Localization of intraplate deformation through fluid-assisted faulting in the lower-crust: The Flinders Ranges, South Australia

In this paper we present a hypothesis for localized, intraplate deformation in the continental crust of south-central Australia that involves fluid-assisted reactivation of faults in the mid- to lower crust. Using data from a temporary seismometer deployment in the Flinders Ranges, we show that earthquakes, relocated in a 3D velocity model, cluster in elongated low vp /. vs anomalies that extend to depths exceeding 20. km, and are aligned with the axis of the Flinders Ranges. In the northern Flinders Ranges these low vp /. vs anomalies can be interpreted as fractured Neoproterozoic to Cambrian sediments that separate two cratonic blocks, the Gawler Craton to the west and the Curnamona Province in the east. Previous studies of Helium isotopes in springs to the north of the area provide evidence of mantle-derived fluids that may influence faulting at depth. Our focal mechanism and stress inversion results show a regionally compressive stress field that provides no evidence for stress concentration. We also argue that mechanisms for localized faulting such as thermal weakening and isostatic rebound also fail to account for the occurrence of earthquakes at mid- to lower crustal depth in this area of high heat flow and that the focused seismicity can only be explained by high pore fluid pressure in the lower crust

The Need for High-Fidelity Robotics Sensor Models

Simulations provide a safe, controlled setting for testing and are therefore ideal for rapidly developing and testing autonomous mobile robot behaviors. However, algorithms for mobile robots are notorious for transitioning poorly from simulations to fielded platforms. The difficulty can in part be attributed to the use of simplistic sensor models that do not recreate important phenomena that affect autonomous navigation. The differences between the output of simple sensor models and true sensors are highlighted using results from a field test exercise with the National Robotics Engineering Center's Crusher vehicle. The Crusher was manually driven through an area consisting of a mix of small vegetation, rocks, and hay bales. LIDAR sensor data was collected along the path traveled and used to construct a model of the area. LIDAR data were simulated using a simple point-intersection model for a second, independent path. Cost maps were generated by the Crusher autonomy system using both the real-world and simulated sensor data. The comparison of these cost maps shows consistencies on most solid, large geometry surfaces such as the ground, but discrepancies around vegetation indicate that higher fidelity models are required to truly capture the complex interactions of the sensors with complex objects