3D inversion of natural-source electromagnetic data from distributed-acquisition systems
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Profiling electromagnetic distributed acquisition systems (DAS), such as MIMDAS and Titan-24, are commonly used for near-surface exploration. Such systems allow more rapid acquisition compared to standard magnetotelluric (MT) approaches. Instead of recording horizontal electric and magnetic fields at every site, DAS acquire a single (along-profile) component of the electric field at every site, but the perpendicular component is measured at every second or so site, and the magnetic field sensors are normally positioned only at a couple of locations within the study area. It is common practice to apply standard MT inversion algorithms to invert such DAS data, despite the lack of full four component measurements at each site. This is only valid in the strictly two-dimensional (2D) case with the geoelectric strike perpendicular to the acquisition profile and for the transverse magnetic (TM) mode, and also under certain assumptions on frequency range and resistivities. In case of the 2D transverse electric (TE) mode and in the general 3D case, employing standard MT inversion software will lead to erroneous results. Therefore, we have developed a new 3D inversion algorithm that considers the correct positioning of all sensors. We investigate the ability of the new inversion to recover the subsurface resistivity and compare the results to standard MT inversion of DAS data. The newly developed inversion code is also useful for conventional MT surveys when data from some channels is lost. With the new approach missing fields can be easily substituted by fields from another site. Presentation Date: Wednesday, October 17, 2018 Start Time: 1:50:00 PM Location: 213A (Anaheim Convention Center) Presentation Type: OralKeywords:
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D-13 3-D QUASI-ANALYTICAL INVERSION OF MT DATA Abstract 1 We demonstrate in this paper that the quasi-analytical (QA) approximation developed by Zhdanov et al. (2000) can be effectively used for 3-D inversion of the array magnetotelluric (MT) data. The practical inversion of 3-D MT data in the Voisey's Bay area provides the detailed geoelectrical images of the subsurface structures. The speed of the rapid 3-D QA MT inversion makes it a useful tool for practical applications. Introduction In this paper we introduce a novel method for interpretation of array magnetotelluric (MT) data over 3-D geoelectrical structures. This method uses a
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Previous studies have used downhole resistivity logs either as a direct constraint during magnetotelluric inversion or as a qualitative validation of inversion accuracy. This study instead uses synthetic 1D magnetotelluric modelling based on downhole resistivity to better understand results of 1D, 2D and 3D inversion.One-dimensional models with representative geology for the area were generated directly from downhole resistivity data. These models were used to generate synthetic data, which was then inverted using a range of methods. Synthetic modelling made a significant contribution to selecting an appropriate inversion technique and the quality of the interpretation. Joint use of SimPEG MT1D and Occam 2D inversions proved the most effective combination to understand the geology of the project area.
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A method is described for finding a resistivity model that fits given magnetotelluric data in the one-dimensional case. The procedure is automatic and objective in that no a priori model structure is imposed. Starting with a uniform half space derived directly from the data, the procedure gradually transforms the half space to one with a continuous and smooth resistivity distribution whose response fits the measured data. The method is illustrated by application to two magnetotelluric data sets.
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It's an important way to improve the resolution of the gravity and magnetic exploration by the constrained and joint inversion between the electromagnetic and gravity data using the known seismic, geological and logging data. The joint inversion of this study is mainly based on the relationship between resistivity and density by logging data statistics. By using the nonlinear artificial fish swarm inversion algorithm and parallel design to realize the parallel joint inversion of the magnetotelluric (MT) and gravity data based on the constrained of logging and seismic data, and improve the resolution of MT and gravity data. The inversion results of the model and field data show that the proposed nonlinear constrained joint inversion has a good practicability.
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The main focus of this thesis is the application of electromagnetic (EM) methods for the exploration of geothermal resources in San Felipe's area, which it is located in northern Mexico. A regional geophysical field survey was carried out in 2014 where Magnetotellurics (MT) and Transient Electromagnetics (TEM) methods were applied. Conventional 1D inversion techniques are firstly performed to the TEM data. Afterwards, a Spatially Constrained Inversion (SCI) scheme is applied to the TEM data and thus, a pseudo-3D resistivity model of the shallow part of San Felipe's subsurface is achieved. The acquired MT data are processed with robust statistics techniques. The effect of the static shift in MT data is corrected based on the TEM information. Prior to the inversion of the MT data, 3D MT modeling studies are carried out to investigate the influence of the field survey configuration applied in San Felipe. Later on, one-dimensional inversions of MT data are carried out and the uncertainty of the inverse models is evaluated. To perform a 3D inversion of San Felipe MT data, trials are firstly performed by systematically varying the input parameters and their impact on the inversion models is appraised. The derived TEM information is incorporated into the 3D MT inversion scheme to stabilize the inversion process. On the one hand, a 3D MT constrained inversion model is achieved using the information of TEM inversion models as constraints. On the other hand, the features from the pseudo-3D resistivity model generated with the SCI of TEM data is incorporated as a priori information into the 3D inversion scheme of MT data. The second approach shows better results and therefore its output is taken as the preferred inversion model. A conductive structure is imaged in the central part of the survey area which is interpreted as a sedimentary basin at its shallow part and as a fault zone with geothermal fluids at depths greater than 1.5 km.
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AbstractMT and ZTEM data were inverted with a number of 2D and 3D algorithms to recover the subsurface conductivity structure of an area of interest. A 2D inversion algorithm was used to model the magnetotelluric TM and TE mode impedances and the ZTEM tipper data, separately. The derived conductivity-depth sections don’t show much agreement, possibly indicating the conductivity structure of the area to be highly three-dimensional.A 3D inversion algorithm was used to invert the MT and ZTEM data, separately and jointly. Overall, there is good agreement between the derived conductivity structures. This suggests that a joint inversion can extract successfully the combined subsurface conductivity information from the two data sets.Keywords2D inversion3D inversionAFMAGMTZTEM
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Abstract Currently, most of MT (magnetotelluric) data are still collected on 2D profiles. The issue that most MT researchers concern is how to invert these 2D profile data to get better results, which are more close to the real structure. Based on the analysis of 3D tensor impedance response generated from the test models and on the study of the inversion results of synthetic data and field data, we discuss the possibility of obtaining nearby 3D resistivity structure from magnetotelluric 2D profile data using 3D inversion. The results show that it is possible to interpret 2D profile data using 3D inversion method. Not only a reasonable image beneath the profile but also reasonable pictures of nearby 3D structure which can not be got by 2D inversion can be obtained using all tensor elements of the 2D profile data in the 3D inversion. The synthetic examples show that the on‐diagonal elements have special effect on recovering the distribution of 3D abnormity near the profile. Therefore, all the tensor elements are suggested to be used in 3D inversion to interpret the profile data.
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