Sparse inversions have proven to be useful for interpreting potential‐field data because the recovered models are characterized by sharp boundaries, compact features and elevated values, compared with conventional smoothness‐based inversion results. However, several open problems remain to be addressed, including the bound dependence and staircasing problems. The former results in recovered anomalous values being close to the upper bound, while the latter leads to recovered anomalous bodies with either horizontal or vertical boundaries. We have developed a mixed L_p norm regularization strategy to address these two problems. Inversion results based on two synthetic examples confirm the validity of our approach.

中文翻译：

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研究注：使用混合Lp范数正则化在3D势场反演中恢复稀疏模型，而没有边界依赖性或固定问题

与常规的基于平滑度的反演结果相比，稀疏反演已被证明可用于解释势场数据，因为所恢复的模型具有清晰的边界，紧凑的特征和较高的值，具有特征。但是，仍然有几个未解决的问题需要解决，包括有限的依存关系和楼梯间的问题。前者导致恢复的异常值接近上限，而后者导致恢复的异常体具有水平或垂直边界。我们已经开发了混合L _p范数正则化策略来解决这两个问题。基于两个综合实例的反演结果证实了我们方法的有效性。