The electromagnetic response of an inhomogeneous layered earth—A general one‐dimensional approach

The electromagnetic response is studied for a model three‐layer earth formed by constant conductivity in the first and the third layers and conductivity varying with depth in the second layer (i.e., the inhomogeneous transition layer). A generalization to the case of many constant or variable conductivity layers is presented, too. The model problem is addressed by numerically solving an initial value problem for an ordinary differential equation for the inhomogeneous transition layer. The applicability of the procedure proposed, which is limited by the numerical method used, is discussed. As an illustration, the computed apparent resistivities and phases are compared with the results of Kao and Rankin (1980). The technique presented is applied to the computation of the response in the very‐low frequency (VLF) method. Application to other methods employing the plane wave is similar.

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Interference effects of aircraft on earth's electromagnetic response at very low frequency and low frequency

Geophysical Prospecting ◽

10.1111/1365-2478.12149 ◽

2014 ◽

Vol 63 (1) ◽

pp. 211-224

Author(s):

C. Shan ◽

L. B. Pedersen

Keyword(s):

Low Frequency ◽

Electromagnetic Response ◽

Interference Effects ◽

Very Low Frequency

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Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods

Sensors ◽

10.3390/s20041030 ◽

2020 ◽

Vol 20 (4) ◽

pp. 1030 ◽

Cited By ~ 2

Author(s):

Jiaquan Wang ◽

Qijun Huang ◽

Qiming Ma ◽

Sheng Chang ◽

Jin He ◽

...

Keyword(s):

Deep Learning ◽

Low Frequency ◽

Bipolar Pulse ◽

Excellent Performance ◽

Widespread Application ◽

Waveform Data ◽

One Dimensional ◽

Very Low Frequency ◽

Breakdown Process

Lightning waveform plays an important role in lightning observation, location, and lightning disaster investigation. Based on a large amount of lightning waveform data provided by existing real-time very low frequency/low frequency (VLF/LF) lightning waveform acquisition equipment, an automatic and accurate lightning waveform classification method becomes extremely important. With the widespread application of deep learning in image and speech recognition, it becomes possible to use deep learning to classify lightning waveforms. In this study, 50,000 lightning waveform samples were collected. The data was divided into the following categories: positive cloud ground flash, negative cloud ground flash, cloud ground flash with ionosphere reflection signal, positive narrow bipolar event, negative narrow bipolar event, positive pre-breakdown process, negative pre-breakdown process, continuous multi-pulse cloud flash, bipolar pulse, skywave. A multi-layer one-dimensional convolutional neural network (1D-CNN) was designed to automatically extract VLF/LF lightning waveform features and distinguish lightning waveforms. The model achieved an overall accuracy of 99.11% in the lightning dataset and overall accuracy of 97.55% in a thunderstorm process. Considering its excellent performance, this model could be used in lightning sensors to assist in lightning monitoring and positioning.

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The Attenuation of Very Low Frequency Brain Oscillations in Transitions from a Rest State to Active Attention

Journal of Psychophysiology ◽

10.1027/0269-8803.23.4.191 ◽

2009 ◽

Vol 23 (4) ◽

pp. 191-198 ◽

Cited By ~ 26

Author(s):

Suzannah K. Helps ◽

Samantha J. Broyd ◽

Christopher J. James ◽

Anke Karl ◽

Edmund J. S. Sonuga-Barke

Keyword(s):

Brain Activity ◽

Sampling Rate ◽

Low Frequency ◽

Future Research ◽

Adhd Symptoms ◽

Default Mode ◽

Very Low Frequency ◽

Wide Range ◽

Interference Hypothesis ◽

Mode Interference

Background: The default mode interference hypothesis ( Sonuga-Barke & Castellanos, 2007 ) predicts (1) the attenuation of very low frequency oscillations (VLFO; e.g., .05 Hz) in brain activity within the default mode network during the transition from rest to task, and (2) that failures to attenuate in this way will lead to an increased likelihood of periodic attention lapses that are synchronized to the VLFO pattern. Here, we tested these predictions using DC-EEG recordings within and outside of a previously identified network of electrode locations hypothesized to reflect DMN activity (i.e., S3 network; Helps et al., 2008 ). Method: 24 young adults (mean age 22.3 years; 8 male), sampled to include a wide range of ADHD symptoms, took part in a study of rest to task transitions. Two conditions were compared: 5 min of rest (eyes open) and a 10-min simple 2-choice RT task with a relatively high sampling rate (ISI 1 s). DC-EEG was recorded during both conditions, and the low-frequency spectrum was decomposed and measures of the power within specific bands extracted. Results: Shift from rest to task led to an attenuation of VLFO activity within the S3 network which was inversely associated with ADHD symptoms. RT during task also showed a VLFO signature. During task there was a small but significant degree of synchronization between EEG and RT in the VLFO band. Attenuators showed a lower degree of synchrony than nonattenuators. Discussion: The results provide some initial EEG-based support for the default mode interference hypothesis and suggest that failure to attenuate VLFO in the S3 network is associated with higher synchrony between low-frequency brain activity and RT fluctuations during a simple RT task. Although significant, the effects were small and future research should employ tasks with a higher sampling rate to increase the possibility of extracting robust and stable signals.

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