Trend Reinforcement
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When employing the Tensioned Minimum Curvature method, you can activate Automated Trend Reinforcement. This is a multi-scale technique that attempts to identify dominant local trends in the source data and then biases the minimum curvature method to reinforce the trend in that direction. It is important to recognise that there are an infinite number of “correct” solutions to the minimum curvature equations. This method simply finds a different solution that satisfies trend reinforcement criteria.
In the images above I have gridded aeromagnetic data using tensioned minimum curvature. On the left is the standard technique. It is typical of gridded aeromagnetic data which is collected at high density along lines that are a (relatively) long way apart. The linear magnetic anomalies, which represent different geological formations, are typically discontinuous. They show a “pearls on a string” appearance.
To produce the image on the right, I have turned on the trend reinforcement option with default parameters. It attempts to interpolate along the linear magnetic anomalies, reinforcing their continuity along strike, and tightening the anomalies across strike. The difference is quite dramatic.
The Angular Step parameter modifies the granularity of the trend direction processing and is measured in degrees. A smaller step may produce a more pleasing result, at the cost of slightly higher runtime.
The Cosine Order parameter sharpens the filter that is used to detect trends in the grid. A higher order may help enhance detection of lineaments and has no effect on performance.
The Operator Width parameter modifies the scale of the trend detection phase. A larger width detect broader trends, at the cost of slightly higher runtime.
The Directional Bias parameter modifies how strongly trends will be reinforced. A larger bias parameter will strengthen the trend reinforcement.
The Confidence Range parameters allow you to tune the algorithm to enhance either low confidence trends or high confidence trends. Specify the minimum confidence (below which there will be no trend reinforcement) and the maximum confidence (at which level maximum trend reinforcement is reached). Move the range towards zero to enhance lower confidence trends and move the range towards 100 to enhance higher confidence trends. A drop menu is provided with several default settings.
The Automated Trend Reinforcement algorithm is highly computation intensive. Now is the time to break out that AMD Threadripper data processing workstation! It is advisable to first tune the minimum curvature algorithm for performance, before activating trend reinforcement. You may also want to keep an eye on the Task Manager to monitor CPU performance.