Robust Hydraulic Fracture Monitoring (HFM) of Multiple Time Overlapping Events Using a Generalized Discrete Radon Transform

In this work we propose a novel algorithm for multiple-event localization for Hydraulic Fracture Monitoring (HFM) through the exploitation of the sparsity of the observed seismic signal when represented in a basis consisting of space time propagators. We provide explicit construction of these propagators using a forward model for wave propagation which depends non-linearly on the problem parameters - the unknown source location and mechanism of fracture, time and extent of event, and the locations of the receivers. Under fairly general assumptions and an appropriate discretization of these parameters we first build an over-complete dictionary of generalized Radon propagators and assume that the data is well represented as a linear superposition of these propagators. Exploiting this structure we propose sparsity penalized algorithms and workflow for super-resolution extraction of time overlapping multiple seismic events from single well data

Longwall mining-induced fracture characterisation based on seismic monitoring

Despite several technological advancements, mining-induced fractures are still critical for the safety of underground coal mines. Rocking fracturing as a natural response to mining activities can pose a potential hazard to mine operators, equipment, and infrastructures. The fractures occur not only around the working face that can be visually measured but also above and in front of the working face and where geological structures are affected by mining activities. Therefore, it is of importance to detect and investigate the properties of mining-induced fractures. Mining-induced seismicity has been generated due to rock fracturing during progressive mining activities and can provide critical fracture information. Currently, the application of using seismic monitoring to characterise fractures has remained relatively challenged in mining because mining-induced fractures are initiated by stress change and strata movement after mineral extraction. Compared to seismic monitoring in the oil and gas industry, the fractures and seismic responses may show different characteristics. Therefore, seismic monitoring in mines lacks a comprehensive investigation of received seismic signals to the properties of induced fractures and the effect on mine workings by these fractures. Additionally, constraints such as the quality of seismic signals and the deficiency of correlation analysis of seismic events in underground mining pose great challenges in using seismic data for hazard prediction. This thesis aims to address these challenges in using seismic monitoring to understand and characterise mining-induced fractures by (1) calculating fracture properties related to seismic source location, magnitude and mechanism based on uniaxial seismic data, (2) spatial and temporal correlation analysis of seismic events, and (3) inspecting fracture distributions and simulation of the fractured zone in longwall coal mines. Firstly, since cheap and easily removable uniaxial geophones close to production areas are preferable in coal mines, a novel method to use uniaxial signal and moment tensor inversion to generate synthetic triaxial waves is designed for a comprehensive description of the fracture properties, including location, radius, aperture and orientation. Secondly, to apply seismic data for advanced analysis, such as rockburst prediction and caving assessment, the correlation of seismic events is proved to be quantitatively assessable, and their correlations may vary throughout the mineral extraction process. The spatial and temporal correlation of seismic event energy is quantitatively analysed using various statistical methods, including autocorrelation function (ACF), semivariogram and Moran's I analysis. In addition, based on the integrated spatial-temporal (ST) correlation assessment, seismic events are further classified into seven clusters to assess the correlations within individual clusters. Finally, several source parameters such as seismic moment (M0), seismic source radius (R), fracture aperture (τ), failure type and fracture orientation were used to characterise fractures induced by longwall mining. This thesis also presents the fracture patterns induced caused progressive longwall mining for the first time. Besides, a discrete element method (DEM) model with seismic-derived fractures is generated and proves the impact of mining-induced fractures on altering stress conditions during mineral extraction. In addition, with the analysis of the seismic source mechanism and a synthetic triaxial method, a discrete fracture network (DFN) is generated from monitored seismic events to restore complete induced fractures. Overall, the outcomes of this study lead to a comprehensive assessment of mining-induced fracture properties based on real-time seismic monitoring, demonstrating its significant potential for hazard prediction and improving the safety of resource recovery

The Large-Scale Polarization Explorer (LSPE)

The LSPE is a balloon-borne mission aimed at measuring the polarization of the Cosmic Microwave Background (CMB) at large angular scales, and in particular to constrain the curl component of CMB polarization (B-modes) produced by tensor perturbations generated during cosmic inflation, in the very early universe. Its primary target is to improve the limit on the ratio of tensor to scalar perturbations amplitudes down to r = 0.03, at 99.7% confidence. A second target is to produce wide maps of foreground polarization generated in our Galaxy by synchrotron emission and interstellar dust emission. These will be important to map Galactic magnetic fields and to study the properties of ionized gas and of diffuse interstellar dust in our Galaxy. The mission is optimized for large angular scales, with coarse angular resolution (around 1.5 degrees FWHM), and wide sky coverage (25% of the sky). The payload will fly in a circumpolar long duration balloon mission during the polar night. Using the Earth as a giant solar shield, the instrument will spin in azimuth, observing a large fraction of the northern sky. The payload will host two instruments. An array of coherent polarimeters using cryogenic HEMT amplifiers will survey the sky at 43 and 90 GHz. An array of bolometric polarimeters, using large throughput multi-mode bolometers and rotating Half Wave Plates (HWP), will survey the same sky region in three bands at 95, 145 and 245 GHz. The wide frequency coverage will allow optimal control of the polarized foregrounds, with comparable angular resolution at all frequencies.Comment: In press. Copyright 2012 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibite

An overview of signal processing issues in chemical sensing

International audienceThis tutorial paper aims at summarizing some problems, ranging from analytical chemistry to novel chemical sensors, that can be addressed with classical or advanced methods of signal and image processing. We gather them under the denomination of "chemical sensing". It is meant to introduce the special session "Signal Processing for Chemical Sensing" with a large overview of issues which have been and remain to be addressed in this application domain, including chemical analysis leading to PARAFAC/tensor methods, hyper spectral imaging, ion-sensitive sensors, artificial nose, chromatography, mass spectrometry, etc. For enlarging and illustrating the points of view of this tutorial, the invited papers of the session consider other applications (NMR, Raman spectroscopy, recognition of explosive compounds, etc.) addressed by various methods, e.g. source separation, Bayesian, and exploiting typical chemical signal priors like positivity, linearity, unit-concentration or sparsity

Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression

To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscillatory brain patterns were determined by permutation correlation analysis between individual time courses of Fourier-ICA components and musical features. We found that (1) three components, including a beta sensorimotor network, a beta auditory network and an alpha medial visual network, were involved in music processing among most healthy subjects; and that (2) one alpha lateral component located in the left angular gyrus was engaged in music perception in most individuals with MDD. The proposed method allowed the statistical group comparison, and we found that: (1) the alpha lateral component was activated more strongly in healthy subjects than in the MDD individuals, and that (2) the derived frequency-dependent networks of musical feature processing seemed to be altered in MDD participants compared to healthy subjects. The proposed pipeline appears to be valuable for studying disrupted brain oscillations in psychiatric disorders during naturalistic paradigms.Peer reviewe

Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods