Joint iterative migration of electric and magnetic‐field data

2007 ◽  
Author(s):  
Michael S. Zhdanov ◽  
Takumi Ueda
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Magnetic Field Data ◽  
Electric And Magnetic Field

scholarly journals Electromagnetic field observations by the DEMETER satellite in connection with the 2009 L'Aquila earthquake

2018 ◽  
Vol 36 (5) ◽  
pp. 1483-1493 ◽  
Author(s):  
Igor Bertello ◽  
Mirko Piersanti ◽  
Maurizio Candidi ◽  
Piero Diego ◽  
Pietro Ubertini
Keyword(s):  
Magnetic Field ◽  
Seismic Activity ◽  
Field Data ◽  
Satellite Orbit ◽  
Relative Energy ◽  
Analysis Tool ◽  
Magnetic Field Data ◽  
Demeter Satellite ◽  
2009 L’Aquila Earthquake ◽  
Electric And Magnetic Field

Abstract. To define a background in the electromagnetic emissions above seismic regions, it is necessary to define the statistical distribution of the wave energy in the absence of seismic activity and any other anomalous input (e.g. solar forcing). This paper presents a completely new method to determine both the environmental and instrumental backgrounds applied to the entire DEMETER satellite electric and magnetic field data over L'Aquila. Our technique is based on a new data analysis tool called ALIF (adaptive local iterative filtering, Cicone et al., 2016; Cicone and Zhou, 2017; Piersanti et al., 2017b). To evaluate the instrumental background, we performed a multiscale statistical analysis in which the instantaneous relative energy (ϵrel), kurtosis, and Shannon entropy were calculated. To estimate the environmental background, a map, divided into 1∘×1∘ latitude–longitude cells, of the averaged relative energy (ϵrel‾), has been constructed, taking into account the geomagnetic activity conditions, the presence of seismic activity, and the local time sector of the satellite orbit. Any distinct signal different (over a certain threshold) from both the instrumental and environmental backgrounds will be considered as a case event to be investigated. Interestingly, on 4 April 2009, when DEMETER flew exactly over L'Aquila at UT = 20:29, an anomalous signal was observed at 333 Hz on both the electric and magnetic field data, whose characteristics seem to be related to pre-seismic activity.

scholarly journals Electromagnetic field observations by the DEMETER satellite in connection with the 2009 L’Aquila earthquake

2018 ◽  
Author(s):  
Igor Bertello ◽  
Mirko Piersanti ◽  
Maurizio Candidi ◽  
Piero Diego ◽  
Pietro Ubertini
Keyword(s):  
Magnetic Field ◽  
Seismic Activity ◽  
Field Data ◽  
Satellite Orbit ◽  
Relative Energy ◽  
Analysis Tool ◽  
Magnetic Field Data ◽  
Demeter Satellite ◽  
2009 L’Aquila Earthquake ◽  
Electric And Magnetic Field

Abstract. To define a background in the electromagnetic emissions above seismic regions, it is necessary to define the statistical distribution of the wave energy in absence of seismic activity and any other anomalous input (e.g. solar forcing). This paper presents a completely new method to determine both the environmental and instrumental background applied to the entire DEMETER satellite electric and magnetic field data over L’Aquila. Our technique is based on a new data analysis tool called ALIF (Adaptive Local Iterative Filtering, [Cicone et al., 2016 and 2017; Piersanti et al., 2017]). To evaluate the instrumental noise, we performed a multiscale statistical analysis, in which the instantaneous relative energy (ϵrel), kurtosis and Shannon entropy was calculated. To estimate the environmental noise, a background map, divided into 1° × 1° latitude/longitude cells, of the averaged relative energy (ϵrel) has been constructed, taking into account the geomagnetic activity conditions, the presence of seismic activity and the local time sector of the satellite orbit. Any distinct signal different (over a certain threshold) from both the instrumental and environmental backgrounds will be considered as a case event to be investigated. Interestingly, on April 4, 2009, when DEMETER flew exactly over L’Aquila at UT = 20:29, an anomalous signal was observed at 333 Hz on both the electric and magnetic field data, whose characteristics seem to be related to pre-seismic activity.

An Augmented Iteratively Re-weighted and Refined Least Squares Method for lp Minimization Problem in Inverse Modeling of Potential Field Magnetic Data

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Maysam Abedi
Keyword(s):  
Magnetic Field ◽  
Magnetic Susceptibility ◽  
Least Squares ◽  
Minimization Problem ◽  
Potential Field ◽  
Field Data ◽  
Least Squares Method ◽  
Porphyry Copper ◽  
Magnetic Data ◽  
Magnetic Field Data

The presented work examines application of an Augmented Iteratively Re-weighted and Refined Least Squares method (AIRRLS) to construct a 3D magnetic susceptibility property from potential field magnetic anomalies. This algorithm replaces an lp minimization problem by a sequence of weighted linear systems in which the retrieved magnetic susceptibility model is successively converged to an optimum solution, while the regularization parameter is the stopping iteration numbers. To avoid the natural tendency of causative magnetic sources to concentrate at shallow depth, a prior depth weighting function is incorporated in the original formulation of the objective function. The speed of lp minimization problem is increased by inserting a pre-conditioner conjugate gradient method (PCCG) to solve the central system of equation in cases of large scale magnetic field data. It is assumed that there is no remanent magnetization since this study focuses on inversion of a geological structure with low magnetic susceptibility property. The method is applied on a multi-source noise-corrupted synthetic magnetic field data to demonstrate its suitability for 3D inversion, and then is applied to a real data pertaining to a geologically plausible porphyry copper unit.  The real case study located in  Semnan province of  Iran  consists  of  an arc-shaped  porphyry  andesite  covered  by  sedimentary  units  which  may  have  potential  of  mineral  occurrences, especially  porphyry copper. It is demonstrated that such structure extends down at depth, and consequently exploratory drilling is highly recommended for acquiring more pieces of information about its potential for ore-bearing mineralization.

scholarly journals A reexamination of “interstellar ion waves” previously identified in Pioneer 10 magnetic field data

1998 ◽  
Vol 25 (19) ◽  
pp. 3721-3724 ◽  
Author(s):  
Neil Murphy ◽  
Edward J. Smith ◽  
Joyce Wolf ◽  
Devrie S. Intriligator
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Magnetic Field Data ◽  
Ion Waves ◽  
Pioneer 10

Using an induction coil sensor to indirectly measure the B-field response in the bandwidth of the transient electromagnetic method

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1489-1494 ◽  
Author(s):  
Richard S. Smith ◽  
A. Peter Annan
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Indirect Measurement ◽  
Rate Of Change ◽  
Induction Coil ◽  
Voltage Response ◽  
Magnetic Field Data ◽  
Transient Electromagnetic ◽  
Different Characteristics ◽  
The Magnetic Field

The traditional sensor used in transient electromagnetic (EM) systems is an induction coil. This sensor measures a voltage response proportional to the time rate of change of the magnetic field in the EM bandwidth. By simply integrating the digitized output voltage from the induction coil, it is possible to obtain an indirect measurement of the magnetic field in the same bandwidth. The simple integration methodology is validated by showing that there is good agreement between synthetic voltage data integrated to a magnetic field and synthetic magnetic‐field data calculated directly. Further experimental work compares induction‐coil magnetic‐field data collected along a profile with data measured using a SQUID magnetometer. These two electromagnetic profiles look similar, and a comparison of the decay curves at a critical point on the profile shows that the two types of measurements agree within the bounds of experimental error. Comparison of measured voltage and magnetic‐field data show that the two sets of profiles have quite different characteristics. The magnetic‐field data is better for identifying, discriminating, and interpreting good conductors, while suppressing the less conductive targets. An induction coil is therefore a suitable sensor for the indirect collection of EM magnetic‐field data.

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