Huailiang Li

Multiple Two-Way Time Message Exchange (TTME) Time Synchronization for Bridge Monitoring Wireless Sensor Networks

Wireless sensor networks (WSNs) have been widely used to collect valuable information in Structural Health Monitoring (SHM) of bridges, using various sensors, such as temperature, vibration and strain sensors. Since multiple sensors are distributed on the bridge, accurate time synchronization is very important for multi-sensor data fusion and information processing. Based on shape of the bridge, a spanning tree is employed to build linear topology WSNs and achieve time synchronization in this paper. Two-way time message exchange (TTME) and maximum likelihood estimation (MLE) are employed for clock offset estimation. Multiple TTMEs are proposed to obtain a subset of TTME observations. The time out restriction and retry mechanism are employed to avoid the estimation errors that are caused by continuous clock offset and software latencies. The simulation results show that the proposed algorithm could avoid the estimation errors caused by clock drift and minimize the estimation error due to the large random variable delay jitter. The proposed algorithm is an accurate and low complexity time synchronization algorithm for bridge health monitoring.

Sorption of plutonium on geological materials associated with a Chinese radioactive waste repository: influence of pH

The aim of this study is to characterize plutonium sorption particularly 239Pu distribution coefficients and the influence of pH on sorption processes in saturated geological materials used as a radioactive waste repository in China. The examined geological media is from a southwestern Chinese repository and consists of soil and slates. The backfill material consists of Na-bentonite from Inner Mongolia Gaomiaozi, and will be used at the Beishan high-level waste (HLW) repository, Gansu Province. The results show that the sorption capacity of Pu by geological medium in an acidic environment is low, but significantly increases with increasing pH.

A New First Break Picking for Three-Component VSP Data Using Gesture Sensor and Polarization Analysis

A new first break picking for three-component (3C) vertical seismic profiling (VSP) data is proposed to improve the estimation accuracy of first arrivals, which adopts gesture detection calibration and polarization analysis based on the eigenvalue of the covariance matrix. This study aims at addressing the problem that calibration is required for VSP data using the azimuth and dip angle of geophones, due to the direction of geophones being random when applied in a borehole, which will further lead to the first break picking possibly being unreliable. Initially, a gesture-measuring module is integrated in the seismometer to rapidly obtain high-precision gesture data (including azimuth and dip angle information). Using re-rotating and re-projecting using earlier gesture data, the seismic dataset of each component will be calibrated to the direction that is consistent with the vibrator shot orientation. It will promote the reliability of the original data when making each component waveform calibrated to the same virtual reference component, and the corresponding first break will also be properly adjusted. After achieving 3C data calibration, an automatic first break picking algorithm based on the autoregressive-Akaike information criterion (AR-AIC) is adopted to evaluate the first break. Furthermore, in order to enhance the accuracy of the first break picking, the polarization attributes of 3C VSP recordings is applied to constrain the scanning segment of AR-AIC picker, which uses the maximum eigenvalue calculation of the covariance matrix. The contrast results between pre-calibration and post-calibration using field data show that it can further improve the quality of the 3C VSP waveform, which is favorable to subsequent picking. Compared to the obtained short-term average to long-term average (STA/LTA) and the AR-AIC algorithm, the proposed method, combined with polarization analysis, can significantly reduce the picking error. Applications of actual field experiments have also confirmed that the proposed method may be more suitable for the first break picking of 3C VSP. Test using synthesized 3C seismic data with low SNR indicates that the first break is picked with an error between 0.75 ms and 1.5 ms. Accordingly, the proposed method can reduce the picking error for 3C VSP data.

A RAPID SEISMIC DATA CALIBRATION TECHNIQUE USING INTEGRATED MEMS INERTIAL SENSOR GROUPS FOR A THREE-COMPONENT VSP

Calibration of 3C VSP data is an exciting challenge because the orientation of the tool is random when only seismic data is considered. In this work we augment the sensor package on the VSP tool with MEMS inertial sensors and apply a gesture measuring method to determine the tool orientation and calibration. This technique can quickly produce high-precision, orientation and angle information when integrated with the seismometer. The augmented sensor package consists of a low-cost triaxial MEMS gyroscope, an electronic compass and an accelerometer. The technique to process the gesture information is based on the OpenGL software for 3D modeling. We have tested this approach on a large number of field datasets and it appears to be faster and more reliable than other approaches.

A rapid seismic data calibration technique using integrated micro-electro-mechanical system inertial sensor groups for a 3C vertical seismic profileRapid data calibration for 3C VSP

Calibration of 3C VSP data is an exciting challenge because the orientation of the tool is random when only seismic data is considered. In this work we augment the sensor package on the VSP tool with MEMS inertial sensors and apply a gesture measuring method to determine the tool orientation and calibration. This technique can quickly produce high-precision, orientation and angle information when integrated with the seismometer. The augmented sensor package consists of a low-cost triaxial MEMS gyroscope, an electronic compass and an accelerometer. The technique to process the gesture information is based on the OpenGL software for 3D modeling. We have tested this approach on a large number of field datasets and it appears to be faster and more reliable than other approaches.

A rapid seismic data calibration technique using integrated micro-electro-mechanical system inertial sensor groups for a 3C vertical seismic profile

Calibration of 3C VSP data is an exciting challenge because the orientation of the tool is random when only seismic data is considered. In this work we augment the sensor package on the VSP tool with MEMS inertial sensors and apply a gesture measuring method to determine the tool orientation and calibration. This technique can quickly produce high-precision, orientation and angle information when integrated with the seismometer. The augmented sensor package consists of a low-cost triaxial MEMS gyroscope, an electronic compass and an accelerometer. The technique to process the gesture information is based on the OpenGL software for 3D modeling. We have tested this approach on a large number of field datasets and it appears to be faster and more reliable than other approaches.

An improved lossless group compression algorithm for seismic data in SEG-Y and MiniSEED file formats

An improved lossless group compression algorithm is proposed for decreasing the size of SEG-Y files to relieve the enormous burden associated with the transmission and storage of large amounts of seismic exploration data. Because each data point is represented by 4 bytes in SEG-Y files, the file is broken down into 4 subgroups, and the Gini coefficient is employed to analyze the distribution of the overall data and each of the 4 data subgroups within the range [0,255]. The results show that each subgroup comprises characteristic frequency distributions suited to distinct compression algorithms. Therefore, the data of each subgroup was compressed using its best suited algorithm. After comparing the compression ratios obtained for each data subgroup using different algorithms, the Lempel-Ziv-Markov chain algorithm (LZMA) was selected for the compression of the first two subgroups and the Deflate algorithm for the latter two subgroups. The compression ratios and decompression times obtained with the improved algorithm were compared with those obtained with commonly employed compression algorithms for SEG-Y files with different sizes. The experimental results show that the improved algorithm provides a compression ratio of 7580%, which is more effective than compression algorithms presently applied to SEG-Y files. In addition, the proposed algorithm is applied to the miniSEED format used in natural earthquake monitoring, and the results compared with those obtained using the Steim2 compression algorithm, the results again show that the proposed algorithm provides better data compression. An algorithm was proposed to compress losslessly the SEG-Y and miniSEED Files.It describes the distribution of SEG-Y and miniSEED files using Gini coefficient.Different data sub-groups should use different compression algorithms.The improved algorithm can achieve a better compression ratio of 7580%.It provides a better compression ratio than the Steim2 to miniSEED Files.

Fast adaptive particle spectrum fitting algorithm based on moment-estimated initial parameters

An algorithm based on moment estimation is presented to determine the initial parameters of the particle spectrum peak shape function for the iteration fitting procedure. The algorithm calculates the mean value, variance, and third-order central moment by using the spectrum peak data, solves the parameters of the fitting function, and then provides them as the initial values to the Levenberg-Marquardt algorithm to ensure convergence and optimized fitting. The effectiveness of the proposed algorithm was tested by gamma and alpha spectra. The algorithm can be used in automated peak curve fitting and spectral analysis.

A Deconvolution Algorithm for Gamma Spectrum Based on Energy Resolution Calibration

A deconvolution algorithm with response function based on energy resolution calibration was presented. A 152Eu gamma spectrum was detected by a LaBr3(Ce) scintillator. The energy resolution calibration was best fitted by a square root of a quadratic function, based on which the detector response matrix was constructed connected with the energy, and the boosted Gold deconvolution algorithm was applied. The deconvolution results are better than the fixed response function with a constant standard deviation.