A method to determine the magnetotelluric static shift from DC resistivity measurements in practice

Geophysics ◽  
2010 ◽  
Vol 75 (1) ◽  
pp. F23-F32 ◽  
Author(s):  
Simona Tripaldi ◽  
Agata Siniscalchi ◽  
Klaus Spitzer
Keyword(s):  
Electrode Spacing ◽  
Impedance Tensor ◽  
Static Shift ◽  
Data Set ◽  
Resistivity Measurements ◽  
Electromagnetic Methods ◽  
Strike Direction ◽  
Distortion Matrix ◽  
Source Electrode

Many efforts have been made to face magnetotelluric (MT) static shift. Impedance tensor analyses give insight to the presence of this feature and allow the determination of some parameters described by the MT distortion matrix. A quantitative determination of the full distortion matrix is, however, still difficult and needs additional measurements. In addition to MT, other electric and electromagnetic methods also are effected by static shift. Using direct current resistivity techniques, e.g., we can determine the static-shift factors in a simpler way because the sources can be controlled. Generally, because the distortion matrix has four entries, four additional quantities have to be determined to describe the static shift completely. They can be achieved, e.g., through measuring two orthogonal electric field components for two orthogonal source configurations. The source electrode spacing, however, has to be sufficiently large to resemble horizontal currents and match the MT plane-wave analog. The procedure at hand extracts the static-shift factors from multielectrode measurements after this condition is met. For the sake of simplicity and demonstration purposes, only inline measurements orthogonal to the strike direction of a 2D model are considered so that the vectorial problem reduces to a scalar one. This procedure is applied to a MT field data set in a regional 2D environment that shows only two additional quantities are necessary to determine the static shift.

Transient electromagnetic inversion: A remedy for magnetotelluric static shifts

Geophysics ◽  
1990 ◽  
Vol 55 (9) ◽  
pp. 1242-1250 ◽  
Author(s):  
Louise Pellerin ◽  
Gerald W. Hohmann
Keyword(s):  
Electric Field ◽  
Apparent Resistivity ◽  
Parametric Representation ◽  
Impedance Tensor ◽  
Static Shift ◽  
Electrode Arrays ◽  
Data Set ◽  
Low Frequencies ◽  
Transient Electromagnetic ◽  
Correction Scheme

Surficial bodies can severely distort magnetotelluric (MT) apparent resistivity data to arbitrarily low frequencies. This distortion, known as the MT static shift, is due to an electric field generated from boundary charges on surficial inhomogeneities, and persists throughout the entire MT recording range. Static shifts are manifested in the data as vertical, parallel shifts of log‐log apparent resistivity sounding curves, the impedance phase being unaffected. Using a three‐dimensional (3-D) numerical modeling algorithm, simulated MT data with finite length electrode arrays are generated. Significant static shifts are produced in this simulation; however, for some geometries they are impossible to identify. Techniques such as spatial averaging and electromagnetic array profiling (EMAP) are effective in removing static shifts, but they are expensive, especially for correcting a previously collected MT data set. Parametric representation and use of a single invariant quantity, such as the impedance tensor determinant, are only useful in limited circumstances and can lead the MT interpreter astray. Transient electromagnetic (TEM) sounding data are relatively inexpensive to collect, do not involve electric field measurements, and are only affected at very early times by surficial bodies. Hence, using TEM data acquired at the same location provides a natural remedy for the MT static shift. We describe a correction scheme to shift distorted MT curves to their correct values based on 1-D inversion of a TEM sounding taken at the same location as the MT site. From this estimated 1-D resistivity structure an MT sounding is computed at frequencies on the order of 1 Hz and higher. The observed MT curves are then shifted to the position of the computed curve, thus eliminating static shifts. This scheme is accurate when the overlap region between the MT and TEM sounding is 1-D, but helpful information can be gleaned even in multidimensional environments. Other advantages of this scheme are that it is straightforward to ascertain if the correction scheme is being accurately applied and it is easy to implement on a personal computer.

scholarly journals Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Jennifer Salau ◽  
Jan Henning Haas ◽  
Wolfgang Junge ◽  
Georg Thaller
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Body Parts ◽  
Nearest Neighbour ◽  
Data Set ◽  
3D Data ◽  
Holstein Friesian ◽  
Knn Classification ◽  
Friesian Cows

Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application.

Magnetotelluric static shift: Estimation and removal using the cokriging method

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. F25-F34 ◽  
Author(s):  
Benoit Tournerie ◽  
Michel Chouteau ◽  
Denis Marcotte
Keyword(s):  
Apparent Resistivity ◽  
A Priori ◽  
Shift Factor ◽  
Static Shift ◽  
Geostatistical Model ◽  
Data Set ◽  
Resistivity Sounding ◽  
The Difference ◽  
Cross Variogram

We present and test a new method to correct for the static shift affecting magnetotelluric (MT) apparent resistivity sounding curves. We use geostatistical analysis of apparent resistivity and phase data for selected periods. For each period, we first estimate and model the experimental variograms and cross variogram between phase and apparent resistivity. We then use the geostatistical model to estimate, by cokriging, the corrected apparent resistivities using the measured phases and apparent resistivities. The static shift factor is obtained as the difference between the logarithm of the corrected and measured apparent resistivities. We retain as final static shift estimates the ones for the period displaying the best correlation with the estimates at all periods. We present a 3D synthetic case study showing that the static shift is retrieved quite precisely when the static shift factors are uniformly distributed around zero. If the static shift distribution has a nonzero mean, we obtained best results when an apparent resistivity data subset can be identified a priori as unaffected by static shift and cokriging is done using only this subset. The method has been successfully tested on the synthetic COPROD-2S2 2D MT data set and on a 3D-survey data set from Las Cañadas Caldera (Tenerife, Canary Islands) severely affected by static shift.

A NEW TECHNIQUE ESTIMATING LOCATION OF MEAN JOINT CENTERS OF ROTATION WITH ASSOCIATED DISPERSIONS AND ASSESSING TO FUNCTIONAL COORDINATE SYSTEM OF MOVING LIMB SEGMENTS

2006 ◽  
Vol 06 (04) ◽  
pp. 373-384
Author(s):  
ERIC BERTHONNAUD ◽  
JOANNÈS DIMNET
Keyword(s):  
Numerical Technique ◽  
New Technique ◽  
Helical Axis ◽  
Joint Movement ◽  
Time Step ◽  
Data Set ◽  
Minimization Procedure ◽  
A New Technique ◽  
Joint Center

Joint centers are obtained from data treatment of a set of markers placed on the skin of moving limb segments. Finite helical axis (FHA) parameters are calculated between time step increments. Artifacts associated with nonrigid body movements of markers entail ill-determination of FHA parameters. Mean centers of rotation may be calculated over the whole movement, when human articulations are likened to spherical joints. They are obtained using numerical technique, defining point with minimal amplitude, during joint movement. A new technique is presented. Hip, knee, and ankle mean centers of rotation are calculated. Their locations depend on the application of two constraints. The joint center must be located next to the estimated geometric joint center. The geometric joint center may migrate inside a cube of possible location. This cube of error is located with respect to the marker coordinate systems of the two limb segments adjacent to the joint. Its position depends on the joint and the patient height, and is obtained from a stereoradiographic study with specimen. The mean position of joint center and corresponding dispersion are obtained through a minimization procedure. The location of mean joint center is compared with the position of FHA calculated between different sequential steps: time sequential step, and rotation sequential step where a minimal rotation amplitude is imposed between two joint positions. Sticks are drawn connecting adjacent mean centers. The animation of stick diagrams allows clinical users to estimate the displacements of long bones (femur and tibia) from the whole data set.

Moment-tensor solutions for the 24 November 1987 Superstition Hills, California, earthquakes

1989 ◽  
Vol 79 (2) ◽  
pp. 493-499
Author(s):  
Stuart A. Sipkin
Keyword(s):  
Main Shock ◽  
Moment Tensor ◽  
Time Dependent ◽  
Origin Time ◽  
Data Set ◽  
Filter Theory ◽  
Long Period ◽  
Seismograph Network ◽  
Scalar Moment

Abstract The teleseismic long-period waveforms recorded by the Global Digital Seismograph Network from the two largest Superstition Hills earthquakes are inverted using an algorithm based on optimal filter theory. These solutions differ slightly from those published in the Preliminary Determination of Epicenters Monthly Listing because a somewhat different, improved data set was used in the inversions and a time-dependent moment-tensor algorithm was used to investigate the complexity of the main shock. The foreshock (origin time 01:54:14.5, mb 5.7, Ms 6.2) had a scalar moment of 2.3 × 1025 dyne-cm, a depth of 8 km, and a mechanism of strike 217°, dip 79°, rake 4°. The main shock (origin time 13:15:56.4, mb 6.0, Ms 6.6) was a complex event, consisting of at least two subevents, with a combined scalar moment of 1.0 × 1026 dyne-cm, a depth of 10 km, and a mechanism of strike 303°, dip 89°, rake −180°.

P-wave backazimuth anomalies observed by a small-aperture seismic array at Pinyon Flat, southern California: Implications for structure and source location

1996 ◽  
Vol 86 (2) ◽  
pp. 470-476 ◽  
Author(s):  
Cheng-Horng Lin ◽  
S. W. Roecker
Keyword(s):  
Southern California ◽  
Seismic Array ◽  
P Wave ◽  
Small Aperture ◽  
Data Set ◽  
Single Interface ◽  
Seismic Networks ◽  
Dense Seismic Array ◽  
Beamforming Technique

Abstract Seismograms of earthquakes and explosions recorded at local, regional, and teleseismic distances by a small-aperture, dense seismic array located on Pinyon Flat, in southern California, reveal large (±15°) backazimuth anomalies. We investigate the causes and implications of these anomalies by first comparing the effectiveness of estimating backazimuth with an array using three different techniques: the broadband frequency-wavenumber (BBFK) technique, the polarization technique, and the beamforming technique. While each technique provided nearly the same direction as a most likely estimate, the beamforming estimate was associated with the smallest uncertainties. Backazimuth anomalies were then calculated for the entire data set by comparing the results from beamforming with backazimuths derived from earthquake locations reported by the Anza and Caltech seismic networks and the Preliminary Determination of Epicenters (PDE) Bulletin. These backazimuth anomalies have a simple sinelike dependence on azimuth, with the largest anomalies observed from the southeast and northwest directions. Such a trend may be explained as the effect of one or more interfaces dipping to the northeast beneath the array. A best-fit model of a single interface has a dip and strike of 20° and 315°, respectively, and a velocity contrast of 0.82 km/sec. Application of corrections computed from this simple model to ray directions significantly improves locations at all distances and directions, suggesting that this is an upper crustal feature. We confirm that knowledge of local structure can be very important for earthquake location by an array but also show that corrections computed from simple models may not only be adequate but superior to those determined by raytracing through smoothed laterally varying models.

scholarly journals Numerical Modeling of the Effects of Electrode Spacing and Multilayered Concrete Resistivity on the Apparent Resistivity Measured Using Wenner Method

2020 ◽  
Author(s):  
Karthick Thiyagarajan ◽  
Parikshit Acharya ◽  
Lasitha Piyathilaka ◽  
sarath kodagoda
Keyword(s):  
Contact Area ◽  
Apparent Resistivity ◽  
Resistivity Measurement ◽  
Gaussian Process Regression ◽  
Electrode Spacing ◽  
Electrical Resistivity Measurement ◽  
Resistivity Measurements ◽  
Electrode Contact ◽  
Concrete Resistivity ◽  
Moisture Conditions

Smart Sensing technologies can play an important role in the conditional assessment of concrete sewer pipe linings. In the long-term, the permeation of acids can deteriorate the pipe linings. Currently, there are no proven sensors available to non-invasively estimate the depth of acid permeation in real-time. The electrical resistivity measurement on the surface of the linings can indicate the sub-surface acid moisture conditions. In this study, we consider acid permeated linings as a two resistivity layer concrete sample, where the top resistivity layer is assumed to be acid permeated and the bottom resistivity layer indicates normal moisture conditions. Firstly, we modeled the sensor based on the four-probe Wenner method. The measurements of the developed model were compared with the previous studies for validation. Then, the sensor model was utilized to study the effects of electrode contact area, electrode spacing distance and two resistivity layered concrete on the apparent resistivity measurements. All the simulations were carried out by varying the thickness of top resistivity layer concrete. The simulation study indicated that the electrode contact area has very minimal effects on apparent resistivity measurements. Also, an increase in apparent resistivity measurements was observed when there is an increase in the distance of the electrode spacing. Further, a machine learning approach using Gaussian process regression modeling was formulated to estimate the depth of acid permeated layer

scholarly journals Determination of the Activation Energy of Polar Firn by D.C. Resistivity Measurements

1967 ◽  
Vol 6 (48) ◽  
pp. 911-915 ◽  
Author(s):  
M. P. Hochstein ◽  
G. F. Risk
Keyword(s):  
Activation Energy ◽  
Resistivity Measurements ◽  
Temperature Range

The activation energy ϵe1 of polar firn samples determined by D.C. resistivity measurements is a function of temperature and density. In the temperature range −2° C. to −10° C. ϵe1 decreases with decreasing temperature reaching a nearly constant value for temperatures colder than −10°C.; in the temperature range −10°C. to −21°C. ϵe1 was found to decrease with increasing density and to lie between 0.7 eV. and 0.4 eV.

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