Reliability of Time Domain Induced Polarization Data

2011 ◽  
Author(s):  
Aurélie Gazoty ◽  
Esben Auken ◽  
Jesper Pedersen ◽  
Gianluca Fiandaca ◽  
Anders Vest Christiansen
Keyword(s):  
Time Domain ◽  
Induced Polarization ◽  
Polarization Data

Spectral induced polarization parameters as determined through time‐domain measurements

Geophysics ◽  
1984 ◽  
Vol 49 (11) ◽  
pp. 1993-2003 ◽  
Author(s):  
Ian M. Johnson
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Field Trials ◽  
Induced Polarization ◽  
Polarization Data ◽  
Forward Solution ◽  
Spectral Parameters ◽  
Impedance Model ◽  
Measurement And Analysis ◽  
The Time Domain

A method for the extraction of Cole-Cole spectral parameters from time‐domain induced polarization data is demonstrated. The instrumentation required to effect the measurement and analysis is described. The Cole-Cole impedance model is shown to work equally well in the time domain as in the frequency domain. Field trials show the time‐domain method to generate spectral parameters consistent with those generated by frequency‐domain surveys. This is shown to be possible without significant alteration to field procedures. Cole-Cole time constants of up to 100 s are shown to be resolvable given a transmitted current of a 2 s pulse‐time. The process proves to have added usefulness as the Cole-Cole forward solution proves an excellent basis for quantifying noise in the measured decay.

Automating the pre-processing of time-domain induced polarization data using machine learning

2020 ◽  
Author(s):  
Adrian S. Barfod* ◽  
Jakob Juul Larsen
Keyword(s):  
Machine Learning ◽  
Time Domain ◽  
Induced Polarization ◽  
Geophysical Data ◽  
Neural Net ◽  
Earth System ◽  
Polarization Data ◽  
The Earth ◽  
Time Modeling ◽  
Wide Range

<p>Exploring and studying the earth system is becoming increasingly important as the slow depletion of natural resources ensues. An important data source is geophysical data, collected worldwide. After gathering data, it goes through vigorous quality control, pre-processing, and inverse modelling procedures. Such procedures often have manual components, and require a trained geophysicist who understands the data, in order to translate it into useful information regarding the earth system. The sheer amounts of geophysical data collected today makes manual approaches impractical. Therefore, automating as much of the workflow related to geophysical data as possible, would allow novel opportunities such as fully automated geophysical monitoring systems, real-time modeling during data collection, larger geophysical data sets, etc.</p><p>Machine learning has been proposed as a tool for automating workflows related to geophysical data. The field of machine learning encompasses multiple tools, which can be applied in a wide range of geophysical workflows, such as pre-processing, inverse modeling, data exploration etc.</p><p>We present a study where machine learning is applied to automate the time domain induced polarization geophysical workflow. Such induced polarization data requires pre-processing, which is manual in nature. One of the pre-processing steps is that a trained geophysicist inspects the data, and removes so-called non-geologic signals, i.e. noise, which does not represent geological variance. Specifically, a real-world case from Grindsted Denmark is presented. Here, a time domain induced polarization survey was conducted containing seven profiles. Two lines were manually processed and used for supervised training of an artificial neural network. The neural net then automatically processed the remaining profiles of the survey, with satisfactory results. Afterwards, the processed data was inverted, yielding the induced polarization parameters respective to the Cole-Cole model. We discuss the limitations and optimization steps related to training such a classification network.</p>

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