Coal seam thickness prediction method and system under big data

Abstract

The invention discloses a coal seam thickness prediction method and system under big data, and belongs to the technical field of coal seam thickness prediction. The method comprises the following steps: S101, obtaining reflected wave data of an observation point; S102, obtaining reflected wave data of a prediction point; S103, collecting coal seam thickness data of the observation point, and acquiring parameter information of a target object of the observation point in a drilling mode; S104: data input: inputting observation point parameter data, observation point reflection wave data and prediction point reflection wave data into a control host of a coal seam thickness prediction system under big data; S105, calculating a coefficient; S106, calculating the thickness of a coal seam; S107:conducting data output. The coal seam thickness change can not be quantitatively explained in the three-dimensional seismic interpretation work, calculation is conducted through an operation program,operation is easy and convenient, the operation precision is high, the workload is small, the work efficiency is improved, errors are not likely to happen, and corresponding geological and technical guarantees are provided for safe, efficient and green mining of coal mines.

Description

technical field [0001] The invention relates to the technical field of coal seam thickness prediction, in particular to a coal seam thickness prediction method and system based on big data. Background technique [0002] In the past, coal seam thickness was obtained based on drilling data. However, in any mine or exploration area, the number of drilling holes is always limited. The accuracy of the coal thickness data provided is generally low, which is difficult to meet the needs of coal mine production. Especially when the thickness of the coal seam is unstable and changes greatly, the coal thickness data exposed by drilling holes seems to be a drop in the bucket. The 3D seismic data with high data density makes it possible to obtain the variation trend of coal seam thickness with high precision. Using borehole logging data for calibration in seismic interpretation will greatly improve the accuracy of seismic inversion to study the variation trend of coal seam thickness. Th...

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Problems solved by technology

Coal mine geological technology management, in order to ensure the safety of mine production and guide the safe, efficient and orderly production of coal mines, generally, all coal mining areas are required to carry out 3D seismic surveys. In their geological tasks, mines generally require Interpreting the change of coal seam thickness is also a difficult problem in 3D seismic interpretation work. Now, when calculating the coal seam thickness parameter data based on statistical principles, the calculation is often cumbersome, the workload is large, error-prone, and the calculation accuracy and work low efficiency

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