristics of effective signals and noise and intelligent noise reduction of the DAS seismic data is further realized according to characteristic differences. According to the DAS seismic data noise reduction method, noise in the DAS seismic data can be effectively removed, reflection coaxial information is well protected, while the signal to the noiseratio of the DAS seismic data is increased, resolution and fidelity of the data are unaffected, powerful guarantee is provided for subsequent accurate acquisition of reflection amplitude, velocity and frequency information, and the DAS seismic data noise reduction method can be widely used in noise suppression of the DAS seismic data.

Description

technical field [0001] The invention relates to a method for reducing noise of two-dimensional DAS seismic data acquired in an actual seismic exploration environment. Background technique [0002] Distributed optical fiber acoustic sensor (DAS) is a new type of exploration data acquisition technology, which has the advantages of array reception, high efficiency and low cost, wide frequency, strong anti-electromagnetic interference ability, etc., and has become a research hotspot in exploration data acquisition worldwide. , has broad development prospects. However, the widespread application of DAS technology still faces some challenges, one of which is that the noise level of the collected data is generally high, with the characteristics of "weak signal, strong interference". In addition, the types of noise in DAS data are also extremely complex. There are strong coherent noises caused by cable slapping and ringing along the borehole casing, and long-period and high-amplitu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01V1/28
CPCG01V1/28
Inventor 李月赵玉星杨宝俊邵丹王胜男
Owner JILIN UNIV
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