Observations of shallow fault creep reveal increasingly complex time‐dependent slip histories that include quasi‐steady creep and triggered as well as spontaneous accelerated slip events. Here we report a recent slow slip event on the southern San Andreas fault triggered by the 2017 Mw8.2 Chiapas (Mexico) earthquake that occurred 3,000 km away. Geodetic and geologic observations indicate that surface slip on the order of 10 mm occurred on a 40‐km‐long section of the southern San Andreas fault between the Mecca Hills and Bombay Beach, starting minutes after the Chiapas earthquake and continuing for more than a year. Both the magnitude and the depth extent of creep vary along strike. We derive a high‐resolution map of surface displacements by combining Sentinel‐1 Interferometric Synthetic Aperture Radar acquisitions from different lines of sight. Interferometric Synthetic Aperture Radar‐derived displacements are in good agreement with the creepmeter data and field mapping of surface offsets. Inversions of surface displacement data using dislocation models indicate that the highest amplitudes of surface slip are associated with shallow (<1 km) transient slip. We performed 2‐D simulations of shallow creep on a strike‐slip fault obeying rate‐and‐state friction to constrain frictional properties of the top few kilometers of the upper crust that can produce the observed behavior.
A concept design of a fuzzy Dead Reckoning (DR) algorithm for a personal navigator (PN) is introduced here. The PN system prototype includes a range of self‐contained sensors such as GPS, accelerometer, gyroscope, magnetometer, digital barometer, and step sensors. In addition, a human locomotion model is considered as a navigation sensor, with the step length (SL) and step direction (SD) as primary parameters. The major focus of this paper is on DR navigation supported by human dynamics during GPS signal blockages. It is demonstrated that in the absence of GPS, the other sensors can sense the body locomotion in terms of its dynamics and geometry that represent an implicit function of SL and SD. A practical implementation of the DR system based on human dynamics is a fuzzy logic Knowledge‐Based System (KBS). This paper discusses the design and implementation of the KBS, followed by its performance evaluation in the indoor environments.
The primary objective of the research presented here is to develop theoretical foundations and implementation algorithms, which integrate the Global Positioning System (GPS), micro-electromechanical inertial measurement unit (MEMS IMU), digital barometer, electronic compass, and human pedometry to provide navigation and tracking of military and rescue ground personnel. This paper discusses the design, implementation and the performance analyses of the personal navigator prototype, with a special emphasis on dead-reckoning (DR) navigation supported by the human locomotion model. The adaptive knowledge system, based on the Artificial Neural Networks (ANN), is implemented to support this functionality. The knowledge system is trained during the GPS signal reception and is used to support navigation under GPS-denied conditions. The human locomotion parameters, step frequency (SF) and step length (SL), are extracted from GPS-timed impact switches (step frequency) and GPS/IMU data (step length), respectively, during the system calibration period. SL is correlated with several data types, such as acceleration, acceleration variation, SF, terrain slope, etc. that constitute the input parameters to the ANN-based knowledge system. The ANN-predicted SL, together with the heading information from the compass and gyro, support DR navigation. The current target accuracy of the system is 3-5 m CEP (circular error probable) 50%.
The prototype of a personal navigator, which integrates Global Positioning System (GPS), tactical grade inertial measurement unit (IMU), digital barometer, magnetometer, and human pedometry to support navigation and tracking of military and rescue ground personnel has been developed at The Ohio State University Satellite Positioning and Inertial Navigation (SPIN) Laboratory. This paper discusses the design, implementation and performance assessment of the prototype, with a special emphasis on dead-reckoning (DR) navigation supported by a human locomotion model. The primary components of the human locomotion model are step frequency (SF), extracted from GPS-timed impact micro-switches placed on the shoe soles of the operator, step length (SL), and step direction (SD), both determined by predictive models derived by the adaptive knowledge based system (KBS). SL KBS is based on Artificial Neural Networks (ANN) and Fuzzy Logic (FL), and is trained a priori using sensory data collected by various operators in various environments during GPS signal reception. An additional KBS module, in the form of a Kalman Filter (KF), isused to improve the heading information (SD) available from the magnetometer and gyroscope under GPS-denied conditions, as well as to integrate the DR parameters to reconstruct the trajectory based on SL and SD. The current target accuracy of the system is 3-5 m CEP (circular error probable, 50%). This paper provides a performance analysis in the indoor and outdoor environments for two different operators. The system's navigation limitation in DR mode is tested in terms of time and trajectory length to determine the upper limit of indoor operation before the need for re-calibration.
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