74 research outputs found
A prior regularized full waveform inversion using generative diffusion models
Full waveform inversion (FWI) has the potential to provide high-resolution
subsurface model estimations. However, due to limitations in observation, e.g.,
regional noise, limited shots or receivers, and band-limited data, it is hard
to obtain the desired high-resolution model with FWI. To address this
challenge, we propose a new paradigm for FWI regularized by generative
diffusion models. Specifically, we pre-train a diffusion model in a fully
unsupervised manner on a prior velocity model distribution that represents our
expectations of the subsurface and then adapt it to the seismic observations by
incorporating the FWI into the sampling process of the generative diffusion
models. What makes diffusion models uniquely appropriate for such an
implementation is that the generative process retains the form and dimensions
of the velocity model. Numerical examples demonstrate that our method can
outperform the conventional FWI with only negligible additional computational
cost. Even in cases of very sparse observations or observations with strong
noise, the proposed method could still reconstruct a high-quality subsurface
model. Thus, we can incorporate our prior expectations of the solutions in an
efficient manner. We further test this approach on field data, which
demonstrates the effectiveness of the proposed method
Artificial neural networks as emerging tools for earthquake detection
As seismic networks continue to spread and monitoring sensors become more ef¿cient, the abundance of data highly surpasses the processing capabilities of earthquake interpretation analysts. Earthquake catalogs are fundamental for fault system studies, event modellings, seismic hazard assessment, forecasting, and ultimately, for mitigating the seismic risk. These have fueled the research for the automation of interpretation tasks such as event detection, event identi¿cation, hypocenter location, and source mechanism analysis. Over the last forty years, traditional algorithms based on quantitative analyses of seismic traces in the time or frequency domain, have been developed to assist interpretation. Alternatively, recentadvancesarerelatedtotheapplicationofArti¿cial Neural Networks (ANNs), a subset of machine learning techniques that is pushing the state-of-the-art forward in many areas. Appropriated trained ANN can mimic the interpretation abilities of best human analysts, avoiding the individual weaknesses of most traditional algorithms, and spending modest computational resources at the operational stage. In this paper, we will survey the latest ANN applications to the automatic interpretation of seismic data, with a special focus on earthquake detection, and the estimation of onset times. For a comparative framework, we give an insight into the labor of human interpreters, who may face uncertainties in the case of small magnitude earthquakes.Peer ReviewedPostprint (published version
Sparse Approximation and Dictionary Learning with Applications to Audio Signals
PhDOver-complete transforms have recently become the focus of a wide wealth of research in
signal processing, machine learning, statistics and related fields. Their great modelling
flexibility allows to find sparse representations and approximations of data that in turn
prove to be very efficient in a wide range of applications. Sparse models express signals as
linear combinations of a few basis functions called atoms taken from a so-called dictionary.
Finding the optimal dictionary from a set of training signals of a given class is the objective
of dictionary learning and the main focus of this thesis. The experimental evidence
presented here focuses on the processing of audio signals, and the role of sparse algorithms
in audio applications is accordingly highlighted.
The first main contribution of this thesis is the development of a pitch-synchronous
transform where the frame-by-frame analysis of audio data is adapted so that each frame
analysing periodic signals contains an integer number of periods. This algorithm presents
a technique for adapting transform parameters to the audio signal to be analysed, it
is shown to improve the sparsity of the representation if compared to a non pitchsynchronous
approach and further evaluated in the context of source separation by binary
masking.
A second main contribution is the development of a novel model and relative algorithm
for dictionary learning of convolved signals, where the observed variables are sparsely approximated
by the atoms contained in a convolved dictionary. An algorithm is devised to
learn the impulse response applied to the dictionary and experimental results on synthetic
data show the superior approximation performance of the proposed method compared to
a state-of-the-art dictionary learning algorithm.
Finally, a third main contribution is the development of methods for learning dictionaries
that are both well adapted to a training set of data and mutually incoherent. Two
novel algorithms namely the incoherent k-svd and the iterative projections and rotations
(ipr) algorithm are introduced and compared to different techniques published in the
literature in a sparse approximation context. The ipr algorithm in particular is shown
to outperform the benchmark techniques in learning very incoherent dictionaries while
maintaining a good signal-to-noise ratio of the representation
Sparse and Redundant Representations for Inverse Problems and Recognition
Sparse and redundant representation of data enables the
description of signals as linear combinations of a few atoms from
a dictionary. In this dissertation, we study applications of
sparse and redundant representations in inverse problems and
object recognition. Furthermore, we propose two novel imaging
modalities based on the recently introduced theory of Compressed
Sensing (CS).
This dissertation consists of four major parts. In the first part
of the dissertation, we study a new type of deconvolution
algorithm that is based on estimating the image from a shearlet
decomposition. Shearlets provide a multi-directional and
multi-scale decomposition that has been mathematically shown to
represent distributed discontinuities such as edges better than
traditional wavelets. We develop a deconvolution algorithm that
allows for the approximation inversion operator to be controlled
on a multi-scale and multi-directional basis. Furthermore, we
develop a method for the automatic determination of the threshold
values for the noise shrinkage for each scale and direction
without explicit knowledge of the noise variance using a
generalized cross validation method.
In the second part of the dissertation, we study a reconstruction
method that recovers highly undersampled images assumed to have a
sparse representation in a gradient domain by using partial
measurement samples that are collected in the Fourier domain. Our
method makes use of a robust generalized Poisson solver that
greatly aids in achieving a significantly improved performance
over similar proposed methods. We will demonstrate by experiments
that this new technique is more flexible to work with either
random or restricted sampling scenarios better than its
competitors.
In the third part of the dissertation, we introduce a novel
Synthetic Aperture Radar (SAR) imaging modality which can provide
a high resolution map of the spatial distribution of targets and
terrain using a significantly reduced number of needed transmitted
and/or received electromagnetic waveforms. We demonstrate that
this new imaging scheme, requires no new hardware components and
allows the aperture to be compressed. Also, it
presents many new applications and advantages which include strong
resistance to countermesasures and interception, imaging much
wider swaths and reduced on-board storage requirements.
The last part of the dissertation deals with object recognition
based on learning dictionaries for simultaneous sparse signal
approximations and feature extraction. A dictionary is learned
for each object class based on given training examples which
minimize the representation error with a sparseness constraint. A
novel test image is then projected onto the span of the atoms in
each learned dictionary. The residual vectors along with the
coefficients are then used for recognition. Applications to
illumination robust face recognition and automatic target
recognition are presented
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