Telling a data-driven story to stakeholders is easier with georeferenced aerial and satellite images that provide real-world context for your maps. However, finding servers with images in the location and resolution you need can be time-consuming, challenging, and frustrating—turning your focus from solving complex scientific problems to grappling with graphic design issues.

Surfer now simplifies the process of obtaining the contextual imagery you need. With just a click, you can connect to various reliable XYZ Tiles servers to download high-quality maps, aerial photos, and satellite imagery with global coverage.

OpenStreetMaps is just one of the many reliable, global databases now accessible by Surfer. With this direct connection to downloadable XYZ Tiles imagery, you can obtain superior images quickly to use as base maps for your Surfer projects to better engage and captivate stakeholders.

Remember when you wanted to build a company template for a series of maps but didn’t want to start from scratch? Surfer now ensures you never have to do so. Instead of tackling the daunting, tedious task of manually creating a template, Surfer now has over 20 ready-to-use frame templates to help you get started. All you have to do is open the template that catches your eye, customize it with company data, and save it for all future projects.

You can tailor a template to have specific elements like a title block, page size, legend, frame style, logo, and more. Once you save and open the template for another project, you can build on what you’ve already designed, speeding up your visualization process. Of course, access to our suite of frame templates doesn’t mean you can’t create a template from scratch. You’ll still have the option to build one from the ground up whenever you prefer.

We’ve enhanced several aspects of the 3D view, equipping you to create high-quality visualizations with fewer clicks and less effort. The new tools available in Surfer include:

• Copy and Paste Format: Previously, creating similar 3D visualizations required manually entering the same properties for each map, leaving room for errors. Now, you can copy the properties from an existing 3D visualization and paste them into another, reducing mistakes and speeding up your work.
• 3D Triad: You can easily see how your 3D model is oriented with Surfer’s new customizable Triad, which you can add to any corner of your 3D view.
• Perspective or Orthographic Projection: Use perspective projection to view your 3D model as the human eye would, or switch to orthographic projection with a keyboard shortcut to preserve parallel lines for easier measuring and digitizing points.
• Predefined 3D Views Using Keyboard Shortcuts: Surfer’s ribbon now gives the option to view your 3D model from the Top, Bottom, North, South, East, and West, eliminating the time-consuming task of aligning your visualization with cardinal directions to get the proper perspective. We’ve also added keyboard shortcuts to make it even easier to quickly understand your model from all angles.
• Bounding Box: To ensure stakeholders grasp the extent of your data, you can visually communicate it by drawing a bounding box around your model. This box can be based on your entire model, an isosurface, or a volume render.
• 3D Vector Files: After drawing 3D polygons or polylines to represent features such as buildings and roads on your base map, you can now save these geometric elements as 3D vector files for future use. Surfer exports 3D geometry in formats like SHP, DXF, and BLN, streamlining your workflow.

With all these new tools in 3D view, you can quickly navigate visualizations, easily orient yourself in models, and improve stakeholder understanding during reports and presentations.

Surfer’s time-saving features equip you to create superior maps much faster, giving you more time to focus on data analysis and developing solutions. Are you ready to accelerate your design process while still producing high-quality visualizations that captivate stakeholders?

To start using these new features, click File | Online | Check for Update

New to Surfer? Start a 14-day free trial of Surfer to get immediate access to all the latest time-saving tools.

Incorporating SPC charts into quality control practices promotes a data-driven approach to decision-making. By highlighting the data that exceeds the upper and/or lower control limits, you are able to efficiently identify variation that requires immediate attention. This can lead to more accurate assessments of process performance and more effective interventions.

SPC charts are not just for monitoring—they are also powerful tools for driving continuous improvement. By regularly analyzing SPC charts, you can identify opportunities for process enhancements by analyzing trends within common cause variation. This ongoing improvement cycle helps maintain competitiveness and adapt to changing market demands.

SPC charts are invaluable for any organization committed to quality and efficiency. Their ability to detect problems early, differentiate between types of variations, enhance process understanding, and drive continuous improvement makes them a cornerstone of effective quality control. By integrating SPC charts into your operations, you can achieve higher consistency and deliver superior results, ultimately gaining a competitive edge.

Embrace the power of SPC charts using Golden Software’s Grapher! Learn how to take your quality control to the next level with our SPC charts how-to article.

1. Download the attached Surfer frame template file.
2. Open Surfer and browse to the downloaded file from the Surfer Welcome dialog.
3. Click File | Save | Save As to rename the file for your current project.
4. Create a map, legend, and other elements as you normally would or copy/paste a finished map into this new project.
5. Edit the text and other items in the User Edit Contents group
6. Save and export the finished map to your preferred presentation format.

Frame Templates in Surfer and Grapher are here to streamline the way you create reports. They save time, ensure consistency, and help you produce professional-quality documents effortlessly. Give them a try and watch your report processing time transform into time for the work you enjoy most!

The algorithm used to interpolate the data can have a large impact on the results. One of the most common, and popular, gridding algorithms is Kriging. The Kriging gridding method is a very flexible algorithm that attempts to express trends in data, rather than bullseyes, and is known for producing visually appealing results.

There are many other gridding methods available and the best gridding algorithm is often determined by the density and dispersion of the input data. The article Choosing the right gridding method in Surfer offers several factors to consider along with recommendations based on those factors. Also included in this article is information about running the GridData_Comparison.bas sample script which creates a grid and contour map of your data using eight of the most common gridding algorithms.

Grid resolution is very similar to image resolution. The number of nodes or pixels in each direction will determine how well smaller features are expressed in the results. Also similar to images, low, medium and high resolution grid files will have approximately 100, 500, and 1000+ grid nodes in the longest direction as a general rule of thumb.

To determine the best resolution for your grid file consider the accuracy required for meaningful interpretation of the results. If every measurement must be represented, the spacing between each grid node must be less than or equal to the distance between the two closest data points. If the exact representation of dense data pockets will not impact the final interpretation, then the spacing should be less than or equal to the average data spacing.

The Z limits for data interpolation are used to account for the data and testing limits of the chemical compound being modeled.

The Z minimum should be set to zero if no data manipulation was required to account for non-detectable testing results. The Z minimum should be set to the data minimum if the data was adjusted to account for the lower testing limit.

The Z maximum should be set to the upper testing or saturation limit. If your data does not approach this value or one is not known, the Z maximum can be left alone.

It is quite common for concentration data to contain spikes where a high value above 1000 can be located next to a low or near zero value. Because data interpolation algorithms use all data in a defined radius to estimate the value at a specific point, these spikes can lead to a positive bias for all surrounding values.

The “Log, save as Linear” Z transform reduces the impact of data spikes by condensing all of the input data to a smaller range before interpolation. It works by taking the log base 10 of the input Z values, performing the interpolation, and then applying the antilog to convert the results back to the original linear values.

One important note is that this setting will ignore zero values. If your data contains true zero values and data spikes, you will need to use your knowledge of the area and chemical properties to determine whether adjusting the input data values or applying the transform produces more accurate results.

Data interpolation is a powerful tool for quickly and accurately converting unevenly spaced data into a uniform matrix or grid. Gridding concentration data comes with some unique challenges that can be overcome by using these five best practices:

1. Adjust the input data so that all values are considered during interpolation.
2. Select the right gridding method for your data dispersion and density.
3. Adjust the resolution to align with your data density and accuracy requirements
4. Limit the Z range of the output grid
5. Apply a Z transform to avoid positive bias around spikes in the data

Download this Surfer script to see these best practices in action with your data!

How is your data distributed? Is there a cutoff or central value around which the data divides? Answering these questions will help you determine the type of colormap that best suits your data. The most common types of color maps and associated data to consider are:

• Sequential: Sequential Data progresses from low to high such as temperature, elevation, or concentration. Similarly, sequential color maps use variations in lightness and saturation of color to emulate a progression from low to high.
• Diverging: Diverging color maps are used when there is a need to highlight deviations from a central point, often zero. They employ two contrasting colors that meet at a neutral midpoint, which helps identify values above or below a specific threshold. Examples of diverging data include velocity, slope, and profitability.
• Qualitative: Qualitative color maps are best suited for categorical data or data grouped into numeric or text-based classes such as well type, contaminant, or zone. These schemes are most useful in visualizations like pie charts, bar charts, or thematic maps.

Human perception of color can vary based on many factors, including cultural differences and color vision deficiencies. It’s essential to use universally distinguishable color combinations. For example, the illustration of water depth using blues, greens, and sometimes yellows or purples is common around the globe. Using similar color maps to illustrate water data makes the data immediately recognizable to a broad audience.

If you are not color blind it can be difficult to understand how a visualization will look to someone that is. Luckily, tools like color blindness simulators can help ensure your visualizations are accessible to everyone.

Simplicity is the key to effective color mapping. Overcomplicating your color scheme with too many colors can confuse the viewer. Use a limited color palette and avoid unnecessary embellishments. A clear, straightforward color scheme will make your data more accessible and easier to understand.

Don’t get caught thinking lots of colorful images will captivate your audience. When presenting numerous charts and maps of related data, using the same color map to represent the same types of data makes it easier for the audience to quickly interpret results. Quick visual interpretation ensures your audience stays engaged throughout a presentation or article instead of getting lost trying to understand one or two images.

Effective color mapping is a fundamental aspect of data visualization that can significantly enhance the communication of complex information. By aligning your color map with your data, considering color perception, keeping it simple, and being consistent, you can create visualizations that are both informative and visually appealing.

Ready to apply these principles in Surfer & Grapher?  Check out the resources below.

Thyng, K. (2015, July). Perceptual Color Maps in matplotlib for Oceanography [Conference Presentation]. Scientific Computing With Python. https://www.youtube.com/watch?v=XjHzLUnHeM0

Smith, N., & Van der Walt, S. (2015, July). A Better Default Colormap for Matplotlib [Conference Presentation]. Scientific Computing With Python. https://www.youtube.com/watch?v=xAoljeRJ3lU

The first thing you’ll need to do is export a KML or KMZ file of your map. If you don’t already have one, check out our KB article Display contours from Surfer in Google Earth.

Next, you’ll need to move or share the file with the laptop, tablet, or phone you’ll be taking into the field. This process will vary depending on your device but one sure fire option is to email the file to yourself or the client so that it can be downloaded and saved to the desired device.

Finally, we can get to the fun stuff and import the file in Google Earth.

1. Open Google Earth on your preferred device.
2. Click the New project button.
   

3. Click Local KML file and then click Import.
   
4. Select the KML or KMZ file from your device and click Open.

Your KML file will appear in your Projects panel as shown below.

Now that you have your site plan in Google Earth you can edit the display properties, pinpoint your location on the map, and add markers with notes or images in places of interest.

To learn more about what you and your clients can do with Google Earth, check out the tutorial Create a map or story in Google Earth Web.

To learn more about creating KML or KMZ files in Surfer, check out the article Display contours from Surfer in Google Earth and note that the workflow outlined in this article can be applied to multiple map types.

Not a Surfer user?  Download the trial to try it for yourself!

One of the primary benefits of the Ricker Method is its ability to provide an accurate assessment of plume dynamics. Traditional methods might offer a snapshot view, but the Ricker Method gives a dynamic perspective, showing how the plume evolves over time. This is particularly important in environments where groundwater flow and contaminant dispersion can vary significantly.

With a clear understanding of plume stability, environmental engineers can design more effective remediation plans. For instance, if the comprehensive analysis indicates that a plume is stable or shrinking, it may be feasible to reduce the intensity of remediation efforts, thus saving resources and costs. Conversely, if the plume is expanding, immediate and more aggressive action can be taken to contain and treat the contamination.

Environmental regulations often require detailed documentation and proof of contamination management. The Ricker Method provides robust statistical analysis, qualitative evaluation, and visual representation that can support compliance with these regulations. It offers a scientifically validated approach to demonstrate that a plume is being monitored and managed effectively.

Golden Software’s Surfer is a powerful tool for visualizing and analyzing geospatial data, making it an ideal platform for implementing the Ricker Method. To learn more about using Surfer to perform the Ricker Method statistical analysis on your contamination plume data, check out our KB article Calculate contamination plume stability statistics in Surfer.

We understand that stakeholder enthusiasm is key. Surfer’s new Block Render Models can help you transform stakeholder anxiety into genuine excitement and set the stage for a successful mining operation. With a block model, mining industry professionals can estimate the shape and size of a resource deposit based on geophysical readings.

Each grid node is rendered as an individual block with full color scheme and transparency customization options. Tailor your model by slicing it to highlight specific areas of the deposit, or you can adjust the limits of the model to match the geometry of existing plans, providing you and your team with as much subsurface information as possible before you break ground.

Create more comprehensive site models by georeferencing 2D imagery in Surfer’s 3D view. Cross sections representing geophysical attributes of your site such as electrical resistivity and seismic data can now be placed precisely at the geographic locations you input. Geologists can even add stratigraphic cross sections to supplement their existing drillhole models to paint a complete picture of the area. 

One of Surfer’s most used 2D features is now available in the 3D view! Assign NoData to a 3D grid during the gridding process, using the 2D convex hull, 2D alpha shape or a 2D polygon boundary to limit the XY extents. You can also assign NoData above or below a 2D grid surface. This helps you create a more visually appealing and relevant model by excluding data from the 3D grid above another surface, such as the topography or groundwater surface or to remove the data above other surface contacts, such as overburden.

Above all else, Golden Software values visualizations that impress stakeholders. To keep up with advancing technology we always are adding graphical improvements. This release we have added the following improvements to help you achieve that wow moment:

• Specular Highlights add a shininess to your 3D models that creates a spectacular effect
• Follow the Camera Lighting to ensure your model is always lit from the viewer’s perspective as you rotate.
• Changing the vertical exaggeration for the model no longer distorts drillhole cylinders

Surfer can now automatically gather the drillhole data associated with any of your runs and interpolate that data into a single 3D grid, eliminating time spent reformatting your data sheet and streamlining your workflow. 

Break new ground when communicating with Surfer to both technical and non-technical audiences alike. Take a leap into the next era of 3D modeling with Surfer and visualize the subsurface like never before!

Explore Surfer and download the free trial today!

The site in question is an industrial park near the local airport. There are also nearby residential neighborhoods and firefighting test sites. The entire area is located in the San Luis Obispo Valley Groundwater Basin in southern California. This basin is essentially a bathtub basin underlain by impermeable sediments of the Franciscan formation and infilled with semi-permeable sediments that contain the groundwater. Outside of a few perch and deep groundwater zones, the basin flows as one groundwater horizon in a westerly direction.

3D View of water basin below site investigation in Surfer

Dickson & Associates was hired by a client that had received a clean-up and abatement order for trichloroethylene (TCE) from the local water board. The water board used local air vapor and groundwater survey data to generate a model that showed a TCE plume with a bullseye under the client’s building and with groundwater flows to the west.They concluded that the location of the bullseye indicated the source of the TCE contamination.

This proved to be an incorrect assumption in a high water use area like the one in question. In this site, there were bi-modal flows to southwest and south as a result of heavy pumping rather than to the west as seen in the basin as a whole.

Zach’s team worked with their client and impacted water users to collect data over a period of years to generate their final model. Data from residential monitoring systems and monitoring well data loggers was combined with several sources of historical data to gain a comprehensive understanding of the plume which cleared their client of any culpability.

Interested in creating a similar project in Surfer? Check out the resources below to learn more about the features and functionality used to create this model.

• Create a Drillhole Map in Surfer
• 3D Gridding in Surfer
• Surfer Help: Assign NoData to 2D & 3D grids
• The Nuts and Bolts of Surfer’s 3D View
• Grapher Help: Polar Rose Chart

If you have any specific questions, please reach out to our dedicated support team for more information.