Open AccessNew equipment and processing for magnetotelluric remote reference observations
Author(s) -
Ritter Oliver,
Junge Andreas,
Dawes Graham
Publication year - 1998
Publication title -
geophysical journal international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.302
H-Index - 168
eISSN - 1365-246X
pISSN - 0956-540X
DOI - 10.1046/j.1365-246x.1998.00440.x
Subject(s) - magnetotellurics , earth's magnetic field , data processing , noise (video) , computer science , filter (signal processing) , range (aeronautics) , data quality , field (mathematics) , algorithm , geophysics , remote sensing , geology , data mining , magnetic field , electrical engineering , mathematics , artificial intelligence , database , engineering , physics , metric (unit) , operations management , quantum mechanics , aerospace engineering , computer vision , pure mathematics , image (mathematics) , electrical resistivity and conductivity
Robust estimates of magnetotelluric and geomagnetic response functions are determined using the coherency and expected uniformity of the magnetic source field as quality criteria. The method is applied on data sets of three simultaneously recording sites. For the data acquisition we used a new generation of geophysical equipment (S.P.A.M. MkIII), which comprises novel concepts of parallel computing and networked, digital data transmission. The data‐processing results show that the amount of noise on the horizontal components of the magnetic field varies considerably in time, between sites and over the frequency range. The removal of such contaminated data beforehand is essential for most data‐processing schemes, as the magnetic channels are usually assumed to be free of noise. The standard remote reference method is aimed at reducing bias in response function estimates. However, this does not necessarily improve their precision as our results clearly show. With our method, on the other hand, we can filter out source field irregularities, thereby providing suitable working conditions for the robust algorithm, and eventually obtain considerably improved results. Contrary to previous concepts, we suggest rejecting as much data as feasible in order to concentrate on the remaining parts of high‐quality observations.