Polarisation Analysis: What is it? Why do you need it? How do you do it?

Hendrick, Natasha; Hearn, Steve

doi:10.1071/eg999177

Cited by 15 publications

(6 citation statements)

References 28 publications

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“…In the 4 to 4.2 Hz and 7.5 to7.7 Hz bands (Figures a and a) the signal is more varied, and polarized signals from other sources are evident. A combination of Love and Rayleigh waves can be seen originating from the direction of the wind turbine and also from a source 20 or 200° away (due to an inherent 180 ° ambiguity with the eigenvectors). This is the direction of Great Orton wind farm, 5 km north east of the wind turbine (identified in Figure later).…”

Section: Resultsmentioning

confidence: 99%

Assessing the seismic wavefield of a wind turbine using polarization analysis

Westwood

Styles

2017

Wind Energy

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Ambient seismic noise can often be seen as problematic, but with the right analysis can act as a tool to image the Earth. Wind turbines are known to generate low frequency vibrations, however, the wave types that are generated are currently unknown. Characterizing these vibrations will allow wind turbines to be used as a seismic source and be of value to geotechnical applications and seismic interferometry. This paper uses polarization analysis of the seismic wavefield around a small wind turbine to identify the type of wave being generated by the turbine and to clarify the source. The seismic data recorded 190m from the wind turbine are processed using a window length of 0.1 seconds and bandpass filtered on a selection of frequency ranges. Polarization analysis is performed for two different wind speed ranges, in order to show the variation of wave characteristics between operational and non-operational modes of the wind turbine. Polarized surface waves are identified as the predominant wave type at blade rotation harmonics, making this work particularly relevant to multichannel analysis of surface waves and seismic interferometry.

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Section: Resultsmentioning

confidence: 99%

Assessing the seismic wavefield of a wind turbine using polarization analysis

Westwood

Styles

2017

Wind Energy

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“…4 and 5) to the vertical plane containing the K-18 borehole and the zero-and far-offset source location, respectively. The used rotation procedure is based on a polarization analysis represented by particle-motion diagrams (DiSiena et al, 1984;Hendrick and Hearn, 1999). The rotation angles are determined by solving a least-square fitting problem of the samples of two corresponding components.…”

Section: Seismic Migrationmentioning

confidence: 99%

Seismic imaging in the Krafla high-temperature geothermal field, NE Iceland, using zero- and far-offset vertical seismic profiling (VSP) data

Kästner

Giese

Millett

et al. 2020

Journal of Volcanology and Geothermal Research

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Among geothermal exploration methods, active surface seismic methods have played only a minor role to date. Especially in high-temperature volcanic systems, reflection seismic data often reveal poor delineation of volcanic features, due to the internal heterogeneity of volcanic sequences. To enhance the vertical resolution, one possibility is the application of downhole seismic methods like vertical seismic profiling (VSP). A test experiment was carried out in the Krafla hightemperature geothermal field, NE-Iceland, to assess the ability of VSP to image subsurface structures, such as fractures, zones of high permeability, magmatic bodies, and zones of supercritical fluids and steam. Logging in such hostile environments is technical challenging in many aspects, but mainly due to the high temperature impact on the downhole electronic components of the measuring equipment. This requires a thorough pre-examination and implementation of the measurement, especially to avoid delays and tool failures. This paper presents results of zero-and far-offset VSP data from the K-18 borehole from within the Krafla caldera, which reveal good correlation with the surrounding lithology. The raw three component seismic data display a good signal-to-noise ratio and dominant signal frequencies between 20 and 40 Hz, down to c. 2200mdepth, for air gun and explosive sources, respectively. A zero-offset source comparison was also conducted to assess the use of different impulsive sources for future VSP surveys in similar settings. By applying a standard VSP processing, we identified stratigraphic boundaries between lavas, hyaloclastites, and intrusions, which are in good agreement with existing well data. For the zero-offset VSP, both P-and S-wave velocity models were calculated and a depth-converted corridor stack was determined. In addition, multicomponent Kirchhoff depth migration and Fresnel volume migration were tested around the borehole. The 3D results are promising, but the specific shape and lateral extent of the reflectors could not be determined due to the restriction to only two sources and the insufficient spatial coverage (aperture). Our study demonstrates that vertical seismic profiles can clearly detect variations in the subsurface volcanic stratigraphy in hightemperature geothermal fields. A more detailed reservoir characterization can be achieved by further data integration, enhanced survey design including more source positions, and improved processing and imaging techniques, such as fullwaveform inversion.

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“…Exponent p is the sensitivity factor for the rectilinearity function and is estimated empirically (Montalbetti and Kanasewich 1970;Hendrick and Hearn 1999). The main reason for using equation (3) is to enhance the coherent intervals and minimise the effect of noisy spikes.…”

Section: Polarization Filteringmentioning

confidence: 99%

An application of waveform denoising for microseismic data using polarization–linearity and time–frequency thresholding

Akram¹

2018

Geophysical Prospecting

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Noise suppression or signal‐to‐noise ratio enhancement is often desired for better processing results from a microseismic dataset. In this paper, a polarization–linearity and time–frequency‐thresholding‐based approach is used for denoising waveforms. A polarization–linearity filter is initially applied to preserve the signal intervals and suppress the noise amplitudes. This is followed by time–frequency thresholding for further signal‐to‐noise ratio enhancement in the S transform domain. The parameterisation for both polarization filter and time–frequency thresholding is also discussed. Finally, real microseismic data examples are shown to demonstrate the improvements in processing results when denoised waveforms are considered in the workflow. The results indicate that current denoising approach effectively suppresses the background noise and preserves the vector fidelity of signal waveform. Consequently, the quality of event detection, arrival‐time picking, and hypocenter location improves.

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Polarisation Analysis: What is it? Why do you need it? How do you do it?

Cited by 15 publications

References 28 publications

Assessing the seismic wavefield of a wind turbine using polarization analysis

Assessing the seismic wavefield of a wind turbine using polarization analysis

Seismic imaging in the Krafla high-temperature geothermal field, NE Iceland, using zero- and far-offset vertical seismic profiling (VSP) data

An application of waveform denoising for microseismic data using polarization–linearity and time–frequency thresholding

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