Research on maximum level noise contaminated of remote reference magnetotelluric measurements using synthesized data
- 1. School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010 (China)
Description
Determining magnetotelluric impedance precisely and accurately is fundamental to valid inversion and geological interpretation. This study aims to determine the minimum value of signal-to-noise ratio (SNR) which maintains the effectiveness of remote reference technique. Results of standard time series simulation, addition of different Gaussian noises to obtain the different SNR time series, and analysis of the intermediate data, such as polarization direction, correlation coefficient, and impedance tensor, show that when the SNR value is larger than 23.5743, the polarization direction disorder at morphology and a smooth and accurate sounding carve value can be obtained. At this condition, the correlation coefficient value of nearly complete segments between the base and remote station is larger than 0.9, and impedance tensor Zxy presents only one aggregation, which meet the natural magnetotelluric signal characteristic. (paper)
Availability note (English)
Available from http://dx.doi.org/10.1088/1755-1315/113/1/012016Additional details
Identifiers
Publishing Information
- Journal Title
- IOP Conference Series: Earth and Environmental Science (Online)
- Journal Volume
- 113
- Journal Issue
- 1
- Journal Page Range
- [6 p.]
- ISSN
- 1755-1315
Conference
- Title
- 3. International Conference on Advances in Energy Resources and Environment Engineering
- Dates
- 8-10 Dec 2017
- Place
- Harbin (China)
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 52109792
- Subject category
- S58: GEOSCIENCES;
- Resource subtype / Literary indicator
- Conference
- Descriptors DEI
- AGGLOMERATION; CORRELATIONS; IMPEDANCE; MORPHOLOGY; POLARIZATION; SIGNALS; SIGNAL-TO-NOISE RATIO; SIMULATION; SOUND WAVES; TENSORS
- Descriptors DEC
- DIMENSIONLESS NUMBERS