14 research outputs found
Seeing through the CO2 plume: joint inversion-segmentation of the Sleipner 4D Seismic Dataset
4D seismic inversion is the leading method to quantitatively monitor fluid
flow dynamics in the subsurface, with applications ranging from enhanced oil
recovery to subsurface CO2 storage. The process of inverting seismic data for
reservoir properties is, however, a notoriously ill-posed inverse problem due
to the band-limited and noisy nature of seismic data. This comes with
additional challenges for 4D applications, given inaccuracies in the
repeatability of the time-lapse acquisition surveys. Consequently, adding prior
information to the inversion process in the form of properly crafted
regularization terms is essential to obtain geologically meaningful subsurface
models. Motivated by recent advances in the field of convex optimization, we
propose a joint inversion-segmentation algorithm for 4D seismic inversion,
which integrates Total-Variation and segmentation priors as a way to counteract
the missing frequencies and noise present in 4D seismic data. The proposed
inversion framework is applied to a pair of surveys from the open Sleipner 4D
Seismic Dataset. Our method presents three main advantages over
state-of-the-art least-squares inversion methods: 1. it produces
high-resolution baseline and monitor acoustic models, 2. by leveraging
similarities between multiple data, it mitigates the non-repeatable noise and
better highlights the real time-lapse changes, and 3. it provides a volumetric
classification of the acoustic impedance 4D difference model (time-lapse
changes) based on user-defined classes. Such advantages may enable more robust
stratigraphic and quantitative 4D seismic interpretation and provide more
accurate inputs for dynamic reservoir simulations. Alongside our novel
inversion method, in this work, we introduce a streamlined data pre-processing
sequence for the 4D Sleipner post-stack seismic dataset, which includes
time-shift estimation and well-to-seismic tie.Comment: This paper proposes a novel algorithm to jointly regularize a 4D
seismic inversion problem and segment the 4D difference volume into
percentages of acoustic impedance changes. We validate our algorithm with the
4D Sleipner seismic dataset. Furthermore, this paper comprehensively explains
the data preparation workflow for 4D seismic inversio
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
Many areas of machine learning and science involve large linear algebra
problems, such as eigendecompositions, solving linear systems, computing matrix
exponentials, and trace estimation. The matrices involved often have Kronecker,
convolutional, block diagonal, sum, or product structure. In this paper, we
propose a simple but general framework for large-scale linear algebra problems
in machine learning, named CoLA (Compositional Linear Algebra). By combining a
linear operator abstraction with compositional dispatch rules, CoLA
automatically constructs memory and runtime efficient numerical algorithms.
Moreover, CoLA provides memory efficient automatic differentiation, low
precision computation, and GPU acceleration in both JAX and PyTorch, while also
accommodating new objects, operations, and rules in downstream packages via
multiple dispatch. CoLA can accelerate many algebraic operations, while making
it easy to prototype matrix structures and algorithms, providing an appealing
drop-in tool for virtually any computational effort that requires linear
algebra. We showcase its efficacy across a broad range of applications,
including partial differential equations, Gaussian processes, equivariant model
construction, and unsupervised learning.Comment: Code available at https://github.com/wilson-labs/col
Learned multiphysics inversion with differentiable programming and machine learning
We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM)
open-source software framework for computational geophysics and, more
generally, inverse problems involving the wave-equation (e.g., seismic and
medical ultrasound), regularization with learned priors, and learned neural
surrogates for multiphase flow simulations. By integrating multiple layers of
abstraction, our software is designed to be both readable and scalable. This
allows researchers to easily formulate their problems in an abstract fashion
while exploiting the latest developments in high-performance computing. We
illustrate and demonstrate our design principles and their benefits by means of
building a scalable prototype for permeability inversion from time-lapse
crosswell seismic data, which aside from coupling of wave physics and
multiphase flow, involves machine learning
Seismic Noise Interferometry and Distributed Acoustic Sensing (DAS): Inverting for the Firn Layer S ‐Velocity Structure on Rutford Ice Stream, Antarctica
Firn densification profiles are an important parameter for ice-sheet mass balance and palaeoclimate studies. One conventional method of investigating firn profiles is using seismic refraction surveys, but these are difficult to upscale to large-area measurements. Distributed acoustic sensing (DAS) presents an opportunity for large-scale seismic measurements of firn with dense spatial sampling and easy deployment, especially when seismic noise is used. We study the feasibility of seismic noise interferometry (SI) on DAS data for characterizing the firn layer at the Rutford Ice Stream, West Antarctica. Dominant seismic energy appears to come from anthropogenic noise and shear-margin crevasses. The DAS cross-correlation interferometry yields noisy Rayleigh wave signals. To overcome this, we present two strategies for cross-correlations: (a) hybrid instruments—correlating a geophone with DAS, and (b) stacking of selected cross-correlation panels picked in the tau-p domain. These approaches are validated with results derived from an active survey. Using the retrieved Rayleigh wave dispersion curve, we inverted for a high-resolution 1D S-wave velocity profile down to a depth of 100 m. The profile shows a “kink” (velocity gradient inflection) at ∼12 m depth, resulting from a change of compaction mechanism. A triangular DAS array is used to investigate directional variation in velocity, which shows no evident variations thus suggesting a lack of azimuthal anisotropy in the firn. Our results demonstrate the potential of using DAS and SI to image the near-surface and present a new approach to derive S-velocity profiles from surface wave inversion in firn studies
The Effects of Spatial Interpolation on a Novel, Dual-Doppler 3D Wind Retrieval Technique
Three-dimensional wind retrievals from ground-based Doppler radars have
played an important role in meteorological research and nowcasting over the
past four decades. However, in recent years, the proliferation of open-source
software and increased demands from applications such as convective
parameterizations in numerical weather prediction models has led to a renewed
interest in these analyses. In this study, we analyze how a major, yet
often-overlooked, error source effects the quality of retrieved 3D wind fields.
Namely, we investigate the effects of spatial interpolation, and show how the
common practice of pre-gridding radial velocity data can degrade the accuracy
of the results. Alternatively, we show that assimilating radar data directly at
their observation locations improves the retrieval of important dynamic
features such as the rear flank downdraft and mesocyclone within a simulated
supercell, while also reducing errors in vertical vorticity, horizontal
divergence, and all three velocity components.Comment: Revised version submitted to JTECH. Includes new section with a real
data cas