Abstract: A seismic quantitative prediction method for shale total organic carbon (TOC) based on sensitive parameter volumes is as follows. A target stratum for a TOC content to be measured is determined, logging curves with high correlations with TOC contents are analyzed, the logging curves are found as sensitive parameters; sample data are constructed using the sensitive parameters; a radial basis function (RBF) neural network is trained with the sample data as an input and the TOC content at a depth corresponding to the sample data as an output to obtain a RBF neural network prediction model; sensitive parameter volumes are obtained by using the sensitive parameters and post stack three-dimension seismic data to invert; prediction samples are constructed using the sensitive parameter volumes; the predicted samples are input to the RBF neural network prediction model to calculate corresponding TOC values, thereby the TOC content of the target stratum is predicted.

Abstract: The invention relates to a tube-in-tube LED tube, which comprises a tubular plastic lampshade, two caps connected to power supply is separately defined at the two ends of the tubular plastic lampshade, a LED lamp board is defined inside the tubular plastic lampshade sheathed with a glass tube and two ends of the glass tube is separately connected to the two caps. In case of the glass tube broken, the tubular plastic lampshade can still protect the LED lamp board from being damaged. By the way, the tube-in-tube LED tube has properties of high safety, uniform illumination, larger illumination range, simple structure, and easy to manufacture and maintenance. Also the LED lamp board is embedded into the slots in the inner wall of the tubular plastic lampshade, resulting in convenient and quickly installation, and material saving.