Inversion of self-potential anomalies caused by simple polarized bodies based on particle swarm optimization

Prticle swarm optimization(PSO)is adopted to invert the self-potential anomalies of simple geometry.Taking the vertical semi-infinite cylinder model as an example,the model parameters are first inverted using standard particle swarm optimization(SPSO),and then the searching behavior of the particle swarm is discussed and the change of the particles’distribution during the iteration process is studied.The existence of different particle behaviors enables the particle swarm to explore the searching space more comprehensively,thus PSO achieves remarkable results in the inversion of SP anomalies.Finally,six improved PSOs aiming at improving the inversion accuracy and the convergence speed by changing the update of particle positions,inertia weights and learning factors are introduced for the inversion of the cylinder model,and the effectiveness of these algorithms is verified by numerical experiments.The inversion results show that these improved PSOs successfully give the model parameters which are very close to the theoretical value,and simultaneously provide guidance when determining which strategy is suitable for the inversion of the regular polarized bodies and similar geophysical problems.

A fault identification based on the parameter variation of apparent current

A new fault identification method, which is called the apparent current method, based on the parameter variation of apparent current is proposed after the analysis of the limitations of the fault interpretation method for the wide field electromagnetic data in the non-seismic exploration for oil and gas exploration. This method takes the study of the wide field electromagnetic theory and the mechanism of the fault generation, this method takes the wide field electromagnetic data as the research object, and establishes the connection between the geoelectric section and the virtual equivalent circuit, and then uses the virtual equivalent circuit as the carrier, and applies the theoretical equation of the apparent current, and combines the geological background of the study area to achieve scientific inference for location of fault in wide field electromagnetic exploration data. Theoretical model tests and the application of practical data proved that the location of underground fault can be accurately deduced by the trend of apparent current in underground space, reducing the multiple interpretations of electromagnetic data interpretation. At the same time, it also verified the correctness of the theory of apparent current and the feasibility of the method of apparent current.

利用鲸鱼优化算法的规则几何物体自然电位反演

自然电场法对地下水和地下污染相关的流动电位和氧化还原电位非常敏感,可以高效经济的实现对地下水和污染物的探测和定位。鲸鱼优化算法是一种元启发式算法,通过模拟座头鲸的捕食行为实现参数优化。将鲸鱼算法应用于规则极化几何体(即球体、水平圆柱体和垂直圆柱体)的自然电场数据反演,可以快速实现对地下目标体的精细勘探。首先,利用鲸鱼算法对球体、垂直圆柱体以及组合模型进行了参数反演测试,并对垂直柱体模型参数的优化过程进行了统计分析,讨论了鲸鱼算法的收敛性。然后,利用3组实验室观测的物理模型数据进行进一步的参数反演测试,与另外2种优化算法进行对比分析表明鲸鱼算法具有明显的优势。最后,将鲸鱼算法用于某场地实测自然电场数据的处理解释,实测数据反演结果得到了开挖验证,说明反演算法的实用性较好。反演测试还表明,基于鲸鱼优化算法的自然电场反演具有一定的抗噪声能力,在噪声条件下还能保持良好的收敛性。该方法可以实现对地下目标体快速精确反演定位,具有良好的实用性,可以广泛应用于地下水和地下污染调查。

Performance evaluation for intelligent optimization algorithms in self-potential data inversion

The self-potential method is widely used in environmental and engineering geophysics. Four intelligent optimization algorithms are adopted to design the inversion to interpret self-potential data more accurately and efficiently: simulated annealing, genetic, particle swarm optimization, and ant colony optimization. Using both noise-free and noise-added synthetic data, it is demonstrated that all four intelligent algorithms can perform self-potential data inversion effectively. During the numerical experiments, the model distribution in search space, the relative errors of model parameters, and the elapsed time are recorded to evaluate the performance of the inversion. The results indicate that all the intelligent algorithms have good precision and tolerance to noise. Particle swarm optimization has the fastest convergence during iteration because of its good balanced searching capability between global and local minimisation.

Sequential and Simultaneous Joint Inversion of Resistivity and IP Sounding Data Using Particle Swarm Optimization

In order to interpret the vertical electrical sounding data more reliably and effectively in the case of lacking proper priori information, two inverse schemes are proposed to invert combined re- sistivity and induced polarization data by using particle swarm optimization technique. Based on the computational formula of induced polarization, the inversion for chargeability/polarizability data can be transformed into inverting equivalent resistivity data. Then, the inversion for combined data can be decomposed into two procedures： inverting resistivity data and inverting equivalent resistivity data. A sequential inversion scheme is presented to run the two procedures sequentially. Contrast to the se- quential scheme, a simultaneous one is proposed to invert resistivity and induced polarization data si- multaneously. Both the sequential and simultaneous schemes are performed via centered-progressive particle swarm optimization algorithm for more exploratory purpose. Numerical experiments show that both the designed inversion algorithms can invert resistivity and induced polarization data suc- cessfully with fast convergence and high accuracy, even performed in a large search space. The inverse results are comparable to the results from generalized linear method. As an approximate importance sampler, the particle swarm optimization based algorithm can provide posterior analysis conveniently. We employ the posterior probability distributions of inverted model parameters to evaluate the per- formance and uncertainty of inversion. The posterior analysis and further field data testing show that the proposed inversion algorithms perform good sampling of the equivalence region and make sure that the global optimum can locate in the high probability areas.

Deep metallogenic mechanism in southeastern China based on receiver function data

Crustal deformation and deep metallogenic mechanisms in southeastern(SE)China are still debated.In this study,we applied the receiver function method to measure crustal thickness and Poisson’s ratio for crustal rock using teleseismic data recorded at 207 seismic stations from China Earthquake Administration Network.The results showed that crustal thickness varied from~27 km in the eastern part to~43 km in the western part of the study area,with an average crustal thickness of 31 km.The crust is thick in the west and thin in the east.The observed Poisson’s ratio for crustal rock was relatively high in the southern Cathaysia Block(CB),with an average of 0.295,while in the Qinling—Dabie terrane,it was relatively low,with an average of 0.257.In the middle of the Yangtze craton and central east of the CB,Poisson’s ratio for crustal rock varied from 0.257 to 0.286.By comparing Poisson’s ratio of the intrusive deposits with that of igneous rocks in volcanic complexes,we deduced that the metallic mineral system might be associated with orogenic and hydrothermal deposits.These results indicated that multistage magma and mineralization in the study area might be attributed to the tectonic-magma-thermal event.The high Poisson’s ratio for crustal rock in the southeastern margin of the CB and northeastern Upper Yangtze Craton might be related to Mesozoic lower crustal mafic partial melt,which provides an important environment for various magmatic intrusions and metallogenies.