# Data Set Descriptions

This section provides a brief description of the data sets available on the ATS GeoSuite App.

# Data quality assessment data sets

The data is based on the best correlation index achievable with a minimum of 5 stack sets per survey point. If the value is below 50% that point should be considered with caution when used as a recommended drilling location.

## Data quality correlation index and site noise description

### Strikes Detected

This describes the number of individual seismic source impacts at each sounding location that was detected by the software based trigger algorithm when the data is imported into the software.

### Strikes Rejected

The correlation algorithms identify seismic source impacts which do not correlate well with the group stack set and reject them. The data describe as the total number of rejected seismic source impact data sets including the false trigger data and the non-correlating sets.

### Correlating Strikes Used

An adaptive correlation algorithm is utilized to determine the best possible stack data set based on a minimum of 50% utilization of the detected seismic source impact events, not including the false data.

### Correlation Index

These correlating data sets are evaluated for an inter correlation index to describe the overall quality of the stack data set at a specific location. This correlation index is used to determine the reliability index of the data at a specific sounding location within the model. This helps evaluate if the point data should be considered for exploration or re-evaluation.

### Pre-Filter Fundamental frequency Level

The fundamental frequency of any electrical noise source is described as the lowest frequency of a periodic wave form. In most cases, this periodic noise is generated by induced currents from power systems, such as, power lines and transformers etc.

This data is produced by the frequency analysis of the first second of data recorded on an ES sounding data recording. This data is assumed to be void of any ES data and is merely represented of the background electrical noise around the sounding location. The fundamental frequency is automatically detected by software algorithms to be either 50 or 60Hz. Once the fundamental electrical power noise frequency is determined, the noise level of the fundamental frequency is calculated. The value is displayed as a root mean square voltage level that is representative of the average fundamental frequency noise content present in the first second of the ES recording. This data classifies which points in an ES project suffer from high electrical noise interference.

### Post-Filter Fundamental frequency Level

The fundamental frequency of any electrical noise source is described as the lowest frequency of a periodic wave form. In most cases, this periodic noise is generated by induced currents from power systems, such as, power lines and transformers etc.

This data is produced by the frequency analysis of the first second of data recorded on an ES sounding data recording after a digital notch filter, set at the fundamental frequency, is applied to the full data set. This first second of data is assumed to be void of any ES data and is merely represented of the filtered background electrical noise around the sounding location. Once the fundamental electrical power noise frequency is filtered out, the resulting noise level of the fundamental frequency is calculated. The value is displayed as a root mean square voltage level that is representative of the average filtered fundamental frequency noise content present in the first second of the ES recording. This data shows the resultant filtered fundamental frequency level at each ES sounding location and effectively quantifies the suppression of fundamental frequency noise produced by filtering.

### Pre-Filter Odd Harmonic frequency Level

The harmonic of a periodic wave form is described as a frequency at an integer multiple of the fundamental frequency. As such, they are all periodic at the fundamental frequency and so are their sums. Odd harmonic frequencies are the odd integer multiples of a fundamental frequency. If a fundamental frequency (f) is 50Hz then the first odd harmonic will be (3f) or 150Hz and the second odd harmonic will be (5f) or 250Hz and so on. The same is true of even harmonic frequencies where the first even harmonic is (2f) or 100Hz and the second even harmonic is (4f) or 200Hz. These harmonic frequencies can extend to high frequencies but usually decrease in amplitude as frequency increases. In most cases, this periodic noise is generated by induced currents from power systems, such as, power lines and transformers etc.

This data is produced by the frequency analysis of the first second of data recorded on an ES sounding data recording. This data is assumed to be void of any ES data and is merely represented of the background electrical noise around the sounding location. The odd harmonic frequencies are calculated by the odd integer multiplication of the detected fundamental frequency up to a maximum frequency of 1000Hz. The average noise level of the odd harmonics of the fundamental frequency is calculated. The value is displayed as a Root mean square voltage level that is representative of the average odd harmonic frequency noise content present in the first second of the ES recording. This data classifies which points in an ES project suffer from high odd harmonic electrical noise interference.

### Post-Filter Odd Harmonic frequency Level

The harmonic of a periodic wave form is described as a frequency at an integer multiple of the fundamental frequency. As such, they are all periodic at the fundamental frequency and so are there sums. Odd harmonic frequencies are the odd integer multiples of a fundamental frequency. If a fundamental frequency (f) is 50Hz then the first odd harmonic will be (3f) or 150Hz and the second odd harmonic will be (5f) or 250Hz and so on. The same is true of even harmonic frequencies where the first even harmonic is (2f) or 100Hz and the second even harmonic is (4f) or 200Hz. These harmonic frequencies can extend to high frequencies but usually decrease in amplitude as frequency increases. In most cases, this periodic noise is generated by induced currents from power systems, such as, power lines and transformers etc.

This data is produced by the frequency analysis of the first second of data recorded on an ES sounding data set after odd harmonic digital filtering was applied to the entire data set. This first second of data is assumed to be void of any ES data and is merely represented of the filtered background electrical noise around the sounding location. The odd harmonic frequencies are calculated by the odd integer multiplication of the detected fundamental frequency up to a maximum frequency of 1000Hz. The average noise level of the filtered odd harmonics of the fundamental frequency is calculated. The value is displayed as a root mean square voltage level that is representative of the average odd harmonic frequency noise content present in the first second of the ES recording after filtering of the odd harmonic frequencies. This data classifies which points in an ES project suffer from high odd harmonic Electrical noise interference.

### Pre-Filter Even Harmonic frequency Level

The harmonic of a periodic wave form is described as a frequency at an integer multiple of the fundamental frequency. As such, they are all periodic at the fundamental frequency and so are there sums. Odd harmonic frequencies are the odd integer multiples of a fundamental frequency. If a fundamental frequency (f) is 50Hz then the first odd harmonic will be (3f) or 150Hz and the second odd harmonic will be (5f) or 250Hz and so on. The same is true of even harmonic frequencies where the first even harmonic is (2f) or 100Hz and the second even harmonic is (4f) or 200Hz. These harmonic frequencies can extend to high frequencies but usually decrease in amplitude as frequency increases. In most cases, this periodic noise is generated by induced currents from power systems, such as, power lines and transformers etc.

This data is produced by the frequency analysis of the first second of data recorded on an ES sounding data recording. This data is assumed to be void of any ES data and is merely represented of the background electrical noise around the sounding location. The even harmonic frequencies are calculated by the even integer multiplication of the detected fundamental frequency up to a maximum frequency of 1000Hz. The average noise level of the even harmonics of the fundamental frequency is calculated. The value is displayed as a root mean square voltage level that is representative of the average even harmonic frequency noise content present in the first second of the ES recording. This data classifies which points in an ES project suffer from high even harmonic Electrical noise interference.

### Post-Filter Even Harmonic frequency Level

The harmonic of a periodic wave form is described as a frequency at an integer multiple of the fundamental frequency. As such, they are all periodic at the fundamental frequency and so are there sums. Odd harmonic frequencies are the odd integer multiples of a fundamental frequency. If a fundamental frequency (f) is 50Hz then the first odd harmonic will be (3f) or 150Hz and the second odd harmonic will be (5f) or 250Hz and so on. The same is true of even harmonic frequencies where the first even harmonic is (2f) or 100Hz and the second even harmonic is (4f) or 200Hz. These harmonic frequencies can extend to high frequencies but usually decrease in amplitude as frequency increases. In most cases, this periodic noise is generated by induced currents from power systems, such as, power lines and transformers etc.

This data is produced by the frequency analysis of the first second of data recorded on an ES sounding data set after even harmonic digital filtering was applied to the entire data set. This first second of data is assumed to be void of any ES data and is merely represented of the filtered background electrical noise around the sounding location. The even harmonic frequencies are calculated by the even integer multiplication of the detected fundamental frequency up to a maximum frequency of 1000Hz. The average noise level of the filtered even harmonics of the fundamental frequency is calculated. The value is displayedas a root mean square voltage level that is representative of the average even harmonic frequency noise content present in the first second of the ES recording after filtering of the even harmonic frequencies. This data classifies which points in an ES project suffer from high even harmonic Electrical noise interference.

### Pre-Filter High frequency Level

High Frequency noise in terms of an ES survey is defined as the noise above a frequency of 1000Hz. High frequency noise is caused by a number of natural and man-made sources, including but not limited to:

1) Electric fences;

2) Lightning;

3) Telephone lines;

4) Communication devices;

5) High frequency seismic sources.

By default, all high frequency noise is filtered out by a 1000Hz digital low pass filter prior to processing. This is done to remove any interference that the high frequency noise may have on the ES data sets. Unfortunately, at times, there is still high frequency noise bleed through caused by exceptionally high amplitude noise sources. It is thus useful to quantify this noise in order to identify potential risk.

The high frequency noise data is the summed average of the noise in the frequency band from 1000Hz to 22050Hz prior to the application of the digital low pass filter. This effectively quantifies the average level of high frequency noise around the ES survey site. The value displayed is a Root mean square voltage level that is representative of the average high frequency noise content present in the first second of the ES recording prior filtering out of the frequencies above 1000Hz. This data is useful at determining if there are any high frequency noise sources present around the ES survey location.

### Filter Efficiency Coefficient

In order to determine whether the applied digital filters are effectively filtering out interference produced by any possible noise sources, a filter efficiency coefficient is calculated. This filter efficiency coefficient is calculated by subtracting the frequency domain post filtering data set from the pre-filtering data set and dividing it by the frequency domain data of the pre-filter data set. This effectively determines how effectively the filters have functioned at removing noise interference from the ES data sets. This value is expressed as a percentage between 0 and 100 percent of filter efficiency.

### Signal to noise ratio

Signal-to-noise ratio (SNR) is a measure that compares the level of a desired signal to the level of background noise. It is defined as the ratio of signal power to the noise power.

The signal to noise ratio for each sounding data set is calculated by summing the peak power of the individual detected seismic source induced ES responses in volts, which have been selected by the correlation algorithms for use in the stack. This value is then divided by the number of used strikes to determine the average ES response peak value. The noise floor is then determined by the summation of the frequency domain data divided by the number of frequency bins used. The Signal to noise ratio is then calculated by dividing the Average peak ES response data by the calculated average noise floor level.

### Calculated Point Noise Risk

The calculated point noise risk is empirically determined by an experiential quantification of acceptable SNR levels in ES surveys. These levels are defined as:

This value, shown in the Table above, is representative of the risk that the filtered noise within the ES data set has to the data set. The higher the risk level, the lower the quality the overall ES data has and the lower the ES data set reliability is.

### Calculated Total ES Sounding Risk

This variable provides a final risk assessment of the quality of the data at any particular sounding location, including the correlation index and noise risk data for each point.

It is calculated by multiplying the correlation index of the stack set data at a sounding location by the calculated electrical noise risk at the same location. This risk coefficient is then displayed as a risk percentage. This data is an indication of the reliability of the recorded ES data and can be used to determine data quality at any particular ES sounding location.

# Electro-Seismic Data Sets

## Hydraulic Conductivity Tomography (ESKT)

The ESKT data shows the relative, inferred, electro seismic hydraulic conductivity tomography data results for the test site sounding profile. As the data has not been calibrated to an absolute field reference point, at which the hydraulic conductivity, with depth, is absolutely known. It is described as a normalized, relative, inferred, hydraulic conductivity value ranging from 0 to 100%. The maximum normalized value, shown on the ESKT profile as 100%, is determined by assigning the maximum recorded hydraulic conductivity value for the site a value of 100%. All other values are normalized against this value.

## Electro Seismic Coupling Coefficient Tomography (ESCCT)

The ESCCT data shows the relative, inferred, electro seismic coupling coefficient tomography data for the sounding profile. The ESCCT data describes the normalized, relative, coupling efficiency for the conversion of the seismic pressure wave to the resultant electro-magnetic wave field. The data is expressed as a normalized percentage of the maximum recorded conversion efficiency.

The ESCCT data can be used to indicate a number site specific variables, including:

1) Pore fluid electrical conductivity variability;

2) Pore fluid acidity variability;

3) Pore fluid temperature variability;

4) Pore fluid viscosity variability;

5) Zeta Potential.

The properties that affect the ESCCT data response are site specific and must be viewed and interpreted in context.

## Fracture Analysis (ESFT)

The ESFT data shows the relative, inferred, bedding plain fracture zone depths. The electro seismic data is spectrally analyses and specific frequency patterns associated with fracturing are used to infer bedding plane fracturing. The results are used to describe possible secondary permeability features. These fractured zones are generally associated with higher fluid flow rates.

There are a number of geological features that could produce indications of a fracture indicator response. These include

1) Geological interfaces or interfacial responses;

2) Quartz bearing formations;

3) Beading plain fractures;

4) Ferrite bearing formations;

5) Sulphide bearing formations.

A reasonable understanding of the site geology and correlating neighboring point data is required to accurately interpret ESFT data.

If the pressure wave does intersect a real fracture, a response will only be produced by a near horizontal fracture. Any fracture with a dip of 30 degrees or more is generally not seen, as they do not develop a strong response.

## Interface Tomography (ESIT)

The ESIT data indicate the positions of interfaces between formations with differing electrical and elastic properties. It makes use of the unique interfacial effects generated, by electro seismic conversion, as the pressure wave passes through a formation change.

The ESIT data shown is representative of all the detected interface responses, irrespective of amplitude. Thus strong and weak reflectors are shown as equally weighted responses. This is done to highlight any geological features that may be missed if viewed in context of reflector strength. The data does show the total gradient polarity of the reflector. A blue response is a negative polarity reflector and red is a positive polarity reflector.

## Electro-Seismic Interface Angular Response Tomography (ESIAT)

The ESIAT data represents the change in angular gradient that occurs at the interface between two formations with differing electrical or elastic properties. This data is response amplitude dependent as the gradient of the response in front and behind the interface are amplitude dependent, however, changes in formations with similar electrical or elastic property changes will have similar angular gradient change responses. This allows for the characterization of interfaces between independent sounding locations. This will allow for the correlation of inter-point interfaces to determine the position and depth of major geological changes.

## Electro-Seismic Interface Angular Normalized Response Tomography (ESIANT)

The ESIANT data set is the normalized version of the ESIAT data set. The normalization of the ESIAT data set is done to minimize the effects of ES response amplitude which may affect the calculation of pre and post response gradient calculations. This effectively improves the correlation of ESIAT between individual sounding locations. This allows for improved characterization of interfaces between independent sounding locations. And improves the interpretation of the position and depth of major geological changes.

## Electro-Seismic Change in Absolute Response Tomography (ESCAGT)

The ESCAGT data set is the relative, inferred indication of the absolute Zeta potential, or Electro kinetic potential, of a geological formation. Zeta potential is defined as the potential difference generated by the ionic charge separation between the ions at the slipping plain of the separation double layer, as referenced to a point within the pore space fluid far away from the double layer. An illustration of this is shown in the Figure below.

The Zeta potential is commonly used as a measure of charge. As such, the Zeta potential is a defining electrical characteristic of any given geological formation and can be used to characterize formation changes or similarities.

In the case of ESCAGT data sets, the values shown are for the absolute calculation of the Zeta potentials magnitude. Thus any negative values are ignored. This allows for the analysis of Zeta potential data from a purely magnitude based point of view.

The data is expressed as the normalized, relative, absolute response gradient of the ES field with a maximum value of 100% and a minimum value of 0%.

## Electro-Seismic Change in Total Response Tomography (ESCTGT)

The ESCTGT data set is the relative, inferred indication of the total Zeta potential, or Electro kinetic potential, of a geological formation. Zeta potential is defined as the potential difference generated by the ionic charge separation between the ions at the slipping plain of the separation double layer, as referenced to a point within the pore space fluid far away from the double layer. An illustration of this is shown in the Figure above.

The Zeta potential is commonly used as a measure of charge. As such, the Zeta potential is a defining electrical characteristic of any given geological formation and can be used to characterize formation changes or similarities.

In the case of ESCAGT data sets, the values shown are for the total calculation of the Zeta potentials. Thus any negative values are included. This allows for the analysis of Zeta potential data from a total field point of view including both magnitude and polarity information. As the Zeta potential for most fully saturated geological formations with pore space fluid that has electrolyte content is inherently negative in value, most of the ESCTGT will be negative in value. An exception to this can be found in the unconsolidated materials that overly the saturated consolidated geological formations. These formations generally are slightly positive in nature. This positive Zeta potential data can be used to define this unconsolidated material and is useful at determining the thickness and extent of unconsolidated topsoil’s under a survey site.

The ESCTGT data is expressed as the normalized, relative, total response gradient of the ES field with a maximum value of 100% and a minimum value of -100%.

## Electro-Seismic Groundwater Flow Potential Tomography (ESGFPT)

The ESGFPT indicates the most likely position for groundwater flow. It combines all the relevant electrical and hydrological ES response data of the formations under the site to determine the normalized relative inferred percentage of probability of groundwater flow. It is important to note that the data represented is not an indication of the absolute probability of groundwater flow, but rather the most likely locations, or locations with the best possibility, of groundwater flow, when compared to all the other ES locations surveyed.

The ESCTGT data is expressed as the normalized, relative, potential for groundwater flow with a maximum value of 100% and a minimum value of 0%.

It must be clearly understood that a value of 100% ESGFPT probability is not and should not be interpreted as a 100% reality of locating high flowing groundwater. It is only an estimated indicator of where the most likely position for groundwater flow might be located under the surveyed site.

## Electro-Seismic Fractures associated with Interfaces Tomography (ESFIT)

The ESFIT data is the representation of the correlation between the ESIT data and the ESFT data sets. This data effectively delineates fracturing indicators that exist at the interfaces between changes of geological formations. This data effectively defines interfacial fracturing and provides insight into the secondary permeability nature of any given geological interface.

The ESFIT data is expressed as the normalized, relative, probability of fracturing with a maximum value of 100% and a minimum value of -100%.

## Electro-Seismic Fractures associated with Hydraulic Conductivity Tomography (ESFKT)

The ESFKT data is the representation of the correlation between the ESKT data and the ESFT data sets. This data effectively delineates fracturing indicators that exist within primary permeability formations or aquifers. This allows for the analysis of probable high flow groundwater sources within aquifer systems that are facilitated by secondary permeability flow paths.

The ESFKT data is expressed as the normalized, relative, probability of fracturing within a primary permeability formation with a maximum value of 100% and a minimum value of 0%.

## Electro-Seismic Fractures associated with Ground Water Flow Potential Tomography (ESFGFPT)

The ESFGFPT data is the representation of the correlation between the ESGFPT data and the ESFT data sets. This data effectively delineates fracturing indicators that exist within calculated, high probability of groundwater flow, aquifers. This allows for the analysis of probable high flow groundwater sources within aquifer systems that are facilitated by secondary permeability flow paths.

The ESFKT data is expressed as the normalized, relative, probability of fracturing within a high probability groundwater flow formation with a maximum value of 100% and a minimum value of 0%.

## Electro-Seismic Coupling Coefficient Change in Absolute Response Gradient Tomography (ESCCCAGT)

The ESCCCAGT data is the representation of the correlation between the ESCCT data and the ESCAGT data sets. This data effectively delineates formations with both high ES coupling efficiencies and high Zeta Potential characteristics. This data is useful at delineating formations which have high coupling efficiencies with strong Zeta potential electrical characteristics. This helps evaluate whether ES coupling efficiency is dominated by Zeta potential or by one of the other properties that control ES coupling efficiency, such as:

1) Pore fluid electrical conductivity variability;

2) Pore fluid acidity variability;

3) Pore fluid temperature variability;

4) Pore fluid viscosity variability.

The ESCCCAGT data is expressed as the normalized, relative, probability of the presence of a formation with both high coupling efficiency and high Zeta Potential with a maximum value of 100% and a minimum value of 0%.

## Electro-Seismic Coupling Coefficient Change in Total Response Gradient Tomography (ESCCCTGT)

The ESCCCTGT data is the representation of the correlation between the ESCCT data and the ESCTGT data sets. This data effectively delineates formations with both high ES coupling efficiencies and high Zeta Potential characteristics. This data is useful at delineating formations which have high coupling efficiencies with strong Zeta potential electrical characteristics. This helps evaluate whether ES coupling efficiency is dominated by Zeta potential or by one of the other properties that control ES coupling efficiency, such as:

5) Pore fluid electrical conductivity variability;

6) Pore fluid acidity variability;

7) Pore fluid temperature variability;

8) Pore fluid viscosity variability.

The difference between the ESCCCTGT and the ESCCCAGT data sets is that the ESCCCTGT data set includes the polarity of the Zeta Potential, as well as the magnitude, where the ESCCCAGT includes only the magnitude of the Zeta potential. This allows for the evaluation of coupling efficiency effects within the unconsolidated topsoil covering a site.

The ESCCCTGT data is expressed as the normalized, relative, probability of the presence of a formation with both high coupling efficiency and high Zeta Potential with a maximum value of 100% and a minimum value of -100%.

# Electro-Telluric Data Sets

## Electro-telluric Tomography (ET)

The ET data set describes the variability in conductivity of the formations under a site. The data set does not describe the apparent conductivity of the formations underlying a site as it is only a representation of the potential difference generated by a streaming current as it passes through a conductive medium. As such the full ohms law equation cannot be applied as the magnet field amplitude of the streaming current is absent. That said, the data does indicate variability in conductivity which can be used to delineate conductive formations even though the exact conductivity cannot be determined without calibration against a resistivity down-hole well log.

The ET data set is generated by frequency analysis of the background noise on the site, for a per-determined period of time, to a maximum frequency of 10Hz. The frequency domain data is analyzed for amplitude to determine the variability of conductivity for the formations at specific depths under the site.

This data set is not linear in resolution. Resolution decrease exponentially from surface level to depth. It is very important that interpretations of conductive formations at depth consider this lower vertical resolution limitation.

The ET data is expressed as the normalized, relative, variability in formation electrical conductivity with a maximum value of 100% and a minimum value of 0%.

## Electro-telluric Gradient Tomography (ETGT)

The ETGT data set describes the gradient of variability in conductivity of the formations under a site. This data allows for the delineation of formations with similar variability in conductivity gradient. As such, it is used to refine lithological unit interpretations for a specific site geology.

The ETGT data is expressed as the normalized, relative, variability in formation electrical conductivity gradient with a maximum value of 100% and a minimum value of -100%.

## Electro-telluric Interface Tomography (ETIT)

The ETIT data set describes the position and depths of interface indicators that occur at the boundaries between two geological units with differing electrical conductivity variability. This data allows for the delineation of ET generated interfaces data. This effectively allows for the interpretation of lithological units under the site from an ET point of view. This data is very useful as a comparative data set to correlate the similar ESIT data sets too.

The ETIT data is expressed as the normalized, relative, probability of an interface with a maximum value of 100% and a minimum value of -100%.

## Electro-telluric Interface Angular Response Tomography (ETIAT)

The ETIAT data represents the change in angular gradient of electrical conductivity variability that occurs at the interface between two formations with differing electrical properties. This data is response amplitude dependent as the gradient of the response in front and behind the interface are amplitude dependent, however, changes in formations with similar electrical conductivity properties changes will have similar angular gradient change responses. This allows for the characterization of interfaces between independent sounding locations. This will allow for the correlation of inter-point interfaces to determine the position and depth of major geological changes.

The ETIAT data is expressed as the normalized, relative, probability of an interface with a maximum value of 100% and a minimum value of -100%.

## Electro-telluric Interface Angular Normalized Response Tomography (ETIANT)

The ETIANT data set is the normalized version of the ETIAT data set. The normalization of the ETIAT data set is done to minimize the effects of ET response amplitude which may affect the calculation of pre and post response gradient calculations. This effectively improves the correlation of ETIAT between individual sounding locations. This allows for improved characterization of interfaces between independent sounding locations. And improves the interpretation of the position and depth of major geological changes.

The ETIANT data is expressed as the normalized, relative, probability of an interface with a maximum value of 100% and a minimum value of -100%.

# Electro-Seismo-Telluric Data Sets

## Electro-Telluric Electro-Seismic Hydraulic Conductivity Tomography (ETESKT)

The ETESKT data is the representation of the correlation between the ESKT and ET data sets. It allows for the analysis of high hydraulic conductivity formations in relation to their perceived conductivity variability. In most cases, higher conductivity formations are associated with groundwater bearing aquifer systems. When a higher conductivity formation correlates well with a high level of hydraulic conductivity, the probability that the formation is a high permeability water saturated aquifer is high. As such this data set is useful at improving the interpretation of aquifer systems.

As the conductivity variability of a geological is related to the salinity of the formation pore space fluid, a formation with a high level of salinity will also have a higher electrical conductivity variation. This means that the ETESKT data can also be used to determine if a particular aquifer system is saline or not.

The ETESKT data is expressed as the normalized, relative, aquifer probability with a maximum value of 100% and a minimum value of 0%.

## Electro-Telluric Electro-Seismic Coupling Coefficient Tomography (ETESCCT)

The ETESCCT data is the representation of the correlation between the ESCCT and ET data sets. It allows for the analysis of a geological formations ES coupling efficiency in terms of its correlation to the electrical conductivity variability. This data is used to determine whether a particular high conductivity formation is high in electrical conductivity variability due to the influence of high ES coupling efficiency, which is determined by the following parameters:

1) Pore fluid electrical conductivity variability;

2) Pore fluid acidity variability;

3) Pore fluid temperature variability;

4) Pore fluid viscosity variability;

5) Zeta Potential.

This data set plays and important role in the interpretation of the electrical characteristics of a given formation and pore space fluid.

The ETESCCT data is expressed as the normalized, relative, correlation index between the ESCCT and ET data sets with a maximum value of 100% and a minimum value of 0%.

## Electro-Telluric Electro-Seismic Change in Absolute Response Gradient Tomography (ETESCAGT)

The ETESCAGT data is the representation of the correlation between the ESCAGT and ET data sets. It allows for the analysis of ES Zeta Potential as it correlates to ET electrical conductivity variability for a particular geological formation. This is important as the ETESCCT data indicates the ES coupling efficiency correlation, as a whole, to the electrical conductivity variability of the formation. This does not allow for the determination of whether an increased ES coupling efficiency is due to formation conductivity or if it is due to factors that affect the pore space fluid, such as salinity, PH or viscosity. The ETESCAGT data, however, targets only the Zeta Potential of the formation which is directly coupled to the formation chargeability and does not relate to the electrical properties of the pore space fluid. This allows for the determination of whether a formation electrical conductivity variability is due to the rock matrix electrical properties or if it is due to variations in the pore space fluid electrical properties.

The ETESCAGT data is expressed as the normalized, relative, correlation index between the ESCAGT and ET data sets with a maximum value of 100% and a minimum value of 0%.

## Electro-Telluric Electro-Seismic Groundwater Flow Potential Tomography (ETESGFPT)

The ETESGFPT data is the representation of the correlation between the ESGFPT and ET data sets. It allows for the analysis of the correlation between formations with high calculated probability of groundwater flow and the electrical conductivity variability of said formations.

This data helps improve the interpretation of the ESGFPT data sets by correlating them to high conductivity variability data, which is a strong indicator of groundwater saturated formations.

The ETESGFPT data is expressed as the normalized, relative, correlation index between the ESGFPT and ET data sets with a maximum value of 100% and a minimum value of 0%.

## Electro-Telluric Electro-Seismic Coupling Coefficient Change in Absolute Response Gradient Tomography (ETESCCCAGT)

The ETESCCCAGT data is the representation of the correlation between the ESCCCAGT and ET data sets.

The ESCCCAGT evaluate whether ES coupling efficiency is dominated by Zeta potential or by one of the other properties that control ES coupling efficiency.

The ETESCCCAGT data set expands on this by allowing for the delineation of formations with high electrical conductivity caused by high coupling efficiencies due to high formation Zeta potentials. This data set effectively represents an enhanced interpretation of the ETESCAGT data set, in that it includes the ES coupling efficiency data in the correlation index calculation.

The ETESGFPT data is expressed as the normalized, relative, correlation index between the ESCCT, ESCAGT and ET data sets with a maximum value of 100% and a minimum value of 0%.

## Electro-Telluric Electro-Seismic Groundwater Flow Potential Change in Absolute Response Gradient Tomography (ETESGFPCAGT)

The ETESGFPCAGT data is the representation of the correlation between the ESGFPT, ESCAGT and ET data sets. The ETESGFPCAGT data is used to determine the depth of formations that have high calculated groundwater flow potential, high inferred Zeta Potential and high electrical conductivity variability. This data thus indicates the probability of aquifers with fresh water saturation and high permeability. It is thus used to evaluate a site for fresh water aquifer systems.

The ETESGFPCAGT data is expressed as the normalized, relative, correlation index between the ESGFPT, ESCAGT and ET data sets with a maximum value of 100% and a minimum value of 0%.

**Magneto-Telluric Data Sets**

**Magneto-telluric Tomography (MT)**

The MT data set describes the apparent resistivity of the formations under a site, calculated by combining the ET and magnetic sensor data using a 1D magneto-telluric apparent resistivity equation.. The data set does not describe the actual resistivity of the formations underlying a site as the MT data will first have to be applied to a resistivity inversion model to calculate this data. Resistivity inversion is not done at any point by the ATS GeoSuite processing systems.

The MT data set is generated by frequency analysis of the background noise on the site, for a per-determined period of time, to a maximum frequency of 10Hz. The frequency domain data is analyzed for amplitude to determine the apparent resistivity for the formations at specific depths under the site.

This data set is not linear in resolution. Resolution decrease exponentially from surface level to depth. It is very important that interpretations of conductive formations at depth consider this lower vertical resolution limitation.

The MT data depth estimate is calibrated by using the calculated soil resistivity at the point of the sounding.

The MT data is expressed as the normalized, relative, apparent formation electrical resistivity with a maximum value of 100% and a minimum value of 0%, unless calibrated by the soil resistivity data recorded at the sounding point location.

**Magneto-telluric Gradient Tomography (MTGT)**

The MTGT data set describes the gradient of apparent resistivity of the formations under a site. This data allows for the delineation of formations with similar apparent resistivity gradient. As such, it is used to refine lithological unit interpretations for a specific site geology.

The MTGT data is expressed as the normalized, relative, apparent formation electrical resistivity gradient with a maximum value of 100% and a minimum value of -100%.

**Magneto-telluric Interface Tomography (MTIT)**

The MTIT data set describes the position and depths of interface indicators that occur at the boundaries between two geological units with differing apparent resistivity. This data allows for the delineation of MT generated interfaces data. This effectively allows for the interpretation of lithological units under the site from an MT point of view. This data is very useful as a comparative data set to correlate the similar ESIT data sets too.

The MTIT data is expressed as the normalized, relative, probability of an interface with a maximum value of 100% and a minimum value of -100%.

**Magneto-telluric Interface Angular Response Tomography (MTIAT)**

The MTIAT data represents the change in angular gradient of apparent electrical resistivity that occurs at the interface between two formations with differing electrical properties. This data is response amplitude dependent as the gradient of the response in front and behind the interface are amplitude dependent, however, changes in formations with similar electrical apparent resistivity properties changes will have similar angular gradient change responses. This allows for the characterization of interfaces between independent sounding locations. This will allow for the correlation of inter-point interfaces to determine the position and depth of major geological changes.

The MTIAT data is expressed as the normalized, relative, probability of an interface with a maximum value of 100% and a minimum value of -100%.

**Magneto-telluric Interface Angular Normalized Response Tomography (MTIANT)**

The MTIANT data set is the normalized version of the MTIAT data set. The normalization of the MTIAT data set is done to minimize the effects of MT response amplitude which may affect the calculation of pre and post response gradient calculations. This effectively improves the correlation of MTIAT between individual sounding locations. This allows for improved characterization of interfaces between independent sounding locations. And improves the interpretation of the position and depth of major geological changes.

The MTIANT data is expressed as the normalized, relative, probability of an interface with a maximum value of 100% and a minimum value of -100%.

**Magneto-Seismo-Telluric Data Sets**

**Magneto-Telluric Magneto-Seismic Hydraulic Conductivity Tomography (MTESKT)**

The MTESKT data is the representation of the correlation between the ESKT and MT data sets. It allows for the analysis of high hydraulic conductivity formations in relation to their perceived apparent resistivity. In most cases, lower apparent resistivity formations are associated with groundwater bearing aquifer systems. When a lower apparent resistivity formation correlates well with a high level of hydraulic conductivity, the probability that the formation is a high permeability water saturated aquifer is high. As such this data set is useful at improving the interpretation of aquifer systems.

As the apparent resistivity of a geological is related to the salinity of the formation pore space fluid, a formation with a high level of salinity will also have a higher apparent electrical resistivity value. This means that the MTESKT data could also be used to determine if a particular aquifer system is saline or not.

The MTESKT data is expressed as the normalized, relative, aquifer probability with a maximum value of 100% and a minimum value of 0%.

**Magneto-Telluric Magneto-Seismic Coupling Coefficient Tomography (MTESCCT)**

The MTESCCT data is the representation of the correlation between the ESCCT and MT data sets. It allows for the analysis of a geological formations ES coupling efficiency in terms of its correlation to the electrical conductivity variability. This data is used to determine whether a particular high conductivity formation is high in electrical conductivity variability due to the influence of high ES coupling efficiency, which is determined by the following parameters:

1) Pore fluid electrical conductivity variability;

2) Pore fluid acidity variability;

3) Pore fluid temperature variability;

4) Pore fluid viscosity variability;

5) Zeta Potential.

This data set plays and important role in the interpretation of the electrical characteristics of a given formation and pore space fluid.

The MTESCCT data is expressed as the normalized, relative, correlation index between the ESCCT and MT data sets with a maximum value of 100% and a minimum value of 0%.

**Magneto-Telluric Magneto-Seismic Change in Absolute Response Gradient Tomography (MTESCAGT)**

The MTESCAGT data is the representation of the correlation between the ESCAGT and MT data sets. It allows for the analysis of ES Zeta Potential as it correlates to MT electrical conductivity variability for a particular geological formation. This is important as the MTESCCT data indicates the ES coupling efficiency correlation, as a whole, to the electrical conductivity variability of the formation. This does not allow for the determination of whether an increased ES coupling efficiency is due to formation conductivity or if it is due to factors that affect the pore space fluid, such as salinity, PH or viscosity. The MTESCAGT data, however, targets only the Zeta Potential of the formation which is directly coupled to the formation chargeability and does not relate to the electrical properties of the pore space fluid. This allows for the determination of whether a formation electrical conductivity variability is due to the rock matrix electrical properties or if it is due to variations in the pore space fluid electrical properties.

The MTESCAGT data is expressed as the normalized, relative, correlation index between the ESCAGT and MT data sets with a maximum value of 100% and a minimum value of 0%.

**Magneto-Telluric Magneto-Seismic Groundwater Flow Potential Tomography (MTESGFPT)**

The MTESGFPT data is the representation of the correlation between the ESGFPT and MT data sets. It allows for the analysis of the correlation between formations with high calculated probability of groundwater flow and the electrical apparent resistivity of said formations.

This data helps improve the interpretation of the ESGFPT data sets by correlating them to low apparent resistivity data, which is a strong indicator of groundwater saturated formations.

The MTESGFPT data is expressed as the normalized, relative, correlation index between the ESGFPT and MT data sets with a maximum value of 100% and a minimum value of 0%.

**Magneto-Telluric Magneto-Seismic Coupling Coefficient Change in Absolute Response Gradient Tomography (MTESCCCAGT)**

The MTESCCCAGT data is the representation of the correlation between the ESCCCAGT and MT data sets.

The ESCCCAGT evaluate whether ES coupling efficiency is dominated by Zeta potential or by one of the other properties that control ES coupling efficiency.

The MTESCCCAGT data set expands on this by allowing for the delineation of formations with low apparent resistivity caused by high coupling efficiencies due to high formation Zeta potentials. This data set effectively represents an enhanced interpretation of the MTESCAGT data set, in that it includes the ES coupling efficiency data in the correlation index calculation.

The MTESGFPT data is expressed as the normalized, relative, correlation index between the ESCCT, ESCAGT and MT data sets with a maximum value of 100% and a minimum value of 0%.

**Magneto-Telluric Magneto-Seismic Groundwater Flow Potential Change in Absolute Response Gradient Tomography (MTESGFPCAGT)**

The MTESGFPCAGT data is the representation of the correlation between the ESGFPT, ESCAGT and MT data sets. The MTESGFPCAGT data is used to determine the depth of formations that have high calculated groundwater flow potential, high inferred Zeta Potential and low apparent resistivity variability. This data thus indicates the probability of aquifers with fresh water saturation and high permeability. It is thus used to evaluate a site for fresh water aquifer systems.

The MTESGFPCAGT data is expressed as the normalized, relative, correlation index between the ESGFPT, ESCAGT and MT data sets with a maximum value of 100% and a minimum value of 0%.

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