Extracting Pseudo Wave Fields from Transient Electromagnetic Fields Using a Weighting Coefficients Approach

2019 ◽  
Vol 24 (3) ◽  
pp. 351-359
Author(s):  
Junjie Xue ◽  
Jiulong Cheng ◽  
Guoqiang Xue ◽  
Hai Li ◽  
Dongyang Hou ◽  
...  

The diffusive electromagnetic field can be transformed into the wave domain by means of mathematical conversion. The transformed field can then be interpreted with the tools in seismic data processing so that the identification to the underground targets can be effectively improved. However, the conversion is typically an ill-posed problem that needs to be solved using regularization tools. Based on the conventional regularization with smooth constraints in the L2 norm, the inversion result is of low resolution, while that obtained using truncated singular value decomposition (TSVD) methods is typically accurate, but has poor stability. To obtain a stable and accurate transformed electromagnetic field value, this study proposed to combine conventional regularization tools and singular value decomposition algorithms by incorporating a set of weighting coefficients. The proposed method is validated on both synthetic and observed data. The results from the proposed method are more accurate at the early time, and at the late time are more stable compared with the other methods. Furthermore, the example of field data shows that the proposed method could potentially further improve the interpretation accuracy of future mining explorations.

Author(s):  
Badr Hssina ◽  
Abdelkader Grota ◽  
Mohammed Erritali

<span>Nowadays, recommendation systems are used successfully to provide items (example: movies, music, books, news, images) tailored to user preferences. Amongst the approaches existing to recommend adequate content, we use the collaborative filtering approach of finding the information that satisfies the user by using the reviews of other users. These reviews are stored in matrices that their sizes increase exponentially to predict whether an item is relevant or not. The evaluation shows that these systems provide unsatisfactory recommendations because of what we call the cold start factor. Our objective is to apply a hybrid approach to improve the quality of our recommendation system. The benefit of this approach is the fact that it does not require a new algorithm for calculating the predictions. We are going to apply two algorithms: k-nearest neighbours (KNN) and the matrix factorization algorithm of collaborative filtering which are based on the method of (singular-value-decomposition). Our combined model has a very high precision and the experiments show that our method can achieve better results.</span>


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