Park, Simon S.Shor, Roman J.Liu, Huan2019-09-232019-09-232019-09-16Liu, H. (2019). Subsurface Sensing Through Data Fusion of Redundant IMU Sensors with Supervised Learning (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/111037In this thesis we obtain angular displacements using two different approaches to improve sensor robustness to magnetic and shock disturbances; also, we discuss the pros and cons of these two different approaches. The first approach is the supervised learning filter (SLF) approach, and the second is the supervised learning-Kalman filter (SL-KF) approach. In SLF, azimuth angle errors obtained from different sensors (magnetometers, accelerometers, and gyroscopes) are compared under magnetic and shock disturbance conditions; then, we employ an adaptive neuro fuzzy inference system (ANFIS) to calculate the error models of the sensors. Based on these sensors’ error models, the proper weights of the azimuth angles obtained from different sensors are computed and applied to the azimuth angles to output a final azimuth angle. However, to achieve the best results of SLF, we assume that at least one magnetometer is not affected by interferences at the same time interval (two magnetometers are separated by a distance D, and D can prevent both magnetometers from being affected by a magnetic disturbance at the same time). Therefore, SL-KF combines SLF with a KF to further reduce the effect of disturbances on sensors. SLF computes the corrected rotational angles and angular velocities that are subsequently fed into a global filter KF, which performs further corrections. The present subsurface positioning (directional drilling) relies on angular displacements and values of measurement depth (drill string length) to estimate a well path. However, these methods have limitations to apply in working conditions (for example drill string length maybe inaccurate caused by steel expands with increased temperature and stress). To deal with the drill string length inaccuracy problem, instead of using real external measurement signals (drill string length), we use correction signals designed based on the dual acceleration difference (DAD) method to correct the positions.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Supervised LearningDirectional DrillingMWDANFISMagnetic Disturbance RobustShock Impact RobustSubsurface SensingDual Acceleration Difference MethodKalman FilterApplied MechanicsArtificial IntelligenceEngineering--Mechanicaldoctoral thesishttp://dx.doi.org/10.11575/PRISM/37099