Rock On: Advancements in Magnetic Bedload Monitoring

Abstract

Despite many years of vigorous research, there is still no reliable method of accurately monitoring bedload transport over long periods of time. This work describes an attempt to implement an accurate quantitative bedload monitoring system by making several improvements to a method of passively monitoring bedload transport using electromagnetic coil sensors. These modifications include changes to both the design of the hardware device used and software for signal processing and analysis with the aim of improving signal quality and reducing the total cost of the system. This novel data capture system was tested in laboratory conditions using a swing testing apparatus. The responses of multiple artificial and natural rock samples were measured and subsequently analysed using the novel tools developed as part of this work. The raw data produced showed similar characteristics to existing magnetic bedload sensing experiments, showing that the new monitoring system performs at a similar sensitivity and accuracy level to existing magnetic bedload monitoring systems with higher temporal resolution. Artificial Neural Networks were evaluated for use in both classification and regression tasks for data processing. Signal segmentation using a classification ANN showed promising initial results but requires further refinement to surpass existing integral-based segmentation methods. Prediction of individual rock volumes from magnetic signal properties were highly accurate for constrained experimental scenarios, but less so for an unconstrained multidimensional data set. Despite inaccuracy in individual predictions, cumulative bedload volume predictions over sets of multiple rocks were found to closely follow the actual recorded cumulative volumes. These results indicate that Artificial Neural Networks may perform better for cumulative bedload volume monitoring than existing models based on magnetic bedload monitoring data.