“If the only tool you have is a hammer, you tend to see every problem as a nail.”
― Abraham H. Maslow

For years the geologists working on civil engineering and environmental projects have grappled with the challenge of building ground models using tools designed for drafting engineering designs.
Traditional engineering design started by drawing 2D sections, plans and elevations to define a 3D structure, which isn’t practical for natural geological structures which are almost never straight and aligned. Geology is a science rather than an engineering discipline because it models the real world instead of designing man-made structures within it. Geology doesn’t come in boxes, triangles, straight lines or even sophisticated Bezier curves. All of these are simply ways of representing the geology on a computer.

Many of the awkward problems people face when geological modelling are the result of the computer software expecting a geoscientist to conform to the computer’s world and not the other way around.

Traditionally geologists have used explicit modelling tools, which are essentially akin to an engineering drawing process. The modeller defines geological structures such as stratigraphy and faults by explicitly drawing them on regularly spaced sections and joining them. The data may be used by the modeller to constrain where they draw, and there may be tools to accelerate the process, but fundamentally it is a process of drawing, and painstakingly redrawing when the underlying data changes over time.

Why the need for implicit modelling?

Instead of manually drawing the geology, if we define rules based on geological principles to interpret ground investigation data through the lens of a geologist, we then create a data driven 3D model that is dynamically linked. This 3D implicit model supports the essential process of validating assumptions and testing hypotheses about the possible ground conditions. It does this in a way that echoes the natural forms found in real life, but leaves the heavy lifting to the computer, which in turn frees up the geologists bandwidth. They spend less time connecting dots, and more time applying their professional expertise.

The critical advantage of an implicit model is that it allows the user to answer a simple question: If I went to a particular point under the ground, what would I expect to find based on the data? When the model can tell us what is likely to be there, we can do a great deal…

So how is this transforming an industry?

Implicit modelling transforms workflows in three critical ways.

  • By allowing rapid iteration of ground models. When new ground investigation data is obtained (for example as you progress from proposal phase to detailed design work), it is simply a matter of adding the data and performing basic quality checks before publishing an update to the model.
  • With 3D model, you can generate on demand outputs – for example, cross sections that are used for geotechnical analysis or by structural engineering disciplines can be generated on the fly from any location within the model.
  • This approach is faster, more flexible and fundamentally better suited to making geological or geotechnical models. Today, many people find the increased modelling speed of the implicit modelling process is justified by the benefit of having an up-to-date model that incorporates the latest data. However, another significant benefit is that implicit modelling allows more than one geological interpretation to be considered and maintained. The geological uncertainty is a critical factor in many projects, and this can be best described by maintaining multiple models.

It’s a proven technology

The quality of an implicit modelling engine lies in the algorithms which determine how known data is used to imply (or estimate) otherwise unknown data to create surfaces. Radial Basis Functions (RBF’s) are the basic method to do this. We set out to create an advanced implicit modelling engine. Years of interpolation research by leading mathematicians culminated in the creation of FastRBFTM, the algorithm that powers Leapfrog. The main difference between traditional RBF’s and FastRBF™ is the ability to deal with over 1,000,000 data points incredibly quickly on an ordinary computer. Filtering and approximation methods make FastRBF™ ideal for visualising and processing non-uniformly sampled noisy data. FastRBF™ has extraordinary extrapolation capabilities, even when large gaps occur in a data set. Since 2004 the Leapfrog algorithms have been continually improved to create tailored outcomes based on the type of geological structure being modelled. Not all implicit modelling solutions are the same. It is the Leapfrog engine that provides the fast and dynamic approach to implicit modelling.

We are committed to helping transform the civil and environmental industries through 3D implicit modelling. So if you want to read more on our expert opinions, we have created a three-part report which talks about hot topics of geological risk and uncertainty. Just click on MyLeapfrog and download your free copies.

For more industry-relevant perspectives, and innovative thinking, have a read of our Unearthed Report . It’s a global briefing on technology and innovation for the civil engineering and environmental industries.

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