Nonlinear Inversion for Complex Resistivity Method Based on QPSO-BP Algorithm

The significant advantage of the complex resistivity method is to reflect the abnormal body through multi-parameters, but its inversion parameters are more than the resistivity tomography method. Therefore, how to effectively invert these spectral parameters has become the focused area of the complex resistivity inversion. An optimized BP neural network (BPNN) approach based on Quantum Particle Swarm Optimization (QPSO) algorithm was presented, which was able to improve global search ability for complex resistivity multi-parameter nonlinear inversion. In the proposed method, the nonlinear weight adjustment strategy and mutation operator were used to enhance the optimization ability of QPSO algorithm. Implementation of proposed QPSO-BPNN was given, the network had 56 hidden neurons in two hidden layers (the first hidden layer has 46 neurons and the second hidden layer has 10 neurons) and it was trained on 48 datasets and tested on another 5 synthetic datasets. The training and test results show that BP neural network optimized by the QPSO algorithm performs better than the BP neural network without initial optimization on the inversion training and test models, and the mean square error distribution is better. At the same time, a double polarized anomalous bodies model was also used to verify the feasibility and effectiveness of the proposed method, the inversion results show that the QPSO-BP algorithm inversion clearly characterizes the anomalous boundaries and is closer to the values of the parameters.

Application Analysis of Various Geophysical Methods in Volume Calculation of Different Landfills

Garbage disposal has gradually become a key issue faced by environmental governance. What must be controlled in garbage disposal is the calculation of garbage volume. In this paper, according to the different landfill conditions of a landfill in Anhui, including: covering soil, overlying HDPE membrane, overlying concrete, etc., combined with the physical differences between the rock mass and the garbage at the bottom of the landfill. The landfill covered with HDPE membrane adopts the transient electromagnetic method, the landfill covered with soil layer adopts the high-density electrical method, and the landfill covered with concrete adopts the combination of transient electromagnetic method and ground penetrating radar. At the same time, Combines the borehole data to determine that the resistivity value of the interface between the garbage and the soil was 29 Ω⋅m. Finally, the sections of multiple survey lines can accomplish three-dimensional stereoscopic that calculate the distribution and actual volume of the garbage. This method is used to determine the final investment cost.

Full-Blind Delegating Private Quantum Computation

The delegating private quantum computation(DQC)protocol with the universal quantum gate set{X,Z,H,P,R,CNOT}was firstly proposed by Broadbent et al.[Broadbent(2015)],and then Tan et al.[Tan and Zhou(2017)]tried to put forward a half-blind DQC protocol(HDQC)with another universal set{H,P,CNOT,T}.However,the decryption circuit of Toffoli gate(i.e.T)is a little redundant,and Tan et al.’s protocol[Tan and Zhou(2017)]exists the information leak.In addition,both of these two protocols just focus on the blindness of data(i.e.the client’s input and output),but do not consider the blindness of computation(i.e.the delegated quantum operation).For solving these problems,we propose a full-blind DQC protocol(FDQC)with quantum gate set{H,P,CNOT,T},where the desirable delegated quantum operation,one of{H,P,CNOT,T},is replaced by a fixed sequence(H,P,CZ,CNOT,T)to make the computation blind,and the decryption circuit of Toffoli gate is also optimized.Analysis shows that our protocol can not only correctly perform any delegated quantum computation,but also holds the characteristics of data blindness and computation blindness.