TY - JOUR A1 - Araya Vargas, Jaime Andrés A1 - Meqbel, Naser M. A1 - Ritter, Oliver A1 - Brasse, H. A1 - Weckmann, Ute A1 - Yanez, Gonzalo A1 - Godoy, B. T1 - Fluid Distribution in the Central Andes Subduction Zone Imaged With Magnetotellurics JF - Journal of geophysical research : Solid earth N2 - We present a model of the electrical resistivity structure of the lithosphere in the Central Andes between 20 degrees and 24 degrees S from 3-D inversion of 56 long-period magnetotelluric sites. Our model shows a complex resistivity structure with significant variability parallel and perpendicular to the trench direction. The continental forearc is characterized mainly by high electrical resistivity (>1,000m), suggesting overall low volumes of fluids. However, low resistivity zones (LRZs, <5m) were found in the continental forearc below areas where major trench-parallel faults systems intersect NW-SE transverse faults. Forearc LRZs indicate circulation and accumulation of fluids in highly permeable fault zones. The continental crust along the arc shows three distinctive resistivity domains, which coincide with segmentation in the distribution of volcanoes. The northern domain (20 degrees-20.5 degrees S) is characterized by resistivities >1,000m and the absence of active volcanism, suggesting the presence of a low-permeability block in the continental crust. The central domain (20.5 degrees-23 degrees S) exhibits a number of LRZs at varying depths, indicating different levels of a magmatic plumbing system. The southern domain (23 degrees-24 degrees S) is characterized by resistivities >1,000m, suggesting the absence of large magma reservoirs below the volcanic chain at crustal depths. Magma reservoirs located below the base of the crust or in the backarc may fed active volcanism in the southern domain. In the subcontinental mantle, the model exhibits LRZs in the forearc mantle wedge and above clusters of intermediate-depth seismicity, likely related to fluids produced by serpentinization of the mantle and eclogitization of the slab, respectively. KW - Subduction Zone KW - Central Andes KW - Magnetotellurics KW - Seismotectonic segmentation KW - Fluid processes Y1 - 2019 U6 - https://doi.org/10.1029/2018JB016933 SN - 2169-9313 SN - 2169-9356 VL - 124 IS - 4 SP - 4017 EP - 4034 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Platz, Anna A1 - Weckmann, Ute T1 - An automated new pre-selection tool for noisy Magnetotelluric data using the Mahalanobis distance and magnetic field constraints JF - Geophysical journal international N2 - In Magnetotellurics (MT) natural electromagnetic field variations are recorded to study the electrical conductivity structure of the subsurface. Thereby long time-series of electromagnetic data are subdivided into smaller segments, which are Fourier transformed and typically averaged in a statistically robust manner to obtain MT transfer functions. Unfortunately, nowadays the presence of man-made electromagnetic noise sources often deteriorates a significant fraction of the recorded time-series by overprinting the desired natural field variations. Available approaches to obtain undisturbed and high quality MT results include, for example robust statistics, remote reference or multi-station analyses which aim at the removal of outliers or uncorrelated noise. However, we have observed that intermittent noise often affects a certain time span resulting in a second cluster of transfer functions in addition to the expected true MT distribution. In this paper, we present a novel criterion for the detection and pre-selection of EM noise in form of outliers or additional clusters based on a distance measure of each data segment with regard to the centre of the data distribution. For this purpose, we utilize the Mahalanobis distance (MD) which computes the distance between two multivariate points considering the covariance matrix of the data that quantifies the shape and the size of multivariate data distributions. As the MD considers the covariance matrix, it corrects not only for different variances but also for any correlation between the data. The computation of both, the mean value and covariance matrix, is susceptible to ouliers (e.g. noise) and requires a statistically robust estimation. We tested several robust estimators, for example median absolute deviation or minimum covariance determinant algorithm and finally implemented an automatic criterion using a deterministic minimum covariance determinant algorithm. We will present results using MT data from various field experiments all over the world, which illustrate successfull data improvement. This approach is able to remove scattered data points as well as to reject complete data cluster originating from noise sources. However, like all purely statistical algorithms the criterion is limited to cases where the majority of the recorded data is well-behaved, that is noise content is below 50 per cent. If the majority of data points originates from noise sources, the new criterion will fail if used in an automatic way. In these cases, additional input by the user either manually or in an automated fashion can be utilized. We therefore suggest to use an add-on criterion to back the MD selection and subsequent robust stacking in form of a physically motivated constraint based on the magnetic incidence direction. This property indicates whether the magnetic field originates from various sources in the far field or from a strong and well defined source in the near field. KW - Magnetotellurics KW - Statistical methods KW - Time-series analysis Y1 - 2019 U6 - https://doi.org/10.1093/gji/ggz197 SN - 0956-540X SN - 1365-246X VL - 218 IS - 3 SP - 1853 EP - 1872 PB - Oxford Univ. Press CY - Oxford ER -