Adaptive time-frequency detection and filtering for imaging in heavy clutter

Abstract. We introduce an adaptive approach for the detection of a reflector in a strongly scattering medium using a timefrequency representation of the array response matrix followed by a Singular Value Decomposition (SVD). We use the Local Cosine Transform (LCT) for the time-frequency representation and introduce a detection criterion that identifies anomalies in the top singular values, across frequencies and in different time windows, that are due to the reflector. The detection is adaptive because the time windows that contain the primary echoes from the reflector are not determined in advance. Their location and width is identified by searching through the time-frequency binary tree of the LCT. After detecting the presence of the reflector we filter the array response matrix to retain information only in the time windows that have been selected. We also project the filtered array response matrix to the subspace associated with the top singular value and then image using travel time migration. We show with extensive numerical simulations that this approach to detection and imaging works well in heavy clutter that is calibrated using random matrix theory so as to simulate regimes close to the experiments in [3]. While the detection and filtering algorithm presented here works well in general clutter it has been analyzed theoretically only for the case of randomly layered media [1]

Robust seismic velocity change estimation using ambient noise recordings

We consider the problem of seismic velocity change estimation using ambient noise recordings. Motivated by [23] we study how the velocity change estimation is affected by seasonal fluctuations in the noise sources. More precisely, we consider a numerical model and introduce spatio-temporal seasonal fluctuations in the noise sources. We show that indeed, as pointed out in [23], the stretching method is affected by these fluctuations and produces misleading apparent velocity variations which reduce dramatically the signal to noise ratio of the method. We also show that these apparent velocity variations can be eliminated by an adequate normalization of the cross-correlation functions. Theoretically we expect our approach to work as long as the seasonal fluctuations in the noise sources are uniform, an assumption which holds for closely located seismic stations. We illustrate with numerical simulations and real measurements that the proposed normalization significantly improves the accuracy of the velocity change estimation

Enhanced statistical stability in coherent interferometric imaging

http://iopscience.iop.org/0266-5611/International audienc

Filtering Deterministic Layer Effects in Imaging

Sensor array imaging arises in applications such as nondestructive evaluation of materials with ultrasonic waves, seismic exploration, and radar. The sensors probe a medium with signals and record the resulting echoes, which are then processed to determine the location and reflectivity of remote reflectors. These could be defects in materials such as voids, fault lines or salt bodies in the earth, and cars, buildings, or aircraft in radar applications. Imaging is relatively well understood when the medium through which the signals propagate is smooth, and therefore nonscattering. But in many problems the medium is heterogeneous, with numerous small inhomogeneities that scatter the waves. We refer to the collection of inhomogeneities as clutter, which introduces an uncertainty in imaging because it is unknown and impossible to estimate in detail. We model the clutter as a random process. The array data is measured in one realization of the random medium, and the challenge is to mitigate cumulative clutter scattering so as to obtain robust images that are statistically stable with respect to different realizations of the inhomogeneities. Scatterers that are not buried too deep in clutter can be imaged reliably with the coherent interferometric (CINT) approach. But in heavy clutter the signal-to-noise ratio (SNR) is low and CINT alone does not work. The “signal,” the echoes from the scatterers to be imaged, is overwhelmed by the “noise,” the strong clutter reverberations. There are two existing approaches for imaging at low SNR: The first operates under the premise that data are incoherent so that only the intensity of the scattered field can be used. The unknown coherent scatterers that we want to image are modeled as changes in the coefficients of diffusion or radiative transport equations satisfied by the intensities, and the problem becomes one of parameter estimation. Because the estimation is severely ill-posed, the results have poor resolution, unless very good prior information is available and large arrays are used. The second approach recognizes that if there is some residual coherence in the data, that is, some reliable phase information is available, it is worth trying to extract it and use it with well-posed coherent imaging methods to obtain images with better resolution. This paper takes the latter approach and presents a first attempt at enhancing the SNR of the array data by suppressing medium reverberations. It introduces filters, or annihilators of layer backscatter, that are designed to remove primary echoes from strong, isolated layers in a medium with additional random layering at small, subwavelength scales. These strong layers are called deterministic because they can be imaged from the data. However, our goal is not to image the layers, but to suppress them and thus enhance the echoes from compact scatterers buried deep in the medium. Surprisingly, the layer annihilators work better than intended, in the sense that they suppress not only the echoes from the deterministic layers, but also multiply scattered ones in the randomly layered structure. Following the layer annihilators presented here, other filters of general, nonlayered heavy clutter have been developed. We review these more recent developments and the challenges of imaging in heavy clutter in the introduction in order to place the research presented here in context. We then present in detail the layer annihilators and show with analysis and numerical simulations how they work

Experience recruits MSK1 to expand the dynamic range of synapses and enhance cognition

Experience powerfully influences neuronal function and cognitive performance, but the cellular and molecular events underlying the experience-dependent enhancement of mental ability haveremained elusive. In particular, the mechanisms that couple the external environment to the genomic changes underpinning this improvement are unknown. To address this we have used male mice harbouring an inactivating mutation of mitogen- and stress-activated protein kinase 1 (MSK1), a BDNF-activated enzyme downstream of the MAPK pathway. We show that MSK1 is required for the full extent of experience-induced improvement of spatial memory, for the expansion of the dynamic range of synapses, exemplified by the enhancement of hippocampal LTP and LTD, and for the regulation of the majority of genes influenced by enrichment. In addition, and unexpectedly, we show that experience is associated with an MSK1-dependent downregulation of key MAPK and plasticity related genes, notably of EGR1/Zif268 and Arc/Arg3.1, suggesting the establishment of a novel genomic landscape adapted to experience. By coupling experience to homeostatic changes in gene expression MSK1, represents a prime mechanism through which the external environment has an enduring influence on gene expression, synaptic function and cognition

Detection and imaging in strongly backscattering randomly layered media

Abstract. Echoes from small reflectors buried in heavy clutter are weak and difficult to distinguish from the medium backscatter. Detection and imaging with sensor arrays in such media requires filtering out the unwanted backscatter and enhancing the echoes from the reflectors that we wish to locate. We consider a filtering and detection approach based on the singular value decomposition of the local cosine transform of the array response matrix. The algorithm is general and can be used for detection and imaging in heavy clutter, but its analysis depends on the model of the cluttered medium. This paper is concerned with the analysis of the algorithm in finely layered random media. We obtain a detailed characterization of the singular values of the transformed array response matrix and justify the systematic approach of the filtering algorithm for detecting and refining the time windows that contain the echoes that are useful in imaging

MSK1 is required for the beneficial synaptic and cognitive effects of enriched experience across the lifespan

Positive experiences, such as social interaction, cognitive training and physical exercise, have been shown to ameliorate some of the harms to cognition associated with ageing. Animal models of positive interventions, commonly known as environmental enrichment, strongly influence neuronal morphology and synaptic function and enhance cognitive performance. While the profound structural and functional benefits of enrichment have been appreciated for decades, little is known as to how the environment influences neurons to respond and adapt to these positive sensory experiences. We show that adult and aged male wild-type mice that underwent a 10-week environmental enrichment protocol demonstrated improved performance in a variety of behavioural tasks, including those testing spatial working and spatial reference memory, and an enhancement in hippocampal LTP. Aged animals in particular benefitted from enrichment, performing spatial memory tasks at levels similar to healthy adult mice. Many of these benefits, including in gene expression, were absent in mice with a mutation in an enzyme, MSK1, which is activated by BDNF, a growth factor implicated in rodent and human cognition. We conclude that enrichment is beneficial across the lifespan and that MSK1 is required for the full extent of these experience-induced improvements of cognitive abilities, synaptic plasticity and gene expression