Leaderboard

While many advancements have been made this last decade in automated classification of above surface features using remote sensing data, progress for detecting underground features has lagged in this area. Technologies for detecting features, including ground penetrating radar, electrical resistivity, and magnetometry exist, but methods for feature extraction and identification mostly depend on the experience of instrument user. One problem has been creating approaches that can deal with complex signals. Ground penetrating radar (GPR), for instance, often produces ambiguous signals that can have a lot different noise interference relative to the feature one wants to identify. One approach has been to apply approximation polynomials to classify given signals that are then inputs for an applied neural networks model using derived coefficients. This technique can help reduce noise and differentiate signals that follow clear patterns that vary from larger background signals. Differentiation of signals based on minimized coefficients are one way to simplify and better differentiate data signals.[1] Another approach is to use multilayer perceptron that has a nonlinear activation function which transforms the data. This is effectively a similar technique but uses different transform functions than other neural network models. Applications of this approach include being able to differentiate thickness of underground structures from surrounding sediments and soil.[2] Other methods have been developed to determine the best location to place source and receivers that can capture relevant data. In seismic research, the use of convolutional neural networks (CNNs) has been applied to determine better positioning of sensors so that better data quality can be achieved. This has resulted in very high precision and recall rates at over 0.99. Using a series of filtered layers, signals can be assessed for their data quality with that of manually placed instruments. The quality of the placement can also be compared to other locations to see if the overall signal capture improves. Thus, rather than focusing on mainly signal processing, this method also focuses on signal placement and capture that compares to other placements to optimize data capture locations.[3] One problem in geophysical data is inversion, where data points are interpreted to be the opposite of what they are due to a reflective signal that may hid the nature of the true data. Techniques using CNNs have also been developed whereby the patterning of data signals around a given inversion can be filtered and assessed using activation functions. Multiple layers that transform and reduce data to specific signals helps to identify where patterns of data suggest an inversion is likely, while checking if this follows patterns from other data using Bayesian learning techniques.[4] source: https://www.gislounge.com/automated-remote-sensing-of-underground-features/