Study on the Deconvolution Method and Processing Flow of Airgun Source Data

With its high repeatability,the airgun source has been used to monitor the temporal variations of subsurface structures. However,under different working conditions,there will be subtle differences in the airgun source signals. To some extent,deconvolution can eliminate changes of the recorded signals due to source variations. Generally speaking,in order to remove the airgun source wavelet signal and obtain the Green's functions between the airgun source and stations,we need to select an appropriate method to perform the deconvolution process for seismic waveform data. Frequency domain water level deconvolution and time domain iterative deconvolution are two kinds of deconvolution methods widely used in the field of receiver functions,etc. We use the Binchuan( in Yunnan Province,China) airgun data as an example to compare the performance of these two deconvolution methods in airgun source data processing. The results indicate that frequency domain water level deconvolution is better in terms of computational efficiency;time domain iterative deconvolution is better in terms of the signal-to-noise ratio( SNR),and the initial motion of P-wave is also clearer. We further discuss the sequence issue of deconvolution and stack for multiple-shot airgun data processing. Finally,we propose a general processing flow for the airgun source data to extract the Green 's functions between the airgun source and stations.

On the Efficiency of a CFD-Based Full Convolution Neural Network for the Post-Processing of Field Data

The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology.Assuming a problem of aircraft design as the workhorse,a regression calculation model for processing the flow data of a FCN-VGG19 aircraft is elaborated based on VGGNet(Visual Geometry Group Net)and FCN(Fully Convolutional Network)techniques.As shown by the results,the model displays a strong fitting ability,and there is almost no over-fitting in training.Moreover,the model has good accuracy and convergence.For different input data and different grids,the model basically achieves convergence,showing good performances.It is shown that the proposed simulation regression model based on FCN has great potential in typical problems of computational fluid dynamics(CFD)and related data processing.

Application of GPS technology to build a mine-subsidence observation station

We propose the design of an observation station to establish a reliable datum for displacement and deformation analysis at the first working-face subsidence observation station of Liuzhuang Mine. The design considers various geologic and mining con-ditions. Having analyzed the aims of the joint survey and the comprehensive survey, we propose design principles, and work modes, for adopting GPS technology as the position measuring technique to be used in these two stages. Baseline vectors and spatial ad-justments of the GPS network were calculated after study of data processing and quality estimation methods. A coordinate system transformation and error estimates of the transformed GPS network data are discussed. The error estimates in all stages show that the GPS control network of the observation station has sufficient accuracy and is highly efficient. The network thus provides a reli-able datum for analyzing the laws of surface displacement and deformation induced by mining.