Published February 1, 2018 | Version v1
Journal article

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/10.1088/1755-1315/113/1/012016

Additional details

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