Clustering seismic facies analysis method based on feature coding of restricted Boltzmann machine

Abstract

The invention discloses a clustering seismic facies analysis method based on feature coding of a restricted Boltzmann machine, which comprises the following steps of: (1) processing actual seismic data, eliminating noise in a seismic signal and improving a signal-to-noise ratio; (2) based on the seismic sedimentology principle, forming formation slices through uniform segmentation along the top and bottom boundaries of a target layer; (3) introducing an unsupervised restricted Boltzmann machine dimension reduction algorithm based on the formation slices, and extracting potential seismic waveform information capable of expressing real target layer reservoir feature changes while reducing dimensions; and (4) for feature selection after dimension reduction, completing spatial clustering analysis of seismic waveform data, and forming a corresponding seismic facies diagram. According to the method, research is carried out on the aspects of denoising, feature extraction, unsupervised learning, semi-supervised learning and the like of the seismic pre-stack waveform based on deep learning, how to better generate the seismic facies diagram by using the extracted low-dimensional features is researched, and geology interpretation work is effectively helped.

Description

technical field [0001] The invention relates to a seismic facies analysis method, in particular to a clustering seismic facies analysis method based on restricted Boltzmann machine feature coding, which belongs to the technical field of data analysis. Background technique [0002] Petroleum exploration, development and production are of great significance to the development of the national economy, and the reservoir model is the theoretical basis of its research. A reasonable reservoir model can bring huge economic benefits and promote economic development. The key technology for studying reservoir models is seismic facies analysis, which uses different seismic parameters to obtain other structural information and ensures the feasibility and effectiveness of reservoir models, so seismic facies analysis technology is particularly important. [0003] The pre-stack seismic wave is the original reflection signal received by the surface angle receivers in different azimuths. For ...

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Problems solved by technology

However, due to the sparseness of well logging data relative to seismic data, well logging data can only represent local geological information, and the classification results are often poor in traditional supervised classification methods

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