Inverting geodetic time series with a principal component analysis-based inversion method

The Global Positioning System (GPS) system now makes it possible to monitor deformation of the Earth's surface along plate boundaries with unprecedented accuracy. In theory, the spatiotemporal evolution of slip on the plate boundary at depth, associated with either seismic or aseismic slip, can be inferred from these measurements through some inversion procedure based on the theory of dislocations in an elastic half-space. We describe and test a principal component analysis-based inversion method (PCAIM), an inversion strategy that relies on principal component analysis of the surface displacement time series. We prove that the fault slip history can be recovered from the inversion of each principal component. Because PCAIM does not require externally imposed temporal filtering, it can deal with any kind of time variation of fault slip. We test the approach by applying the technique to synthetic geodetic time series to show that a complicated slip history combining coseismic, postseismic, and nonstationary interseismic slip can be retrieved from this approach. PCAIM produces slip models comparable to those obtained from standard inversion techniques with less computational complexity. We also compare an afterslip model derived from the PCAIM inversion of postseismic displacements following the 2005 8.6 Nias earthquake with another solution obtained from the extended network inversion filter (ENIF). We introduce several extensions of the algorithm to allow statistically rigorous integration of multiple data sources (e.g., both GPS and interferometric synthetic aperture radar time series) over multiple timescales. PCAIM can be generalized to any linear inversion algorithm

Triggering of the 2014 M_w7.3 Papanoa earthquake by a slow slip event in Guerrero, Mexico

Since their discovery two decades ago, slow slip events have been shown to play an important role in accommodating strain in subduction zones. However, the physical mechanisms that generate slow slip and the relationships with earthquakes are unclear. Slow slip events have been recorded in the Guerrero segment of the Cocos–North America subduction zone. Here we use inversion of position time series recorded by a continuous GPS network to reconstruct the evolution of aseismic slip on the subduction interface of the Guerrero segment. We find that a slow slip event began in February 2014, two months before the magnitude (M_w) 7.3 Papanoa earthquake on 18 April. The slow slip event initiated in a region adjacent to the earthquake hypocentre and extended into the vicinity of the seismogenic zone. This spatio-temporal proximity strongly suggests that the Papanoa earthquake was triggered by the ongoing slow slip event. We demonstrate that the triggering mechanism could be either static stress increases in the hypocentral region, as revealed by Coulomb stress modelling, or enhanced weakening of the earthquake hypocentral area by the slow slip. We also show that the plate interface in the Guerrero area is highly coupled between slow slip events, and that most of the accumulated strain is released aseismically during the slow slip episodes

On the relationship between offshore geodetic coverage and slip model uncertainty: Analog megathrust earthquake case studies.

We apply a geodetic slip inversion technique to analog subduction megathrust earthquakes to demonstrate how limited offshore geodetic coverage affects coseismic slip models. We analyzed two archetypical megathrust earthquakes: trench‐breaking and non‐trench‐breaking earthquakes. Slip inversion models of analog earthquakes show quantitative and qualitative changes as a function of offshore coverage. Shallow slip cannot be resolved if the observation coverage of the offshore segment is <50%. Moreover, the slip pattern of shallow event flips from landward to trenchward skewed as offshore coverage reduces to <40%. The estimated slip for both event types converges to a similar unimodal pattern when there is no offshore coverage. We infer 5‐20% slip overestimation when the observations are above the high slipping zone during trench‐breaking events versus 5‐10% underestimation during non‐trench‐breaking events if observations are land‐limited. The moment magnitude derived for trench‐breaking ruptures might be significantly affected (ΔM w ~ 0.5)

MussaIvaldi, “Dynamical dimension of a hybrid neurorobotic system

Abstract—The goal of this work is to understand how neural tissue can be programmed so as to execute pre-determined functions. We developed a research tool that includes the brainstem of a lamprey and a two-wheeled robot interconnected in a closed loop. The main achievement reported here is the development of a framework for studying the dynamics of the neural tissue based on the interaction of this tissue with the robot. Index Terms—brain-machine interface, dynamical dimension, neurocontrollers

Computational analysis in vitro: dynamics and plasticity of a neuro-robotic system

When the brain interacts with the environment it constantly adapts by representing the environment in a form that is called an internal model. The neurobiological basis for internal models is provided by the connectivity and the dynamical properties of neurons. Thus, the interactions between neural tissues and external devices provide a fundamental means for investigating the connectivity and dynamical properties of neural populations. We developed this idea, suggested in the 1980s by Valentino Braitenberg, for investigating and representing the dynamical behavior of neuronal populations in the brainstem of the lamprey. The brainstem was maintained in vitro and connected in a closed loop with two types of artificial device: (a) a simulated dynamical system and (b) a small mobile robot. In both cases, the device was controlled by recorded extracellular signals and its output was translated into electrical stimuli delivered to the neural system. The goal of the first study was to estimate the dynamical dimension of neural preparation in a single-input/single-output configuration. The dynamical dimension is the number of state variables that together with the applied input determine the output of a system. The results indicate that while this neural system has significant dynamical properties, its effective complexity, as established by the dynamical dimension, is rather moderate. In the second study, we considered a more specific situation, in which the same portion of the nervous system controls a robotic device in a two-input/two-output configuration. We fitted the input-output data from the neuro-robotic preparation to neural network models having different internal dynamics and we observed the generalization error of each model. Consistent with the first study, this second experiment showed that a simple recurrent dynamical model was able to capture the behavior of the hybrid system. This experimental and computational framework provides the means for investigating neural plasticity and internal representations in the context of brain-machine interfaces