Method for applying seismic multiattribute parameters to predicting coal seam thickness

Abstract

The embodiment of the invention provides a method for applying seismic multiattribute parameters to predicting the coal seam thickness. The method comprises: a suitable time window is selected in a three-dimensional offset data body, seismic attribute data of amplitude, frequency, and instantaneity and the like are extracted from the time window, and a seismic attribute database is established; a correlated analysis is executed on seismic attributes and coal seam thicknesses and cross-correlation analyses are further executed on the seismic attributes, so that a plurality of seismic attributes that are most meaningful are optimized as basic parameters of a coal seam thickness prediction model; with combination of known boring data, a multicomponent polynomial regression model and a BP artificial neural network model of between all the seismic attributes and the coal seam thicknesses are established by utilizing a multicomponent polynomial regression method and a BP artificial neural network method; and the models are utilized to predict coal seam thicknesses. According to the method provided in the embodiment of the invention, because multiattribute parameters are considered, obtained calculating models are perfect and realistic; an effect for prediction of the coal seam thickness is good; and credibility and accuracy are high.

Description

technical field [0001] The invention relates to seismic data processing and interpretation technology in seismic exploration, in particular to a method for predicting coal seam thickness by using seismic multi-attribute parameters. Background technique [0002] In the field of coal seismic exploration, in addition to finding out the structure in the mining area, it is also necessary to provide the change of the thickness of the coal seam. With the development of fully mechanized mining technology, the change of coal thickness has become an urgent problem to be solved. Since most coal seams are typical thin layers, the vertical resolution cannot meet the requirements of solving coal thickness. How to use seismic information and drill hole data to accurately obtain coal seam thickness information is a topic that many scholars at home and abroad are currently studying. [0003] How to estimate the thickness of the thin layer from the reflected wave of the thin layer has always...

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Problems solved by technology

[0005] In the process of realizing the present invention, the inventor found that the prior art has at least the following disadvantages: because these methods use a single parameter and there are many factors affecting the amplitude, they cannot overcome the multiple solutions of seismic information, and the effect is not ideal

Although some use multi-attribute prediction, they only use theory and model research results to extract seismic attributes, mainly focusing on the research of oil and gas reservoir prediction and identification methods, using one or several calculation methods to predict oil and gas reservoir information, without combining the research area. In the actual attribute optimization, due to the large uncertainty in the combination of various seismic attribute information and the sensitivity of various attributes to reflect the thickness, there are large differences in the combination of seismic attributes in different regions and different horizons, so that the predictability Reduced reliability

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