A fast identification method based on the typical geophysical differences between submarine shallow carbonates and hydrate bearing sediments in the northern South China Se a

Bottom simulating reflector(BSR)has been recognized as one of the indicators of gas hydrates.However,BSR and hydrate are not one-to-one correspondence.In the Xisha area of South China Sea(SCS),carbonate rocks wildly develop,which continuously distribute parallel to the seafloor with high amplitude on seismic sections,exhibiting reflections similar to BSRs in the Shenhu area nearby.This phenomenon causes some interference to hydrates identification.In this paper,the authors discussed the typical geophysical differences between carbonate rocks and hydrates,indicating that the main difference exists in relationship between porosity and velocity,causing different amplitude versus offset(AVO)characters.Then the authors proposed a new model assuming that the carbonates form the matrix and the hydrate fill the pore as a part of the matrix.The key modeling parameters have been optimized constrained by Pvelocities and S-velocities simultaneously,and the model works well both for carbonate rock and gas hydrate bearing sediments.For quantitative identification,the authors calculated the velocities when carbonates and hydrates form the matrix together in different proportions.Then they proposed a carbonate and hydrate identification template(CHIT),in which the possible hydrate saturation(PHS)and possible carbonate content(PCC)can be both scaled out for a group of sample composed by P-velocity and S-velocity.If PHS is far larger than PCC,it is more likely to be a hydrate sample because carbonates and hydrates do not coexist normally.The real data application shows that the template can effectively distinguish between hydrates and carbonate rocks,consequently reducing the risk of hydrate exploration.

Fast identification of mural pigments at Mogao Grottoes using a LIBS-based spectral matching algorithm

To quickly identify the mineral pigments in the Dunhuang murals,a spectral matching algorithm(SMA)based on four methods was combined with laser-induced breakdown spectroscopy(LIBS)for the first time.The optimal range of LIBS spectrum for mineral pigments was determined using the similarity value between two different types of samples of the same pigment.A mineral pigment LIBS database was established by comparing the spectral similarities of tablets and simulated samples,and this database was successfully used to identify unknown pigments on tablet,simulated,and real mural debris samples.The results show that the SMA method coupled with the LIBS technique has great potential for identifying mineral pigments.

Temperature Prediction Model Identification Using Cyclic Coordinate Descent Based Linear Support Vector Regression

Temperature prediction plays an important role in ring die granulator control,which can influence the quantity and quality of production. Temperature prediction modeling is a complicated problem with its MIMO, nonlinear, and large time-delay characteristics. Support vector machine( SVM) has been successfully based on small data. But its accuracy is not high,in contrast,if the number of data and dimension of feature increase,the training time of model will increase dramatically. In this paper,a linear SVM was applied combing with cyclic coordinate descent( CCD) to solving big data regression. It was mathematically strictly proved and validated by simulation. Meanwhile,real data were conducted to prove the linear SVM model's effect. Compared with other methods for big data in simulation, this algorithm has apparent advantage not only in fast modeling but also in high fitness.