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1977年,Wiggins提出最小熵反褶积(MED)。这个方法依赖于定义一个能衡量信号简单性的模,这个模就是在“数据分析”中已应用多年的规范方差模。最小熵反褶积就是寻找反褶积因子使规范方差模达到最大值。因此,我们直接称这种反褶积为最大方差模反褶积,简记为MVND (Maximum Variance Norm Deconvolution的缩写)。为什么对地震记录能使用最大方差模反褶积呢?Wiggins的文章并没有分析这个问题。这个问题是很复杂的,这篇文章只是试图作一初步讨论。首先,我们给出了规范方差模的两个基本性质;其次,指出了在什么条件下能使用最大方差模反褶积。我们希望这篇文章能起到抛砖引玉的作用,能引起物探工作者的兴趣,以便进行更多的试验和更深入的研究。
In 1977, Wiggins proposed minimum entropy deconvolution (MED). This approach relies on defining a model that measures signal simplicity, a model that has been used for many years in a Data Analysis model. The minimum entropy deconvolution is to find the deconvolution factor to maximize the canonical variance model. Therefore, we directly call this deconvolution the maximum variance modular deconvolution, abbreviated as MVND (Maximum Variance Norm Deconvolution acronym). Why use maximum-variance deconvolution for seismograms? Wiggins’s article does not analyze this issue. This question is very complicated, this article is only trying to make a preliminary discussion. First of all, we give two basic properties of the normalized variance model. Secondly, we indicate the conditions under which maximal variance modular deconvolution can be used. We hope this article can play a valuable role in attracting geophysical workers so that more experiments and deeper studies can be conducted.