Potential field separation based on the low rank and sparse characteristics
ZHU Dan1, LIU Tianyou1, LI Hongwei2
1. Institute of Geophysics and Geomatic, China University of Geosciences(Wuhan), Wuhan, Hubei 430074, China;
2. School of Mathematics and Physic, China University of Geosciences(Wuhan), Wuhan 430074, China
Abstract：The robust principal component analysis (RPCA) extends the single objective function optimization problem for low-dimensional signal rank minimization of principal component analysis (PCA) to the two-objective function optimization problem for low-dimensional signal rank minimization with high-dimensional signal sparse.Conventional potential field separation methods such as matched filtering usually have overfitting or underfitting problems.To solve this kind of problems,this paper analyzes the low rank structures of regional field and sparse structures of local field.The RPCA is used to the potential field separation,also makes the separation more robust.Based on synthetic model tests,the proposed method is an effective and practical spatial-domain method with weight-ing parameters easy to be valued,and it can avoid errors caused by the Fourier transform.The proposed method is used to deal with data in Weiningbeishan area,Ningxia Province.Separated local low gravity anomalies and local high magnetic anomalies match very well with known underground igneous rocks,and six potential igneous rock zones are delineated.
朱丹, 刘天佑, 李宏伟. 利用数据低秩性和稀疏性的位场分离[J]. 石油地球物理勘探, 2019, 54(4): 925-936.
ZHU Dan, LIU Tianyou, LI Hongwei. Potential field separation based on the low rank and sparse characteristics. Oil Geophysical Prospecting, 2019, 54(4): 925-936.
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