正则化参数自适应选取的声学CT温度场重建

Acoustic CT temperature field reconstruction based on adaptive regularization parameter selection

声学CT温度场重建为不适定逆问题。正则化参数的选取对重建精度有重要影响。提出一种正则化参数自适应选取的温度场重建算法——ARPSM(adaptive regularization parameter selection by minimum change criterion)算法。该算法采用一种新的、称为最小变化法的正则化参数选取法,自适应地选取正则化参数,兼顾温度场细节重建和噪声抑制。模型温度场和实验室内均匀温度场的重建结果表明,与常用的L曲线法相比,最小变化法确定的正则化参数对应着更小的温度场重建误差。ARPSM算法具有较高的重建精度和较强的噪声抑制能力,可望用于仓储粮食温度分布监测等对重建质量有较高要求的应用场合。

Acoustic CT temperature field reconstruction is an ill-posed problem. The selection of regularization pa- rameter has an important influence on reconstruction accuracy. A temperature field reconstruction algorithm based on adaptive regularization parameter selection is proposed, which is called ARPSM （adaptive regularization parameter selection hy minimum change criterion） algorithm. The algorithm uses a novel adaptive regularization parameter se- lection method, named as minimum change criterion, to determine the proper regularization parameter, which can make a good compromise between de-noising and detailed reconstruction of temperature field. The reconstruction resuits of model temperature fields and a uniform temperature field in Lab show that the regularization parameters selected with the minimum change criterion are superior to those selected with often-used L-curve method and produce lower reconstruction errors. ARPSM algorithm has higher reconstruction accuracy and better anti-noise ability, thus it is expected to be used in the situations where high-quality reconstruction is needed, such as monitoring the temperature distribution of stored grain.