scholarly journals Computer simulation of low-frequency electromagnetic data acquisition

1982 ◽  
Author(s):  
W.A. SanFilipo ◽  
G.W. Hohmann
Keyword(s):  
Computer Simulation ◽  
Data Acquisition ◽  
Low Frequency ◽  
Electromagnetic Data

To: “Computer simulation of low‐frequency electromagnetic data acquisition” by W. A. San Filipo and G. W. Hohmann (GEOPHYSICS, September 1983, p. 1219–1232).

Geophysics ◽  
1984 ◽  
Vol 49 (9) ◽  
pp. 1575-1575
Keyword(s):  
Computer Simulation ◽  
Data Acquisition ◽  
Time Domain ◽  
Low Frequency ◽  
Primary Field ◽  
Initial Value ◽  
Electromagnetic Data ◽  
Time Harmonic ◽  
Time Domain Response ◽  
The Time Domain

The following changes should be made to the paper, “Computer simulation of low‐frequency electromagnetic data acquisition” by W. A. San Filipo and G. W. Hohmann (Geophysics, September 1983, p. 1219–1232). The equation for the vertical magnetic induction in gammas over a conductive half‐space for a vertical time‐harmonic dipole (p. 1221) should be: [Formula: see text] The computed signals used in the examples are correct, as can be verified by the initial value (on‐time primary field) of the time‐domain response shown in Figure 15.

scholarly journals Computer simulation of low‐frequency electromagnetic data acquisition

Geophysics ◽  
1983 ◽  
Vol 48 (9) ◽  
pp. 1219-1232 ◽  
Author(s):  
William A. San Filipo ◽  
Gerald W. Hohmann
Keyword(s):  
Computer Simulation ◽  
Data Acquisition ◽  
Dynamic Range ◽  
Low Frequency ◽  
Noise Removal ◽  
Digital Data ◽  
Low Frequencies ◽  
Pass Filter ◽  
Natural Field ◽  
Field Noise

Computer simulation of low‐frequency electromagnetic (EM) digital data acquisition in the presence of natural field noise demonstrates several important limitations and considerations. Without a remote reference noise removal scheme, it is difficult to obtain an adequate ratio of signal to noise below 0.1 Hz for frequency‐domain processing and below 0.3 Hz base frequency for time‐domain processing for a typical source‐receiver configuration. A digital high‐pass filter substantially facilitates rejection of natural field noise above these frequencies; however, at lower frequencies where much longer stacking times are required, it becomes ineffective. Use of a remote reference to subtract natural field noise extends these low‐frequency limits by one decade, but the remote reference technique is limited by the resolution and dynamic range of the instrumentation. Gathering data in short segments so that natural field drift can be offset for each segment allows a higher gain setting to minimize dynamic range problems. The analysis is also applicable to the induced polarization technique in which similar problems arise at low frequencies in the presence of telluric noise.

Development of a Low Frequency Electric Field Probe Integrating Data Acquisition and Storage

2020 ◽  
Vol E103.C (8) ◽  
pp. 345-352
Author(s):  
Zhongyuan ZHOU ◽  
Mingjie SHENG ◽  
Peng LI ◽  
Peng HU ◽  
Qi ZHOU
Keyword(s):  
Electric Field ◽  
Data Acquisition ◽  
Low Frequency ◽  
Frequency Electric Field ◽  
Electric Field Probe ◽  
And Storage

Data Acquisition and Processing of Weak Low-Frequency Magnetic Signals

2011 ◽  
Vol 65 ◽  
pp. 299-302 ◽  
Author(s):  
Shou Qiang Men ◽  
Christian Resagk
Keyword(s):  
Magnetic Field ◽  
Data Acquisition ◽  
Fourier Transforms ◽  
Low Frequency ◽  
Fast Fourier Transforms ◽  
Two Dimensional ◽  
Calibration System ◽  
Magnetic Field Sensors ◽  
Data Acquisition And Processing

A simple calibration system for magnetic field sensors was designed, and experiments were carried out to calibrate two-dimensional fluxgate sensors and a sensor ring composed of eight fluxgate sensors. Fast Fourier Transforms and trapezoidal numerical integrals were applied to deal with the raw signals. It is found that it is not suitable to apply fast Fourier Transforms only to deal with signals with several peaks close to each other, but trapezoidal numerical integrals should also be used in combination with the FFT method.

scholarly journals Geomagnetic anomaly associated with Fukushima earthquake on February 13th, 2021

2021 ◽  
Vol 331 ◽  
pp. 07012
Author(s):  
Cipta Ramadhani ◽  
Bulkis Kanata ◽  
Abdullah Zainuddin ◽  
Rosmaliati ◽  
Teti Zubaidah
Keyword(s):  
Data Processing ◽  
Geomagnetic Field ◽  
Field Data ◽  
Low Frequency ◽  
Earthquake Precursors ◽  
Polarization Ratio ◽  
Magnetic Polarization ◽  
Central Frequency ◽  
Electromagnetic Data ◽  
Geomagnetic Anomaly

In this study, we performed research on electromagnetic anomalies related to earthquakes as early signs (precursors) that occurred in Fukushima, Japan on February 13th, 2021. The research focused on the utilization of geomagnetic field data which was derived from the Kakioka (KAK), Kanoya (KNY), and Memambetsu (MMB) observatories, particularly in the ultra-low frequency (ULF) to detect earthquake precursors. The method of electromagnetic data processing was conducted by applying a polarization ratio. In addition, we improved the methodology by splitting the ULF data (which ranged from 0.01-0.1 Hz) into 9 central frequencies and picking up the highest value from each central frequency to get the polarization ratio. The anomaly of magnetic polarization was identified 2-3 weeks before the mainshock in a narrowband frequency in the range of 0.04-0.05 Hz.

scholarly journals Model Calibration of Stochastic Process and Computer Experiment for MVO Analysis of Multi-Low-Frequency Electromagnetic Data

Processes ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 605
Author(s):  
Muhammad Naeim Mohd Aris ◽  
Hanita Daud ◽  
Khairul Arifin Mohd Noh ◽  
Sarat Chandra Dass
Keyword(s):  
Stochastic Process ◽  
Computer Simulation ◽  
Model Calibration ◽  
Low Frequency ◽  
Computer Experiment ◽  
Hydrocarbon Exploration ◽  
Computational Time ◽  
Low Frequencies ◽  
Uncertainty Measurement ◽  
Mathematical Equations

An electromagnetic (EM) technique is employed in seabed logging (SBL) to detect offshore hydrocarbon-saturated reservoirs. In risk analysis for hydrocarbon exploration, computer simulation for subsurface modelling is a crucial task. It can be expensive and time-consuming due to its complicated mathematical equations, and only a few realizations of input-output pairs can be generated after a very lengthy computational time. Understanding the unknown functions without any uncertainty measurement could be very challenging as well. We proposed model calibration between a stochastic process and computer experiment for magnitude versus offset (MVO) analysis. Two-dimensional (2D) Gaussian process (GP) models were developed for low-frequencies of 0.0625–0.5 Hz at different hydrocarbon depths to estimate EM responses at untried observations with less time consumption. The calculated error measurements revealed that the estimates were well-matched with the computer simulation technology (CST) outputs. Then, GP was fitted in the MVO plots to provide uncertainty quantification. Based on the confidence intervals, hydrocarbons were difficult to determine especially when their depth was 3000 m from the seabed. The normalized magnitudes for other frequencies also agreed with the resulting predictive variance. Thus, the model resolution for EM data decreases as the hydrocarbon depth increases even though multi-low frequencies were exercised in the SBL application.

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