No abstract

Relaxation time spectra (RTS) derived from time domain induced polarization data (TDIP) are helpful to assess oil reservoir pore structures. However, due to the sensitivity to the signal-to-noise ratio (SNR), the inversion accuracy of the traditional singular value decomposition (SVD) inversion method reduces with a decrease of SNR. In order to enhance the inversion accuracy and improve robustness of the inversion method to the SNR, an improved inversion method, based on damping factor and spectrum component residual correction, is proposed in this study. The numerical inversion results show that the oscillation of the RTS derived from the SVD method increased with a decrease of SNR, which makes components of the improved method, and the RTS has high inversion accuracy and robustness. Moreover, RTS derived from core sample data is basically in accord with the pore-size distribution curve, and the RTS derived from the actual induced polarization logging data is smooth and continuous, which indicates that the improved method is practicable.

Street-level landmarks are the basis of high-precision IP geolocation, and the location-error of landmark affects the accuracy of geolocation result. However, existing landmark evaluation methods cannot determine the error range of landmarks. Therefore, a street-level landmark evaluation algorithm that can estimate the upper bound of landmark error is proposed. Firstly the city of candidate landmarks are verified by IP location databases. Secondly, candidate landmarks are grouped through their last-hop routers, and then divided into several clusters by E-Apriori algorithm based on their location. Thirdly, the Land-mark reliability probability model is used to calculate the probability of the last-hop router location range. Finally the upper error bound of the landmark is determined by the position of the last-hop router. By verifying 503 reliable land-marks evaluated by the algorithm in Hong Kong, we find that the algorithm can determine the upper error bound of landmarks, and the accuracy reaches 100%. We test the algorithm based on 100 accurate landmarks and 400 unreliable landmarks, and find that our algorithm can evaluate 84 accurate landmarks and 1 invalid landmark, i.e., it achieves an accuracy of 98.8%. Finally, we take landmark evaluation experiment based on 50,000 candidate landmarks in Hong Kong and Zhengzhou respectively. The results show that geolocation errors decrease obviously using our reliable landmarks, and the mean error of 100 targets in Hong Kong is reduced from 4.18 km to 2.78 km.

No abstract

No abstract