Geologic noise and background electromagnetic (EM) waves often degrade the quality of very low frequency electromagnetic (VLF-EM) data. To retrieve signals with significant geologic information, we used a new nonlinear decomposition technique called the empirical mode decomposition (EMD) method with the Hilbert transform. We conducted a 2D resistivity model study that included inversion of the synthetic data to test the accuracy and capabilities of this method. Next, we applied this method to real data obtained from a field experiment and a geologic example. The filtering procedure for real data starts with applying the EMD method to decompose the VLF data into a series of intrinsic mode functions that admit a well-behaved Hilbert transform. With the Hilbert transform, the intrinsic mode functions yielded a spectrogram that presents an energy-wavenumber-distance distribution of the VLF data. We then examined the decomposed data and their spectrogram to determine the noise components, which we eliminated to obtain more reliable VLF data. The EMD-filtered data and their associated spectrograms indicated the successful application of this method. Because VLF data are recorded as a complex function of the real variable distance, the in-phase and quadrature parts are complementary components of each other and could be a Hilbert transform pair if the data are analytical and noise free. Therefore, by comparing the original data set with the one obtained from the Hilbert transform, we could evaluate data quality and could even replace the original with its Hilbert transform counterpart with acceptable accuracy. By application of both this technique and conventional methods to real data in this study, we have shown the superiority of this new method and have obtained a more reliable earth model by inverting the EMD-filtered data.
We performed field measurements using the modified method of spectral ratios to estimate shallow seismic Q. Three component seismograms from artificial sources were recorded to determine [Formula: see text] and [Formula: see text] in the unconsolidated sedimentary layer at the experimental site. This modified spectral ratio method was assumed to be frequency dependent, and the amplitude ratios then were plotted against the arrival‐time difference of any two receivers for one particular frequency. The slope of the regression line in the log‐amplitude‐time space yields a Q for each frequency. Results show that Q is a function of frequency in the frequency range (below 300 Hz) we tested. A simple mathematical derivation with experimental data strongly suggests that the Q of shallow seismic waves is frequency dependent. Corrections for geometric spreading are used; however, the original and corrected Qs show no significant difference in our data, and therefore the geometric factor may be ignored in this problem. The conventional frequency‐independent spectral ratio method is easier and faster to apply, but it gives less stable results than this modified method. The unstable Q is attributed to geometric amplification effects in the conventional frequency‐independent spectral ratio method. The source factor can have an effect on the estimates of Q; however, different seismic sources give about the same Q over the dominant frequency band. We established the frequency function by assuming a simple power law regression model, where [Formula: see text] and k ≪ 1 in [Formula: see text]. This may confirm that the weathered unconsolidated layer is saturated partially, and [Formula: see text] stresses that attenuation in our study is physically a local compressional mechanism.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.