No single geophysical survey method can examine the subsurface at all sites under any situation. The choice of geophysical methods employed will vary depending on the specific needs, circumstances, and site conditions. The technologies most commonly used when mapping and monitoring groundwater, mineral deposits, and environmental change include ground penetrating radar (GPR), electrical resistivity, and seismic.

The depth of exploration for these methods varies from a few inches to hundreds of feet.  The most common geophysical survey methods and what they are typically used for include:

Electrical Resistivity Tomography (ERT) survey

• ERT measures resistivity against depth to create 2D profiles or slices.
• ERT is typically used for mapping and monitoring of groundwater, mineral deposits, and environmental change.
• ERT measurements can typically investigate depths of 0–200 meters (m).

Ground Penetrating Radar (GPR)

• GPR is a geophysical method that operates by transmitting electromagnetic waves from an antenna and reflects off layers and objects hidden in the ground. These reflections are collected as data which generates a picture of the subsurface.
• GPR can penetrate different depths depending on the type of material being surveyed and the application:
  • Soil and rock – GPR can penetrate less than 10 meters.
  • Concrete – GPR can penetrate concrete up to 24 inches for scanning purposes and can even detect rebar in the concrete.
  • Other materials – GPR can penetrate up to 100 feet in low conductivity materials like dry sand or granite. It can also penetrate hundreds of meters in highly resistive materials like ice.

Seismic Survey

• Seismics is a method that utilizes a vibration source to measure propagation of elastic waves. The results will show the mechanical properties of the ground. Common applications are soil stability, rock quality and depth to bedrock.
• Seismic survey is a low-impact method that enables geoscientists to interpret geological features up to 50 kilometers beneath the earth’s surface.

A skilled geoscientist generally has a clear picture of the subsurface landscape before their survey results are modeled. The objective of data interpolation is to use collected geophysical data and gridding algorithms to accurately represent and enhance this understanding. When working with 2D geophysical data, three key characteristics must be considered:

Noisy data

Noise in geophysical survey data refers to any unwanted or irrelevant signals that interfere with data interpretation. It can originate from various sources, such as the instruments used, soil conditions, or external disturbances.

Inconsistent spacing in data collection

Geophysical survey data, regardless of the survey method used, tends to have high density of data in the vertical direction with lower density data spaced along the acquisition transect.

Accounting for regions where data doesn’t exist

When gridding geophysical data, mapping and modeling software typically populate the entire X and Y extent of the raw data with grid nodes. This approach can result in filling areas where no data was collected, such as above the ground surface or along the edges in ERT surveys.

Understanding these challenges helps identify which gridding parameters to focus on when creating your 2D profile from geophysical data.

To optimize data interpolation, it’s crucial to select the appropriate gridding method or algorithm. Here are some useful tips:

• If the data is relatively smooth between sample points or survey lines, Minimum Curvature gridding is recommended. This is the most commonly used gridding method in geophysics, and most mapping and modeling software rely on it.
• Kriging is ideal for random data, non-parallel line data, or orthogonal line data. It’s best used when data is variable between sample locations, statistical in nature, or poorly sampled or clustered. Kriging is particularly well-suited for geochemical or other geological sample-based data but is rarely used for geophysical data, which typically follows a naturally smooth surface.

When gridding 2D geophysical slice data, minimum curvature gridding is generally the preferred method.

Geophysical survey data often has varying density in the vertical direction compared to the horizontal direction, due to differences in acquisition techniques. Typically, data sample locations along survey lines are spaced about a foot apart, while vertical samples tend to be much more densely packed.

When gridding 2D slice data, it’s important to adjust the grid resolution to account for the dense vertical sampling and the sparser spacing along the survey line. Specifically, we recommend increasing the grid resolution to be equal to or less than the survey spacing in the X direction.  Increasing the resolution helps capture as much detail as possible in the finished map.

After the gridding is complete, the 2D survey profile or cross section is typically displayed as a contour map. Many data interpolation algorithms, including minimum curvature, do not take the varying surface elevation into account and interpolate data into these regions. This means that the regions that are not supposed to have data, like the area above the ground surface on a 2D profile, need to be clipped or removed.

In Surfer, a null or NoData value can be assigned to areas outside the data limits using the Alpha Shape functionality.  This ensures there will only be data displayed in areas where there should be and the interpolation is restricted to areas that contain data.

The skilled geoscientist typically has an understanding of what the subsurface landscape looks like long before generating any maps or models. The goal for data interpolation is to use collected drillhole data and gridding algorithms to accurately reflect and build on that understanding. When working with well data there are four key data characteristics to consider:

• Three-dimensional data
  Data collected at different depths in a well has X, Y, Z, and C coordinates. This increases the complexity of data interpolation and decreases the number of available algorithms.
• Uneven data distribution
  Well locations can be sparse in some areas and prolific in other areas. This variable XY spacing is often combined with dense, uniformly spaced data in the Z direction.
• Multiple data collection methods
  Different instruments, such as Laser Induced Fluorescence (LIF) and Cone Penetration Testing (CPT), generate different types of data with different distributions along the length of the well.
• Impact of natural phenomena
  Natural subsurface phenomena such as soil horizons can impact the preferred flow of monitored materials

Understanding these challenges helps identify which gridding parameters to focus on when creating your 3D grid file or matrix.

Before gridding 3D well data, it’s important to consider the type of data that you have down-the-hole. Different data collection methods produce two common data types that differ based on the way the data is spaced along the well path: point data and interval data.

Point data, which is typically chemical concentration data, Laser Induced Fluorescence (LIF) data, and Cone Penetration Testing (CPT) data, can be very dense and has little spacing between data points. Interval data, which is typically lithology data, is spaced farther apart. Each interval, defined by a From depth to a To depth, represents a layer of soil or a section of constant value.

The first step in any gridding process is importing the data. During this step, make sure you select the data type that aligns with your data. If only the from or to values from interval data are imported, this can result in an upward or downward bias in the interpolation results.

Example of interval data layout and import in Surfer