51,642 research outputs found
Estimation of poroelastic parameters from seismograms using Biot theory
We investigate the possibility to extract information contained in seismic
waveforms propagating in fluid-filled porous media by developing and using a
full waveform inversion procedure valid for layered structures. To reach this
objective, we first solve the forward problem by implementing the Biot theory
in a reflectivity-type simulation program. We then study the sensitivity of the
seismic response of stratified media to the poroelastic parameters. Our
numerical tests indicate that the porosity and consolidation parameter are the
most sensitive parameters in forward and inverse modeling, whereas the
permeability has only a very limited influence on the seismic response. Next,
the analytical expressions of the sensitivity operators are introduced in a
generalized least-square inversion algorithm based on an iterative modeling of
the seismic waveforms. The application of this inversion procedure to synthetic
data shows that the porosity as well as the fluid and solid parameters can be
correctly reconstructed as long as the other parameters are well known.
However, the strong seismic coupling between some of the model parameters makes
it difficult to fully characterize the medium by a multi-parameter inversion
scheme. One solution to circumvent this difficulty is to combine several model
parameters according to rock physics laws to invert for composite parameters.
Another possibility is to invert the seismic data for the perturbations of the
medium properties, such as those resulting from a gas injection
Beyond the quantum formalism: consequences of a neural-oscillator model to quantum cognition
In this paper we present a neural oscillator model of stimulus response
theory that exhibits quantum-like behavior. We then show that without adding
any additional assumptions, a quantum model constructed to fit observable
pairwise correlations has no predictive power over the unknown triple moment,
obtainable through the activation of multiple oscillators. We compare this with
the results obtained in de Barros (2013), where a criteria of rationality gives
optimal ranges for the triple moment.Comment: 4 pages; to appear in the Advances in Cognitive Neurodynamics,
Proceedings of the 4th International Conference on Cognitive Neurodynamics -
201
The Surface Laplacian Technique in EEG: Theory and Methods
This paper reviews the method of surface Laplacian differentiation to study
EEG. We focus on topics that are helpful for a clear understanding of the
underlying concepts and its efficient implementation, which is especially
important for EEG researchers unfamiliar with the technique. The popular
methods of finite difference and splines are reviewed in detail. The former has
the advantage of simplicity and low computational cost, but its estimates are
prone to a variety of errors due to discretization. The latter eliminates all
issues related to discretization and incorporates a regularization mechanism to
reduce spatial noise, but at the cost of increasing mathematical and
computational complexity. These and several others issues deserving further
development are highlighted, some of which we address to the extent possible.
Here we develop a set of discrete approximations for Laplacian estimates at
peripheral electrodes and a possible solution to the problem of multiple-frame
regularization. We also provide the mathematical details of finite difference
approximations that are missing in the literature, and discuss the problem of
computational performance, which is particularly important in the context of
EEG splines where data sets can be very large. Along this line, the matrix
representation of the surface Laplacian operator is carefully discussed and
some figures are given illustrating the advantages of this approach. In the
final remarks, we briefly sketch a possible way to incorporate finite-size
electrodes into Laplacian estimates that could guide further developments.Comment: 43 pages, 8 figure
Decision Making for Inconsistent Expert Judgments Using Negative Probabilities
In this paper we provide a simple random-variable example of inconsistent
information, and analyze it using three different approaches: Bayesian,
quantum-like, and negative probabilities. We then show that, at least for this
particular example, both the Bayesian and the quantum-like approaches have less
normative power than the negative probabilities one.Comment: 14 pages, revised version to appear in the Proceedings of the QI2013
(Quantum Interactions) conferenc
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