this paper describes the application of approximate methods to invert airborne magnetic data as well as helicopter-borne frequency domain electromagnetic data in order to retrieve a joint model of magnetic susceptibility and electrical resistivity. the study area located in semnan province of iran consists of an arc-shaped porphyry andesite covered by sedimentary units which may have potential ...

The total least squares (TLS) techniques, also called orthogonal regression and errors-in-variables modeling, see [15, 16], have been developed independently in several areas. For a given linear (orthogonally invariant) approximation problem AX ≈ B, where A ∈ Rm×n, B ∈ Rm×d, X ∈ Rn×d, the TLS formulation aims at a solution of a modified problem (A + E)X = B + G such that min ‖[G,E]‖F . The alge...

This paper describes the application of approximate methods to invert airborne magnetic data as well as helicopter-borne frequency domain electromagnetic data in order to retrieve a joint model of magnetic susceptibility and electrical resistivity. The study area located in Semnan province of Iran consists of an arc-shaped porphyry andesite covered by sedimentary units which may have potential ...

Eigenvalue and singular value decomposition (SVD) problems are fundamental for many engineering and physics applications. For example, image processing, compression, facial recognition, vibrational analysis of mechanical structures, and computing energy levels of electrons in nanostructure materials can all be expressed as eigenvalue problems. Also, the SVD plays a very important role in statis...

A fast algorithm for solving the under-determined 3-D linear gravity inverse problem based on the randomized singular value decomposition (RSVD) is developed. The algorithm combines an iteratively reweighted approach for L1-norm regularization with the RSVD methodology in which the large scale linear system at each iteration is replaced with a much smaller linear system. Although the optimal ch...

Word embeddings have emerged as a popular approach to unsupervised learning of word relationships in machine learning and natural language processing. In this article, we benchmark two of the most popular algorithms, GloVe and word2vec, to assess their suitability for capturing medical relationships in large sources of biomedical data. Leaning on recent theoretical insights, we provide a unifie...