105 research outputs found
Microseismic source imaging using physics-informed neural networks with hard constraints
Microseismic source imaging plays a significant role in passive seismic
monitoring. However, such a process is prone to failure due to the aliasing
problem when dealing with sparse measured data. Thus, we propose a direct
microseismic imaging framework based on physics-informed neural networks
(PINNs), which can generate focused source images, even with very sparse
recordings. We use the PINNs to represent a multi-frequency wavefield and then
apply the inverse Fourier transform to extract the source image. Specially, we
modify the representation of the frequency-domain wavefield to inherently
satisfy the boundary conditions (the measured data on the surface) by means of
the hard constraint, which helps to avoid the difficulty in balancing the data
and PDE losses in PINNs. Furthermore, we propose the causality loss
implementation with respect to depth to enhance the convergence of PINNs. The
numerical experiments on the Overthrust model show that the method can admit
reliable and accurate source imaging for single- or multiple- sources and even
in passive monitoring settings. Then, we further apply our method on the
hydraulic fracturing field data, and demonstrate that our method can correctly
image the source
Robust data driven discovery of a seismic wave equation
Despite the fact that our physical observations can often be described by
derived physical laws, such as the wave equation, in many cases, we observe
data that do not match the laws or have not been described physically yet.
Therefore recently, a branch of machine learning has been devoted to the
discovery of physical laws from data. We test such discovery algorithms, with
our own flavor of implementation D-WE, in discovering the wave equation from
the observed spatial-temporal wavefields. D-WE first pretrains a neural network
(NN) in a supervised fashion to establish the mapping between the
spatial-temporal locations (x,y,z,t) and the observation displacement wavefield
function u(x,y,z,t). The trained NN serves to generate meta-data and provide
the time and spatial derivatives of the wavefield (e.g., u_tt and u_xx) by
automatic differentiation. Then, a preliminary library of potential terms for
the wave equation is optimized from an overcomplete library by using a genetic
algorithm. We, then, use a physics-informed information criterion to evaluate
the precision and parsimony of potential equations in the preliminary library
and determine the best structure of the wave equation. Finally, we train the
"physics-informed" neural network to identify the corresponding coefficients of
each functional term. Examples in discovering the 2D acoustic wave equation
validate the feasibility and effectiveness of D-WE. We also verify the
robustness of this method by testing it on noisy and sparsely acquired
wavefield data
Reflection Moveout Inversion For Horizontal Transverse Isotropy: Accuracy And Limitation
Horizontal transverse isotropy (HTI) is the simplest azimuthally anisotropic model used
to describe vertical fracturing in an isotropic matrix. Using the elliptical variation of
P-wave normal-moveout (NMO) velocity with azimuth, measured in three different
source-to-receiver orientations, we can obtain the vertical velocity V[subscript Pvert], anisotropy parameter δ[superscript (V)], and the azimuth a of the symmetry-axis plane.
Parameter estimation from variations in the moveout velocity in azimuthally anisotropic
media is quite sensitive to the angular separation between the survey lines in 2D, or
equivalently source-to-receiver azimuths in 3D, and to the set of azimuths used in the
inversion procedure. The accuracy in estimating the parameter α, in particular, is also
sensitive to the strength of anisotropy. The accuracy in resolving δ[superscript (V)] and [subscript Pvert] is about the same for any strength of anisotropy. In order to maximize the accuracy and stability in parameter estimation, it is best to have the azimuths for the three source-to- receiver directions 60° apart. In land seismic data acquisition having wide azimuthal coverage is quite feasible. In marine seismic data acquisition, however, where the azimuthal data coverage is limited, multiple survey directions are necessary to achieve such wide azimuthal coverage. Having more than three distinct source-to-receiver azimuths (e.g., full azimuthal coverage) provides useful data redundancy that enhances the quality of the estimates, and sets the stage for a least-square type of inversion in which the errors in the parameters estimates are minimized in a least-square sense. In layered azimuthally anisotropic media, applying Dix differentiation to obtain interval moveout velocity provides sufficient accuracy in the inversion for the medium parameters, especially where the direction of the symmetry planes is uniform. In order to obtain acceptable parameter estimates, an HTI layer overlain by an azimuthally isotropic overburden (as might happen for fractured reservoirs) should have a thickness (in time) relative to the total thickness. The total thickness should be equal to or greater than the ratio of the error in the NMO (stacking) velocity to the interval anisotropy strength of the fractured layer.Saudi AramcoMassachusetts Institute of Technology. Borehole Acoustics and Logging ConsortiumMassachusetts Institute of Technology. Earth Resources Laboratory. Reservoir Delineation
Consortiu
Joint Microseismic Event Detection and Location with a Detection Transformer
Microseismic event detection and location are two primary components in
microseismic monitoring, which offers us invaluable insights into the
subsurface during reservoir stimulation and evolution. Conventional approaches
for event detection and location often suffer from manual intervention and/or
heavy computation, while current machine learning-assisted approaches typically
address detection and location separately; such limitations hinder the
potential for real-time microseismic monitoring. We propose an approach to
unify event detection and source location into a single framework by adapting a
Convolutional Neural Network backbone and an encoder-decoder Transformer with a
set-based Hungarian loss, which is applied directly to recorded waveforms. The
proposed network is trained on synthetic data simulating multiple microseismic
events corresponding to random source locations in the area of suspected
microseismic activities. A synthetic test on a 2D profile of the SEAM Time
Lapse model illustrates the capability of the proposed method in detecting the
events properly and locating them in the subsurface accurately; while, a field
test using the Arkoma Basin data further proves its practicability, efficiency,
and its potential in paving the way for real-time monitoring of microseismic
events
Meta-Processing: A robust framework for multi-tasks seismic processing
Machine learning-based seismic processing models are typically trained
separately to perform specific seismic processing tasks (SPTs), and as a
result, require plenty of training data. However, preparing training data sets
is not trivial, especially for supervised learning (SL). Nevertheless, seismic
data of different types and from different regions share generally common
features, such as their sinusoidal nature and geometric texture. To learn the
shared features, and thus, quickly adapt to various SPTs, we develop a unified
paradigm for neural network-based seismic processing, called Meta-Processing,
that uses limited training data for meta learning a common network
initialization, which offers universal adaptability features. The proposed
Meta-Processing framework consists of two stages: meta-training and
meta-testing. In the meta-training stage, each SPT is treated as a separate
task and the training dataset is divided into support and query sets. Unlike
conventional SL methods, here, the neural network (NN) parameters are updated
by a bilevel gradient descent from the support set to the query set, iterating
through all tasks. In the meta-testing stage, we also utilize limited data to
fine-tune the optimized NN parameters in an SL fashion to conduct various SPTs,
such as denoising, interpolation, ground-roll attenuation, image enhancement,
and velocity estimation, aiming to converge quickly to ideal performance.
Comprehensive numerical examples are performed to evaluate the performance of
Meta-Processing on both synthetic and field data. The results demonstrate that
our method significantly improves the convergence speed and prediction accuracy
of the NN
Micro-seismic Elastic Reflection Full Waveform Inversion with An Equivalent Source
In micro-seismic event measurements, pinpointing the passive source's exact
spatial and temporal location is paramount. This research advocates for the
combined use of both P- and S-wave data, captured by geophone monitoring
systems, to improve source inversion accuracy. Drawing inspiration from the
secondary source concept in Elastic Reflection Full Waveform Inversion (ERFWI),
we introduce an equivalent source term. This term combines source functions and
source images. Our optimization strategy iteratively refines the spatial
locations of the source, its temporal functions, and associated velocities
using a full waveform inversion framework. Under the premise of an isotropic
medium with consistent density, the source is defined by two spatial and three
temporal components. This offers a nuanced source representation in contrast to
the conventional seismic moment tensor. To address gradient computation, we
employ the adjoint-state method. However, we encountered pronounced
non-linearity in waveform inversion of micro-seismic events, primarily due to
the unknown source origin time, resulting in cycle skipping challenges. To
counteract this, we devised an objective function that is decoupled from the
source origin time. This function is formulated by convolving reference traces
with both observed and predicted data. Through the concurrent inversion of the
source image, source time function, and velocity model, our method offers
precise estimations of these parameters, as validated by a synthetic 2D example
based on a modified Marmousi model. This nested inversion approach promises
enhanced accuracy in determining the source image, time function, and velocity
model
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
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