Add/Modify Data IP (2/3) - Measurements
This same tool is used to add data to VOXI the first time, and to subsequently modify it.
Use Data > Add Data on the VOXI Manager tree to set the type of inversion and provide the data channels for the geophysical modelling.
Subsequently, to modify the data of an existing VOXI document use Data > Data Source > Modify.
Measurements dialog options
Type of system |
You can invert both Resistivity - IP and Spectral IP data. Select the appropriate system type of the database supplied in the previous dialog. |
Type of model |
The model types are a function of the IP system specified above and in the first dialog. The model type selection list is contextual and is populated dynamically .The supported model types for the Resistivity and IP system are:
You must first invert the data to obtain a Conductivity or Resistivity model in order to be able to proceed and invert for a Chargeability model. The supported model types for the Spectral IP system are:
The Complex conductivity/resistivity option inverts both the amplitude and phase models. These can also be modelled separately. |
Field data |
The field data tabs are contextual subject to the type of system and model. You will be prompted for the data necessary to invert the selected model(s). You will not be able to proceed until you have provided all the necessary information. If you are modelling:
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IP type |
This parameter is contextual and appears if above, you have selected the Model type, Chargeability. Different types of data can be used to model chargeability, all of which are mathematically interrelated. Please specify the nature of the Field data you have supplied for the inversion process.
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Mask |
Prior to modelling, the data must be QAQC’d. During the QAQC stage some data values are flagged for exclusion. Good points are flagged as 1 and bad points as dummies. This information is stored in a channel. Select this exclusion mask channel to exclude the bad data from being used in the inversion process. |
Fit error |
VOXI will attempt to fit your data until the difference between the model prediction (the fit) and the measured data is on average less than the Fit error. Increasing the Fit error focuses the inversion on features of a larger magnitude at the expense of overlooking the small variations, while decreasing the Fit Error focuses on the subtler features and enhances the structure. Four fit error methods are available. After you select the type, the dialog will adjust to prompt you for the related parameters as described below. |
Absolute error |
A single constant error value is used for all data points. By default, the Absolute error value is set to 5% of the standard deviation of the data. You can typically accept this default, which gives acceptable results in most cases. If your data is well controlled with little noise, i.e., the model should explain all the data, then you may choose to reduce the Absolute error. In this case, you may start by halving this value. On the other hand, if you find your data is very noisy and the modelling fits the noise, increase the Absolute error. |
Relative error |
The error will be a fraction of the measurement at each observation above a minimum error threshold. The default errors are populated as 5% relative error, and the minimum threshold is set to a value which is equivalent to 5% of the standard deviation of the input data. |
Data error channel |
You may also define the fit error for each data point by providing an error channel. This might be useful where you have some other reason for fitting parts of the measured data relatively less or more tightly than other parts. |
Fraction of standard deviation |
In this case, the error will be a fraction of the overall measurements, and will be defined as a Fraction of standard deviation of the data. The default fraction of standard deviation is 5% of the standard deviation. |
Application Notes
The VOXI inversion process - subject to the constraints provided - will attempt to completely explain the measurements through the physical parameters of the model.
Access Seequent Online Learning and select the VOXI guided paths to learn more about effective workflows and key concepts.
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