改进麻雀搜索算法的自然电位数据反演

自然电位法是一种成本低廉，野外观测操作简便的天然源地球物理勘探方法。自然电位数据反演具有病态和非线性的特征。常规反演算法分为局部优化类和全局搜索类，但基于梯度运算的局部优化算法难以求得全局最优解且反演效果依赖于初始模型的构建，而传统全局搜索算法又存在收敛速度慢，易陷入局部极值和不稳定的缺点。基于此，对一种新的全局优化策略(麻雀搜索算法)进行改进，通过混沌映射叠加反向学习策略初始化麻雀种群，再依随机概率使用Levy飞行策略更新麻雀个体位置来进一步提升算法对于解空间的探索能力和增强算法跳出局部极值的可能性。将改进前后的麻雀搜索算法分别应用于合成自然电位数据(不含与含10%、30%的随机噪声)与来自印度和法国的实测数据的反演解释中以对比检验改进算法的反演效果。理论测试结果表明：麻雀搜索算法(SSA)在无噪声干扰下的垂直圆柱和倾斜板模型数据反演误差为0.42%和0.25%，相同情况下改进麻雀搜索算法(ISSA)的反演误差为0.06%和0.07%，改进后算法拟合精度提高到3～7倍，对比目标函数收敛曲线图中ISSA的收敛速度与收敛精度都要明显优于SSA;SSA反演参数的稳定性、精度和异常响应曲线拟...

Inversion of self-potential data based on improved sparrow search algorithm

Self-potential (SP) method is a natural source geophysical prospecting technology with low cost and simple operation for field observation. Generally, the inversion of SP data has the characteristics of ill-posedness and non-linearity. The conventional inversion algorithms are classified into the types of local optimization and global search. However, it is hard for the gradient-based local optimization algorithm to obtain the global optimal solution, and the inversion results are dependent on the construction of initial model. The traditional global search algorithm has the disadvantages of low convergence rate, insufficient ability of escaping local minima and instability. Thus, a new global optimization strategy (sparrow search algorithm, SSA) was improved on this basis. Specifically, the sparrow swarm was initialized with the chaotic map superimposed reverse learning strategy, and the locations of sparrows were updated using the Levy flight strategy based on the random probability, so as to further improve the possibility of the algorithm to explore the solution space and escape the local minima in terms of the enhanced algorithm. Then, the sparrow search algorithm (SSA) and improved sparrow search algorithm (ISSA) were applied to the inversion interpretation of the synthetic SP data (without and with 10% and 30% of random noise contamination) and the field data measured from India and France, so as to comparatively verify the inversion results of the improved algorithm. As shown in the theoretical test results, the inversion errors of SSA in the vertical cylinder and inclined sheet models without noise interference are 0.42% and 0.25% respectively. In contrast, the inversion errors of ISSA are 0.06% and 0.07%, respectively. Therefore, the fitting accuracy of the improved algorithm is increased to 3-7 times of SSA. Besides, the convergence speed and accuracy of ISSA are obviously better than SSA in the convergence curve of the objective function. Moreover, the stability and accuracy of SSA inversion parameters, as well as its fitting degree to abnormal response curve, will become worse with the presence of random noise, and the erosion will become more drastic as the amplitude of the random noise increases. However, ISSA can not only maintain its fast convergence behavior, but also obtain smaller fitting errors in the abnormal response curve. Actual data inversion results further verifies that the ISSA has faster convergence speed, stronger anti-interference capability, higher precision and more robustness, which could be effectively utilized to the quantitative interpretation of SP data, and promoted to solve other geophysical inversion problems.