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In this paper, we develop a new method of three-dimensional (3-D) inversion of multi-transmitter electromagnetic data. We apply the spectral Lanczos decomposition method (SLDM) in the framework of the localized quasi-linear inversion introduced by Zhdanov and Tartaras (2002 Geophys. 1. Int. 148 506-1 9). The SLDM makes it possible to find the regularized solution of the ill-posed inverse problem for all values of the regularization parameter a at once. As an illustration, we apply this technique for interpretatio n of the helicopter-borne electro magnetic (HEM) data over inhomogeneous geoelectrical structures, typical for mining exploration. This technique helps to accelerate HEM data inversion and provides a stable and focused image of the geoelectrical target. The new method and the corresponding computer code have been tested on synthetic data. The case history includes interpretation of HEM data collected by INCO Exploration in the Voisey's Bay area of Canada.

Low-dose computed tomography (LDCT) became a clear trend in radiology with an aspiration to refrain from delivering excessive X-ray radiation to the patients. The reduction of the radiation dose decreases the risks to the patients but raises the noise level, affecting the quality of the images and their ultimate diagnostic value. One mitigation option is to consider pairs of low-dose and high-dose CT projections to train a denoising model using deep learning algorithms; however, such pairs are rarely available in practice. In this paper, we present a new self-supervised method for CT denoising. Unlike existing self-supervised approaches, the proposed method requires only noisy CT projections and exploits the connections between adjacent images. The experiments carried out on an LDCT dataset demonstrate that our method is almost as accurate as the supervised approach, while also outperforming several modern self-supervised denoising methods.