The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform

Kaushalendra Mangal Bhatt - One of the best experts on this subject based on the ideXlab platform.

  • Microseisms and its impact on the marine‐controlled source Electromagnetic Signal
    Journal of Geophysical Research, 2014
    Co-Authors: Kaushalendra Mangal Bhatt
    Abstract:

    The marine-controlled source Electromagnetic method (mCSEM) is employed for studying the electrical characteristics and fluid contents of sedimentary reservoirs. However, the success rate of the method can be improved significantly by finding the sources of Electromagnetic noise and addressing the challenge posed by them at larger offsets where the reservoir Signal is often weak. I have studied the mCSEM data and reporting an Electromagnetic noise. The strength of the noise is observed 1600 times stronger than the seafloor mCSEM Signal at 0.1 Hz. Moreover, the noise and the transmitted mCSEM Signals are found coherent in interstation recordings. These readings suggest the severity of the noise. The source investigation presuming the observed noise as an infragravity wave failed to match the response. Then, the role of microseisms is investigated. Microseism causes oscillation of the seafloor and produces Electromagnetic disturbances by the dynamics of water. I have used various conditions for a proper discrimination of the noise as microseisms. This mechanism is clearly illustrated with the help of a conceptual diagram. The role of the directionality is part of the study, which is argued for having a significant role in the generation of microseisms. In this paper, a new algorithm is presented and is used for calculating the coherency. The algorithm helps in mapping the coherency value simultaneously in time and frequency domains.

  • microseisms and its impact on the marine controlled source Electromagnetic Signal
    Journal of Geophysical Research, 2014
    Co-Authors: Kaushalendra Mangal Bhatt
    Abstract:

    The marine-controlled source Electromagnetic method (mCSEM) is employed for studying the electrical characteristics and fluid contents of sedimentary reservoirs. However, the success rate of the method can be improved significantly by finding the sources of Electromagnetic noise and addressing the challenge posed by them at larger offsets where the reservoir Signal is often weak. I have studied the mCSEM data and reporting an Electromagnetic noise. The strength of the noise is observed 1600 times stronger than the seafloor mCSEM Signal at 0.1 Hz. Moreover, the noise and the transmitted mCSEM Signals are found coherent in interstation recordings. These readings suggest the severity of the noise. The source investigation presuming the observed noise as an infragravity wave failed to match the response. Then, the role of microseisms is investigated. Microseism causes oscillation of the seafloor and produces Electromagnetic disturbances by the dynamics of water. I have used various conditions for a proper discrimination of the noise as microseisms. This mechanism is clearly illustrated with the help of a conceptual diagram. The role of the directionality is part of the study, which is argued for having a significant role in the generation of microseisms. In this paper, a new algorithm is presented and is used for calculating the coherency. The algorithm helps in mapping the coherency value simultaneously in time and frequency domains.

Fengkui Gong - One of the best experts on this subject based on the ideXlab platform.

  • Electromagnetic Signal Classification Based on Deep Sparse Capsule Networks
    IEEE Access, 2019
    Co-Authors: Mingqian Liu, Guiyue Liao, Zhutian Yang, Hao Song, Fengkui Gong
    Abstract:

    In complex Electromagnetic environments, Electromagnetic Signal classification rates are low as long time have to be the cost to extract features. To cope with the issue, in this paper, an Electromagnetic Signal classification method is proposed based on deep sparse capsule networks. In the proposed method, received Signals are frequency reduced and sampled processing first. Subsequently, a cross ambiguity function based on linear canonical transformation, a cross ambiguity function based on linear canonical domain, and higher-order spectrum are estimated, respectively. The maximum value of each section of the cross ambiguity function is combined with the maximum value of equally spaced cross sections of higher order amplitude spectrum to obtain the two-dimensional feature information. Finally, Electromagnetic Signals are classified by the deep sparse capsule networks. The simulation results show that the proposed method not only has good classification performance but also can automatically get a hierarchical feature representation by learning. Moreover, the corresponding time cost can be effectively reduced.

Holger L Kern - One of the best experts on this subject based on the ideXlab platform.

  • using Electromagnetic Signal propagation models for radio and television broadcasts an introduction
    Political Analysis, 2018
    Co-Authors: Charles Crabtree, Holger L Kern
    Abstract:

    This note offers an introduction to Electromagnetic Signal propagation models, which can be used to model terrestrial radio and television Signal strength across space. Such data are useful to social scientists interested in identifying the effects of mass media broadcasts when (i) individual-level data on media exposure do not exist or when (ii) media exposure, while observed, is not exogenous. We illustrate the use of Electromagnetic Signal propagation models by creating a Signal strength measure of military-controlled radio stations during the 2012 coup in Mali.

Fanzeng Chen - One of the best experts on this subject based on the ideXlab platform.

  • Denoising stacked autoencoders for transient Electromagnetic Signal denoising
    Nonlinear Processes in Geophysics, 2019
    Co-Authors: Kecheng Chen, Xuben Wang, Danlei Chen, Fanzeng Chen
    Abstract:

    Abstract. The transient Electromagnetic method (TEM) is extremely important in geophysics. However, the secondary field Signal (SFS) in the TEM received by coil is easily disturbed by random noise, sensor noise and man-made noise, which results in the difficulty in detecting deep geological information. To reduce the noise interference and detect deep geological information, we apply autoencoders, which make up an unsupervised learning model in deep learning, on the basis of the analysis of the characteristics of the SFS to denoise the SFS. We introduce the SFSDSA (secondary field Signal denoising stacked autoencoders) model based on deep neural networks of feature extraction and denoising. SFSDSA maps the Signal points of the noise interference to the high-probability points with a clean Signal as reference according to the deep characteristics of the Signal, so as to realize the Signal denoising and reduce noise interference. The method is validated by the measured data comparison, and the comparison results show that the noise reduction method can (i) effectively reduce the noise of the SFS in contrast with the Kalman, principal component analysis (PCA) and wavelet transform methods and (ii) strongly support the speculation of deeper underground features.

  • A denoising stacked autoencoders for transient Electromagnetic Signal denoising
    2018
    Co-Authors: Kecheng Chen, Xuben Wang, Danlei Chen, Fanzeng Chen
    Abstract:

    <p><strong>Abstract.</strong> Transient Electromagnetic method (TEM) is extremely important in geophysics. However, the secondary field Signal(SFS) in TEM received by coil is easily disturbed by random noise, sensor noise and man-made noise, which results in the difficulty in detecting deep geological information. To reduce the noise interference and detect deep geological information, we apply autoencoders, an unsupervised learning model in deep learning, on the basis of analyzing the characteristic of SFS, to denoise SFS. We introduce SFSDSA, a Secondary Field Signal Denoising Stacked Autoencoders, based on deep neural networks of feature extraction and denoising. SFSDSA maps the Signal points of the noise interference to the high probability points with clean Signal as reference according to the deep characteristics of the Signal, so as to realize the Signal denoising and reduce noise interference. The method is validated by the measured data comparison, and the comparison results show that the noise reduction method can effectively reduce the noise of SFS, in contrast with the Kalman and wavelet transform methods, and strongly support the speculation of deeper underground features.</p>

Xiang-yang Sun - One of the best experts on this subject based on the ideXlab platform.

  • Theoretical Study on the While Drilling Electromagnetic Signal Transmission of Horizontal Well
    DEStech Transactions on Computer Science and Engineering, 2017
    Co-Authors: Ye-huo Fan, Zai-ping Nie, Xiang-yang Sun
    Abstract:

    Electromagnetic wave transmission is one kind of wireless Signal transmission of while drilling, has been a research hotspot. The previous studies mainly on vertical well, but this technology often used in horizontal well, so it is necessary for study on the Electromagnetic wave transmission from horizontal well to surface. Based on the field of the horizontal electric dipole in a conducting half-space and the equivalent transmission line theory, the theoretical model is established. Several groups of horizontal section and vertical section with different lengths are calculated. The results of calculation are analyzed.