scholarly journals Magnetotelluric Signal-Noise Separation Using IE-LZC and MP

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1190
Author(s):  
Xian Zhang ◽  
Diquan Li ◽  
Jin Li ◽  
Yong Li ◽  
Jialin Wang ◽  
...  
Keyword(s):  
Apparent Resistivity ◽  
Matching Pursuit ◽  
Low Frequency ◽  
Reference Method ◽  
Test Site ◽  
Phase Curve ◽  
Characteristic Parameters ◽  
Geological Interpretation ◽  
Useful Signal ◽  
Fcm Clustering

Eliminating noise signals of the magnetotelluric (MT) method is bound to improve the quality of MT data. However, existing de-noising methods are designed for use in whole MT data sets, causing the loss of low-frequency information and severe mutation of the apparent resistivity-phase curve in low-frequency bands. In this paper, we used information entropy (IE), the Lempel–Ziv complexity (LZC), and matching pursuit (MP) to distinguish and suppress MT noise signals. Firstly, we extracted IE and LZC characteristic parameters from each segment of the MT signal in the time-series. Then, the characteristic parameters were input into the FCM clustering to automatically distinguish between the signal and noise. Next, the MP de-noising algorithm was used independently to eliminate MT signal segments that were identified as interference. Finally, the identified useful signal segments were combined with the denoised data segments to reconstruct the signal. The proposed method was validated through clustering analysis based on the signal samples collected at the Qinghai test site and the measured sites, where the results were compared to those obtained using the remote reference method and independent use of the MP method. The findings show that strong interference is purposefully removed, and the apparent resistivity-phase curve is continuous and stable. Moreover, the processed data can accurately reflect the geoelectrical information and improve the level of geological interpretation.

scholarly journals SIGNAL-NOISE IDENTIFICATION OF MAGNETOTELLURIC SIGNALS USING FRACTAL-ENTROPY AND CLUSTERING ALGORITHM FOR TARGETED DE-NOISING

Fractals ◽  
2018 ◽  
Vol 26 (02) ◽  
pp. 1840011 ◽  
Author(s):  
JIN LI ◽  
XIAN ZHANG ◽  
JINZHE GONG ◽  
JINGTIAN TANG ◽  
ZHENGYONG REN ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Low Frequency ◽  
Approximate Entropy ◽  
Phase Curve ◽  
Box Dimension ◽  
Characteristic Parameters ◽  
Signal Noise ◽  
Structure Information ◽  
Strong Interference ◽  
Noise Identification

A new technique is proposed for signal-noise identification and targeted de-noising of Magnetotelluric (MT) signals. This method is based on fractal-entropy and clustering algorithm, which automatically identifies signal sections corrupted by common interference (square, triangle and pulse waves), enabling targeted de-noising and preventing the loss of useful information in filtering. To implement the technique, four characteristic parameters — fractal box dimension (FBD), higuchi fractal dimension (HFD), fuzzy entropy (FuEn) and approximate entropy (ApEn) — are extracted from MT time-series. The fuzzy c-means (FCM) clustering technique is used to analyze the characteristic parameters and automatically distinguish signals with strong interference from the rest. The wavelet threshold (WT) de-noising method is used only to suppress the identified strong interference in selected signal sections. The technique is validated through signal samples with known interference, before being applied to a set of field measured MT/Audio Magnetotelluric (AMT) data. Compared with the conventional de-noising strategy that blindly applies the filter to the overall dataset, the proposed method can automatically identify and purposefully suppress the intermittent interference in the MT/AMT signal. The resulted apparent resistivity-phase curve is more continuous and smooth, and the slow-change trend in the low-frequency range is more precisely reserved. Moreover, the characteristic of the target-filtered MT/AMT signal is close to the essential characteristic of the natural field, and the result more accurately reflects the inherent electrical structure information of the measured site.

AUDIO MAGNETOTELLURIC SIGNAL-NOISE IDENTIFICATION AND SEPARATION BASED ON MULTIFRACTAL SPECTRUM AND MATCHING PURSUIT

Fractals ◽  
2019 ◽  
Vol 27 (01) ◽  
pp. 1940007 ◽  
Author(s):  
JIN LI ◽  
XIAN ZHANG ◽  
JINGTIAN TANG ◽  
JIN CAI ◽  
XIAOQIONG LIU
Keyword(s):  
Support Vector Machine ◽  
Time Series Data ◽  
Matching Pursuit ◽  
Low Frequency ◽  
Multifractal Spectrum ◽  
Phase Curve ◽  
Series Data ◽  
Support Vector ◽  
Signal Noise ◽  
Noise Identification

To avoid the blindness of the overall de-noising method and retain useful low frequency signals that are not over processed, we proposed a novel audio magnetotelluric (AMT) signal-noise identification and separation method based on multifractal spectrum and matching pursuit. We extracted two sets of multifractal spectrum characteristic from AMT time-series data to analyze the singularity. We used a support vector machine approach to learn the multifractal spectrum characteristics in a sample’s library and generate a model of support vector machine to distinguish between sections with and without interference in the measured AMT data. The matching pursuit algorithm was used to separate only those sections identified as having interference. Experimental results showed that the proposed method can effectively identify interference in the EMTF mathematical model and measured AMT data. Sections without interference were accurately preserved and reconstructed AMT signals were close to the natural electromagnetic field. The resulting apparent resistivity-phase curve is more continuous and smooth, and effectively improves the quality of AMT data. Moreover, the proposed method provides more reliable AMT data for subsequent electromagnetic inversion.

scholarly journals Separation of magnetotelluric signals based on refined composite multiscale dispersion entropy and orthogonal matching pursuit

2021 ◽  
Author(s):  
Xian Zhang ◽  
Jin Li ◽  
Diquan Li ◽  
Yong Li ◽  
Bei Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Multiscale Entropy ◽  
Characteristic Parameters ◽  
Signal Noise ◽  
Useful Signal ◽  
Reconstructed Signal ◽  
Noise Identification

Abstract Magnetotelluric (MT) data processing can increase the reliability of measured data. Traditional MT de-noising methods are usually filtered in entire MT time-series sequence, which result in losing of useful MT signals and the decrease of imaging accuracy of electromagnetic inversion. However, targeted MT noise separation can retain the part of data not affected by strong noise, and enhance the quality of MT data. Thus, we proposed a novel method for MT noise separation, which using refined composite multiscale dispersion entropy (RCMDE) and orthogonal matching pursuit (OMP). Firstly, the RCMDE characteristic parameters are extracted from each segment of the MT time-series. Then, the characteristic parameters are input to the fuzzy c-mean (FCM) clustering for automatic identification of MT signal and noise. Next, OMP method is utilized to remove the identified noise segments independently. Finally, the reconstructed signal consists of the denoised data segments and the identified useful signal segments. We conducted the simulation experiments and algorithm evaluation on the EMTF data, simulated data and measured sites. The results indicate that the RCMDE can improve the stability of multiscale dispersion entropy (MDE) and multiscale entropy (MSE) by analyzing the characteristics of the signal samples library, effectively dividing MT signals and noise. Compared with the existing techniques of the entire time domain de-noising and signal-noise identification, the proposed method used RCMDE and OMP as characteristic parameter and noise separation, simplified the multi-features fusion, and improved the accuracy of signal-noise identification. Moreover, the de-noising efficiency has accelerated, and the MT data quality of low-frequency band has improved greatly.

scholarly journals Separation of Magnetotelluric Signals Based on Refined Composite Multiscale Dispersion Entropy and Orthogonal Matching Pursuit

2020 ◽  
Author(s):  
Xian Zhang ◽  
Jin Li ◽  
Diquan Li ◽  
Yong Li ◽  
Bei Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Multiscale Entropy ◽  
Characteristic Parameters ◽  
Signal Noise ◽  
Useful Signal ◽  
Reconstructed Signal ◽  
Noise Identification

Abstract Magnetotelluric (MT) signal processing can increase the reliability of data. Traditional MT de-noising methods are usually filtered in entire MT time-series sequence, which result in losing of useful MT signals and the degradation of electromagnetic inversion imaging accuracy. However, targeted MT noise separation will retain the part of data that is not affected by strong noise, and enhance the quality of MT data. Thus, we proposed a novel method which using refined composite multiscale dispersion entropy (RCMDE) and orthogonal matching pursuit (OMP) for MT noise separation. Firstly, we extracted the RCMDE characteristic parameters from each segment of the MT time-series. Then, the characteristic parameters are input to the fuzzy c-mean (FCM) clustering for automatic signal-noise identification. Next, the identified noise segments were independently denoised by using OMP method. Finally, the reconstructed signal consists of the denoised data segments and the identified useful signal segments. We conducted the simulation experiments and algorithm evaluation on the EMTF data, simulated data and measured sites. The results indicate that a variety of entropy without scale factor is not stable enough, and the RCMDE can improve the stability of multiscale dispersion entropy (MDE) and multiscale entropy (MSE) by analyzing the characteristics of the signal samples library, and thus effectively dividing MT signals and noise. In this paper, the existing techniques of the entire time domain de-noising and signal-noise identification method are compared. Only for RCMDE and OMP as characteristic parameter and noise separation method, the proposed method simplified the multi-features fusion method, and improved the accuracy of signal-noise identification, accelerated de-noising efficiency, and improved the MT data quality of low-frequency band.

Fish Attraction with Pulsed Low-Frequency Sound

1968 ◽  
Vol 25 (7) ◽  
pp. 1441-1452 ◽  
Author(s):  
Joseph D. Richard
Keyword(s):  
Acoustic Stimulus ◽  
Low Frequency ◽  
Reef Fishes ◽  
Test Site ◽  
Acoustic Signals ◽  
Commercial Fisheries ◽  
Test Results ◽  
Potential Applications ◽  
Predatory Fishes ◽  
Underwater Television

A series of tests were conducted to determine the effectiveness of pulsed low-frequency acoustic signals for attracting fishes. The acoustic signals were contrived to simulate the hydrodynamically generated disturbances normally associated with active predation. Underwater television was used to observe fish arrivals during both control and test periods. Demersal predatory fishes were successfully attracted although they habituated rapidly to the acoustic stimulus. Members of the families Serranidae, Lutjanidae, and Pomadasyidae were particularly well represented among the fishes attracted. Sharks were also attracted in considerable numbers. Herbivorous reef fishes, although common around the test site, were not attracted. Possible relationships between the test results and the hearing capabilities of fishes are discussed. It is concluded that acoustic attraction techniques have potential applications in certain existing commercial fisheries.

scholarly journals Photodetector resistant to background light noise with extended dynamic range of input signals

2021 ◽  
pp. 3-8
Author(s):  
V. M. Lipka ◽  
V. V. Ryukhtin ◽  
Yu. G. Dobrovolsky
Keyword(s):  
Dynamic Range ◽  
Automatic Gain Control ◽  
Modulation Frequency ◽  
Low Frequency ◽  
Gain Control ◽  
Ambient Air ◽  
Frequency Range ◽  
Background Light ◽  
Useful Signal ◽  
Extended Dynamic

Measurement of periodic optical information signals in the background light noise with a photodetector with extended dynamic range is an urgent task of modern electronics and thus has become the aim of this study. To increase the dynamic range of the photodetector, a new version of the automatic gain control (AGC) circuit has been developed, which consists of an AGC controller, an output photodetector amplifier and an AGC detector. The authors measured the dynamic range of the photodetector when receiving optical radiation with a wavelength of 1064 nm in the power range from 2.10–8 to 2.10–5 W at a modulation frequency of 20 kHz with the AGC on. Under these conditions, the dynamic range of the photodetector was found to be up to 67 dB. If the AGC was off, the dynamic range did not exceed 30 dB. Thus, the study made it possible to create a photodetector with an extended dynamic range up to 67 dB based on a new version of the AGC circuit. The design of the photodetector allowed choosing a useful signal of a particular modulation frequency in the frequency range from 3 to 45 kHz and effectively suppresses the frequencies caused by optical interference in the low frequency range from the frequency of the input signal of constant amplitude up to 3 kHz inclusive. This compensates the current up to 15 mA, which is equivalent to the power of light interference of about 15 mW. Further research should address the issues of reliability of the proposed photodetector design and optimization of its optical system. The photodetector can be used in geodesy and ambient air quality monitoring.

Seismic effects of the MILROW and CANNIKIN nuclear explosions

1972 ◽  
Vol 62 (6) ◽  
pp. 1411-1423 ◽  
Author(s):  
E. R. Engdahl
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Test Site ◽  
High Gain ◽  
High Yield ◽  
Upper Crust ◽  
Seismic Effects ◽  
Underground Nuclear Explosions ◽  
Low Level ◽  
Nuclear Explosions

abstract Seismic effects of the underground nuclear explosions MILROW (October 1969, about 1 megaton) and CANNIKIN (November 1971, under 5 megatons) were monitored by a network of continuously recording, high-frequency, high-gain seismographs located on Amchitka and nearby islands. Each explosion was immediately followed by hundreds of small, discrete events (mB < 4), of similar focal mechanism and with a characteristic low-frequency signature, which were apparently related to the deterioration of the explosion cavity. This activity intensified, then terminated within minutes of a large, complex multiple event and concurrent formation of a surface subsided area that signaled complete collapse of the explosion cavity (MILROW, 37 hr; CANNIKIN, 38 hr). A number of small explosion-stimulated tectonic events, apparently unrelated to the collapse phenomenon, occurred intermittently for several weeks following each explosion—near the explosion cavity and up to 13 km southeast of CANNIKIN ground zero along the Island. These events were confined to the upper crust of the Island, had characteristic high-frequency signatures, and, near the Rifle Range Fault, had focal mechanisms which could be correlated with pre-existing faulting. The evidence points to a short-term interaction of the explosions with local ambient tectonic stresses. Because these stresses are of relatively low level on Amchitka, the observed seismic effects were significantly less extensive and smaller than similar effects reported from high-yield explosions at the Nevada Test Site. Continuous monitoring of the natural seismicity of the Amchitka region since 1969 has not revealed other evidence for an interaction between either MILROW or CANNIKIN and natural tectonic processes. The structural stability and apparent low level of stress in the upper crust of Amchitka suggest that the Island effectively is seismically decoupled from the active subduction zone below.

scholarly journals VLF surface-impedance measurements for ice-depth mapping — an assessment of some commonly encountered interference effects

1996 ◽  
Vol 42 (140) ◽  
pp. 33-36 ◽  
Author(s):  
David V. Thiel ◽  
Daniel James ◽  
Peter Johnson
Keyword(s):  
Surface Impedance ◽  
Apparent Resistivity ◽  
Low Frequency ◽  
Two Dimensional ◽  
Interference Effects ◽  
Impedance Measurements ◽  
Very Low Frequency ◽  
Dimensional Boundary ◽  
Lateral Variations ◽  
Made In

AbstractThe effects on very low-frequency surface-impedence measurements of lateral variations commonly found in ice environments have been measured and modelled numerically using die quasi-static two-dimensional boundary-element method. Results indicate that surface-impedance measurements made in the vicinity of crevasses oriented perpendicular to the plane Of incidence, and those made in the vicinity of moraines and melt streams, can all show significant changes to the measured apparent resistivity. It is, therefore, misleading to use such measurements in the interpretation of ice depth.

scholarly journals High-Frequency Electromagnetic Induction (HFEMI) Sensor Results from IED Constituent Parts

2019 ◽  
Vol 11 (20) ◽  
pp. 2355 ◽  
Author(s):  
Benjamin Barrowes ◽  
Mikheil Prishvin ◽  
Guy Jutras ◽  
Fridon Shubitidze
Keyword(s):  
High Frequency ◽  
Electromagnetic Induction ◽  
Low Frequency ◽  
Test Site ◽  
Frequency Range ◽  
Discrimination Capability ◽  
Metal Detectors ◽  
Metallic Parts ◽  
Ground Penetrating ◽  
Improvised Explosive

The detection and classification of subsurface improvised explosive devices (IEDs) remains one of the most pressing military and civilian problems worldwide. These IEDs are often intentionally made with either very small metallic parts or less-conducting parts in order to evade low-frequency electromagnetic induction (EMI) sensors, or metal detectors, which operate at frequencies of 50 kHz or less. Recently, high-frequency electromagnetic induction (HFEMI), which extends the established EMI frequency range above 50 kHz to 20 MHz and bridges the gap between EMI and ground-penetrating radar frequencies, has shown promising results related to detecting and identifying IEDs. In this higher frequency range, less-conductive targets display signature inphase and quadrature responses similar to higher conducting targets in the LFEMI range. IED constituent parts, such as carbon rods, small pressure plates, conductivity voids, low metal content mines, and short wires respond to HFEMI but not to traditional low-frequency EMI (LFEMI). Results from recent testing over mock-ups of less-conductive IEDs or their components show distinctive HFEMI responses, suggesting that this new sensing realm could augment the detection and discrimination capability of established EMI technology. In this paper, we present results of using the HFEMI sensor over IED-like targets at the Fort AP Hill test site. We show that results agree with numerical modeling thus providing motives to incorporate sensing at these frequencies into traditional EMI and/or GPR-based sensors.

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