scholarly journals Two-dimensional determinant inversion of marine magnetotelluric data and a field example from the Gulf of California, Mexico

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. E37-E57
Author(s):  
Shunguo Wang ◽  
Steven Constable ◽  
Valeria Reyes-Ortega ◽  
Hormoz Jahandari ◽  
Colin Farquharson ◽  
...  
Keyword(s):  
Gulf Of California ◽  
Subduction Zones ◽  
Field Data ◽  
Real Data ◽  
Transverse Magnetic ◽  
Two Dimensional ◽  
Magnetotelluric Data ◽  
Earth Structures ◽  
3D Effects ◽  
2D Data

Two-dimensional marine magnetotelluric (MT) observations are useful for offshore geologic studies, such as natural resource exploration, fault mapping, fluid estimation at subduction zones, and the delineation of the lithosphere-asthenosphere boundary beneath the seafloor. Earth structures are often assumed to be two dimensional, which allows MT data to be decomposed into a transverse electric (TE) mode and a transverse magnetic (TM) mode. The 2D assumption can effectively reduce acquisition and computational costs. However, offline 3D effects and other problems such as lack/failure of the compass on instruments are often encountered, making it difficult to decompose data into the TE and TM modes. In these cases, 2D inversion may be misleading or may not provide an acceptable misfit to the marine MT observations. Thus, we have developed a 2D determinant inversion to the marine MT method to mitigate these difficulties, implemented in the MARE2DEM code, and we tested its utility using synthetic examples and a field example. In the synthetic examples, the determinant inversion demonstrates an ability to overcome 3D effects caused by 3D anomalies and bathymetry. With confidence from the synthetic tests, we interpreted real data acquired in the Gulf of California, Mexico, where not only is the bathymetry 3D in nature, but the external compasses failed to record the orientation. The field data can not only be fit to a reasonable misfit with a determinant inversion, but the resolved conductive zones also have a good correlation with known faults. A comparison between the resistivity model from the field data and a seismic reflection section shows that a previously interpreted fault, the Wagner Fault, should be shifted 5 km toward the southwest and made slightly steeper. Thus, the implementation of the determinant inversion may provide a new approach for using problematic 2D data.

scholarly journals METHODOLOGY TO ESTIMATE THE MAXIMUM DEPTH OF RELIABILITY OF TWO-DIMENSIONAL GEOELECTRICAL MODELS DERIVED FROM MAGNETOTELLURIC DATA

2013 ◽  
Vol 31 (1) ◽  
pp. 97
Author(s):  
Andréa Cristina Lima dos Santos ◽  
Antônio Lopes Padilha ◽  
Ícaro Vitorello ◽  
Marcelo Banik de Pádua ◽  
Augusto César Bittencourt Pires
Keyword(s):  
Heuristic Method ◽  
Transverse Magnetic ◽  
Empirical Method ◽  
Maximum Depth ◽  
Northeastern Brazil ◽  
Perfect Conductor ◽  
Two Dimensional ◽  
Magnetotelluric Data ◽  
Starting Point ◽  
Forward Calculation

An empirical technique is proposed to estimate the maximum depth of reliability of two-dimensional (2D) geoelectrical models derived from magnetotelluric surveys conducted in regions with different conductivity. The results are then compared to those derived from a heuristic methodology well established in the literature. Experimental data from a linear profile cutting across the major structures in the SE portion of the Borborema Province, northeastern Brazil, are used. The data were collected with modern instrumentation, processed by robust techniques and modeled using inversion algorithms available nowadays to the research community of electromagnetic induccion inside the Earth. Sensitivity tests have shown that the 2D geoelectrical section is robust and accurately represents the conductivity distribution below the profile. The 2D section is used as a starting point for the empirical method employed, which consists of introducing a perfect conductor (or resistor) at different depths of the 2D model. The effect of adding this structure to the data fitting is checked through forward calculation and by comparing the RMS misfit. The results show that the maximum depth of reliability of the 2D geoelectrical model is usually given by the phase of the transverse electric (TE) mode, whereas the maximum depth of propagation of the EM signal is usually given by the phase of transverse magnetic (TM) mode. The empirical approach shows similar variations in depth when compared to the results from the heuristic method, but provides lateral variations more compatible with the diffusive process of EM wave propagation. RESUMO: Uma técnica empírica para estimar a profundidade máxima de confiabilidade de modelos geoelétricos bidimensionais (2D), obtidos a partir de sondagens magnetotelúricas realizadas em regiões de diferentes condutividades, é aqui proposta e seus resultados são comparados àqueles derivados de uma metodologia heurística já consagrada na literatura. Para tanto, são utilizados dados experimentais obtidos em um perfil linear que corta transversalmente as principais estruturas e terrenos na porção SE da Província Borborema, região Nordeste do Brasil. Os dados utilizados foram coletados com instrumentação moderna, processados por técnicas robustas e modelados por algoritmos de inversão atualmente disponíveis para a comunidade de estudos de indução eletromagnética no interior da Terra. A seção geoelétrica 2D derivada desse procedimento é robusta em relação a diferentes testes de sensibilidade e representa adequadamente a distribuição de condutividade elétrica sob o perfil, sendo aqui utilizada como ponto de partida para o método empírico empregado. A técnica empírica aqui proposta é bastante simples, baseada na introdução de um condutor (ou um resistor) perfeito em diferentes profundidades do modelo de distribuição de condutividades e verificando seu efeito no ajuste dos dados (RMS) por cálculo direto usando o programa empregado na inversão dos dados. Os resultados obtidos mostram que a profundidade limite de validade da interpretação 2D do modelo geoelétrico é geralmente dada pela fase do modo transverso elétrico (TE) de propagação do sinal eletromagnético (EM), enquanto o limite máximo de propagação desse sinal é dado pela fase do modo transverso magnético (TM). Em comparação com as profundidades de investigação obtidas pelo método heurístico, a metodologia empírica mostra comportamento semelhante nas variações de profundidade, mas fornece variações laterais mais compatíveis com o processo difusivo de propagação das ondas EM.Palavras-chave: sondagem magnetotelúrica; modelo geoelétrico bidimensional; profundidade máxima de confiabilidade do modelo 

Invariant TE and TM impedances in the marine magnetotelluric method

2020 ◽  
Vol 221 (1) ◽  
pp. 163-177 ◽  
Author(s):  
A M Montiel-Álvarez ◽  
J M Romo ◽  
S Constable ◽  
E Gómez-Treviño
Keyword(s):  
Gulf Of California ◽  
Field Data ◽  
Real Data ◽  
Quadratic Equation ◽  
Impedance Tensor ◽  
Usual Practice ◽  
Average Maximum ◽  
Magnetotelluric Method ◽  
Traditional Approaches ◽  
Rotational Invariants

SUMMARY The magnetotelluric (MT) impedance tensor has a nil diagonal when one of the axes of the coordinate system coincides with the strike of a 2-D structure. In general, real data are full tensors either because of 3-D effects or measurements not aligned to the geological strike. The usual practice to adapt the field tensor to the 2-D assumption is to rotate to a new system of coordinates. In most cases, there is no single angle of rotation that warranties that the diagonal elements become zeros as in the ideal 2-D case. Even maximizing the off-diagonal elements does not necessarily produce a nil diagonal. Consequently, the 2-D inversions proceed by neglecting whatever there is left in the diagonals. In this work, we explore an alternative that places no constraints on direction but assures a nil diagonal. We use two rotational invariants that compact the four elements of the tensor into only two and reduce in 2-D to the TE and TM impedances. These are obtained readily by solving a quadratic equation. We explore four different scenarios: (1) using the invariants, (2) rotating the tensor perpendicular to the profile, (3) rotating to the average maximum orientation for each station and (4) maximizing the off-diagonal elements of the tensor for each site, frequency to frequency. These approaches were applied to 3-D synthetic and field data. The field data correspond to two marine magnetotelluric surveys in the Gulf of California. In one of them, there is no information on the instrument orientation because the compasses failed. In this case, the rotational invariants come handy to overcome the problem. In the other survey, there was orientation information and the 2-D inversions illustrate the better performance of the invariants relative to the traditional approaches.

scholarly journals Photovoltaic Prediction Software: Evaluation with Real Data from Northern Spain

2021 ◽  
Vol 11 (11) ◽  
pp. 5025
Author(s):  
David González-Peña ◽  
Ignacio García-Ruiz ◽  
Montserrat Díez-Mediavilla ◽  
Mª. Isabel Dieste-Velasco ◽  
Cristina Alonso-Tristán
Keyword(s):  
Energy Production ◽  
Field Data ◽  
Real Data ◽  
Software Tools ◽  
Horizontal Axis ◽  
Real Field ◽  
Tracking Systems ◽  
Northern Spain ◽  
Commercial Software ◽  
Solar Tracking

Prediction of energy production is crucial for the design and installation of PV plants. In this study, five free and commercial software tools to predict photovoltaic energy production are evaluated: RETScreen, Solar Advisor Model (SAM), PVGIS, PVSyst, and PV*SOL. The evaluation involves a comparison of monthly and annually predicted data on energy supplied to the national grid with real field data collected from three real PV plants. All the systems, located in Castile and Leon (Spain), have three different tilting systems: fixed mounting, horizontal-axis tracking, and dual-axis tracking. The last 12 years of operating data, from 2008 to 2020, are used in the evaluation. Although the commercial software tools were easier to use and their installations could be described in detail, their results were not appreciably superior. In annual global terms, the results hid poor estimations throughout the year, where overestimations were compensated by underestimated results. This fact was reflected in the monthly results: the software yielded overestimates during the colder months, while the models showed better estimates during the warmer months. In most studies, the deviation was below 10% when the annual results were analyzed. The accuracy of the software was also reduced when the complexity of the dual-axis solar tracking systems replaced the fixed installation.

Two-dimensional Bayesian inversion of magnetotelluric data using trans-dimensional Gaussian processes

2021 ◽  
Author(s):  
Daniel Blatter ◽  
Anandaroop Ray ◽  
Kerry Key
Keyword(s):  
Gaussian Process ◽  
Process Model ◽  
Field Data ◽  
Probability Distributions ◽  
Bulk Composition ◽  
Computational Cost ◽  
Model Space ◽  
Bayesian Inversion ◽  
Adaptive Finite Element ◽  
Magnetotelluric Data

Summary Bayesian inversion of electromagnetic data produces crucial uncertainty information on inferred subsurface resistivity. Due to their high computational cost, however, Bayesian inverse methods have largely been restricted to computationally expedient 1D resistivity models. In this study, we successfully demonstrate, for the first time, a fully 2D, trans-dimensional Bayesian inversion of magnetotelluric data. We render this problem tractable from a computational standpoint by using a stochastic interpolation algorithm known as a Gaussian process to achieve a parsimonious parametrization of the model vis-a-vis the dense parameter grids used in numerical forward modeling codes. The Gaussian process links a trans-dimensional, parallel tempered Markov chain Monte Carlo sampler, which explores the parsimonious model space, to MARE2DEM, an adaptive finite element forward solver. MARE2DEM computes the model response using a dense parameter mesh with resistivity assigned via the Gaussian process model. We demonstrate the new trans-dimensional Gaussian process sampler by inverting both synthetic and field magnetotelluric data for 2D models of electrical resistivity, with the field data example converging within 10 days on 148 cores, a non-negligible but tractable computational cost. For a field data inversion, our algorithm achieves a parameter reduction of over 32x compared to the fixed parameter grid used for the MARE2DEM regularized inversion. Resistivity probability distributions computed from the ensemble of models produced by the inversion yield credible intervals and interquartile plots that quantitatively show the non-linear 2D uncertainty in model structure. This uncertainty could then be propagated to other physical properties that impact resistivity including bulk composition, porosity and pore-fluid content.

Discrete linear transformations of potential field data

Geophysics ◽  
1989 ◽  
Vol 54 (4) ◽  
pp. 497-507 ◽  
Author(s):  
Jorge W. D. Leão ◽  
João B. C. Silva
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Amazon Basin ◽  
Real Data ◽  
Damping Factor ◽  
Linear Transformations ◽  
Potential Field Data ◽  
Data Window ◽  
Green’S Matrix ◽  
Equivalent Layer

We present a new approach to perform any linear transformation of gridded potential field data using the equivalent‐layer principle. It is particularly efficient for processing areas with a large amount of data. An N × N data window is inverted using an M × M equivalent layer, with M greater than N so that the equivalent sources extend beyond the data window. Only the transformed field at the center of the data window is computed by premultiplying the equivalent source matrix by the row of the Green’s matrix (associated with the desired transformation) corresponding to the center of the data window. Since the inversion and the multiplication by the Green’s matrix are independent of the data, they are performed beforehand and just once for given values of N, M, and the depth of the equivalent layer. As a result, a grid operator for the desired transformation is obtained which is applied to the data by a procedure similar to discrete convolution. The application of this procedure in reducing synthetic anomalies to the pole and computing magnetization intensity maps shows that grid operators with N = 7 and M = 15 are sufficient to process large areas containing several interfering sources. The use of a damping factor allows the computation of meaningful maps even for unstable transformations in the presence of noise. Also, an equivalent layer larger than the data window takes into account part of the interfering sources so that a smaller damping factor is employed as compared with other damped inversion methods. Transformations of real data from Xingú River Basin and Amazon Basin, Brazil, demonstrate the contribution of this procedure for improvement of a preliminary geologic interpretation with minimum a priori information.

scholarly journals HiCImpute: A Bayesian Hierarchical Model for Identifying Structural Zeros and Enhancing Single Cell Hi-C Data.

2021 ◽  
Author(s):  
Qing Xie ◽  
Chengong Han ◽  
Victor Jin ◽  
Shili Lin
Keyword(s):  
Quality Improvement ◽  
Data Quality ◽  
Single Cell ◽  
Single Cells ◽  
High Sensitivity ◽  
Real Data ◽  
Cell Types ◽  
Sequencing Depth ◽  
2D Data ◽  
Structural Zeros

Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicate things further is the fact that not all zeros are created equal, as some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros), whereas others are indeed due to insufficient sequencing depth (sampling zeros), especially for loci that interact infrequently. Differentiating between structural zeros and sampling zeros is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of zeros has received little attention in the single cell Hi-C literature, where the issue of sparsity has been addressed mainly as a data quality improvement problem. To fill this gap, in this paper, we propose HiCImpute, a Bayesian hierarchy model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. HiCImpute takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available. Through an extensive set of analyses of synthetic and real data, we demonstrate the ability of HiCImpute for identifying structural zeros with high sensitivity, and for accurate imputation of dropout values in sampling zeros. Downstream analyses using data improved from HiCImpute yielded much more accurate clustering of cell types compared to using observed data or data improved by several comparison methods. Most significantly, HiCImpute-improved data has led to the identification of subtypes within each of the excitatory neuronal cells of L4 and L5 in the prefrontal cortex.

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