265 research outputs found
Semi-blind-trace algorithm for self-supervised attenuation of trace-wise coherent noise
Trace-wise noise is a type of noise often seen in seismic data, which is
characterized by vertical coherency and horizontal incoherency. Using
self-supervised deep learning to attenuate this type of noise, the conventional
blind-trace deep learning trains a network to blindly reconstruct each trace in
the data from its surrounding traces; it attenuates isolated trace-wise noise
but causes signal leakage in clean and noisy traces and reconstruction errors
next to each noisy trace. To reduce signal leakage and improve denoising, we
propose a new loss function and masking procedure in semi-blind-trace deep
learning. Our hybrid loss function has weighted active zones that cover masked
and non-masked traces. Therefore, the network is not blinded to clean traces
during their reconstruction. During training, we dynamically change the masks'
characteristics. The goal is to train the network to learn the characteristics
of the signal instead of noise. The proposed algorithm enables the designed
U-net to detect and attenuate trace-wise noise without having prior information
about the noise. A new hyperparameter of our method is the relative weight
between the masked and non-masked traces' contribution to the loss function.
Numerical experiments show that selecting a small value for this parameter is
enough to significantly decrease signal leakage. The proposed algorithm is
tested on synthetic and real off-shore and land datasets with different noises.
The results show the superb ability of the method to attenuate trace-wise noise
while preserving other events. An implementation of the proposed algorithm as a
Python code is also made available
Extrapolated full waveform inversion with deep learning
The lack of low frequency information and a good initial model can seriously
affect the success of full waveform inversion (FWI), due to the inherent cycle
skipping problem. Computational low frequency extrapolation is in principle the
most direct way to address this issue. By considering bandwidth extension as a
regression problem in machine learning, we propose an architecture of
convolutional neural network (CNN) to automatically extrapolate the missing low
frequencies without preprocessing and post-processing steps. The bandlimited
recordings are the inputs of the CNN and, in our numerical experiments, a
neural network trained from enough samples can predict a reasonable
approximation to the seismograms in the unobserved low frequency band, both in
phase and in amplitude. The numerical experiments considered are set up on
simulated P-wave data. In extrapolated FWI (EFWI), the low-wavenumber
components of the model are determined from the extrapolated low frequencies,
before proceeding with a frequency sweep of the bandlimited data. The proposed
deep-learning method of low-frequency extrapolation shows adequate
generalizability for the initialization step of EFWI. Numerical examples show
that the neural network trained on several submodels of the Marmousi model is
able to predict the low frequencies for the BP 2004 benchmark model.
Additionally, the neural network can robustly process seismic data with
uncertainties due to the existence of noise, poorly-known source wavelet, and
different finite-difference scheme in the forward modeling operator. Finally,
this approach is not subject to the structural limitations of other methods for
bandwidth extension, and seems to offer a tantalizing solution to the problem
of properly initializing FWI.Comment: 30 pages, 22 figure
Earthquake Arrival Association with Backprojection and Graph Theory
The association of seismic wave arrivals with causative earthquakes becomes
progressively more challenging as arrival detection methods become more
sensitive, and particularly when earthquake rates are high. For instance,
seismic waves arriving across a monitoring network from several sources may
overlap in time, false arrivals may be detected, and some arrivals may be of
unknown phase (e.g., P- or S-waves). We propose an automated method to
associate arrivals with earthquake sources and obtain source locations
applicable to such situations. To do so we use a pattern detection metric based
on the principle of backprojection to reveal candidate sources, followed by
graph-theory-based clustering and an integer linear optimization routine to
associate arrivals with the minimum number of sources necessary to explain the
data. This method solves for all sources and phase assignments simultaneously,
rather than in a sequential greedy procedure as is common in other association
routines. We demonstrate our method on both synthetic and real data from the
Integrated Plate Boundary Observatory Chile (IPOC) seismic network of northern
Chile. For the synthetic tests we report results for cases with varying
complexity, including rates of 500 earthquakes/day and 500 false
arrivals/station/day, for which we measure true positive detection accuracy of
> 95%. For the real data we develop a new catalog between January 1, 2010 -
December 31, 2017 containing 817,548 earthquakes, with detection rates on
average 279 earthquakes/day, and a magnitude-of-completion of ~M1.8. A subset
of detections are identified as sources related to quarry and industrial site
activity, and we also detect thousands of foreshocks and aftershocks of the
April 1, 2014 Mw 8.2 Iquique earthquake. During the highest rates of aftershock
activity, > 600 earthquakes/day are detected in the vicinity of the Iquique
earthquake rupture zone
Intelligent Feature Extraction, Data Fusion and Detection of Concrete Bridge Cracks: Current Development and Challenges
As a common appearance defect of concrete bridges, cracks are important
indices for bridge structure health assessment. Although there has been much
research on crack identification, research on the evolution mechanism of bridge
cracks is still far from practical applications. In this paper, the
state-of-the-art research on intelligent theories and methodologies for
intelligent feature extraction, data fusion and crack detection based on
data-driven approaches is comprehensively reviewed. The research is discussed
from three aspects: the feature extraction level of the multimodal parameters
of bridge cracks, the description level and the diagnosis level of the bridge
crack damage states. We focus on previous research concerning the quantitative
characterization problems of multimodal parameters of bridge cracks and their
implementation in crack identification, while highlighting some of their major
drawbacks. In addition, the current challenges and potential future research
directions are discussed.Comment: Published at Intelligence & Robotics; Its copyright belongs to
author
Applied Metaheuristic Computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Integration of feature extraction, attribute combination and image segmentation for object delineation on seismic images
Automatic geological interpretation, specifically modeling salt dome and fault detection, is controversial task on seismic images from complex geological media. In advanced techniques of seismic interpretation and modeling, various strategies are utilized for combination and integration different information layers to obtain an image adequate for automatic extraction of the object from seismic data. Efficiency of the selected feature extraction, data integration and image segmentation methods are the most important parameters that affect accuracy of the final model. Moreover, quality of the seismic data also affects confidence of the selected seismic attributes for integration. The present study proposed a new strategy for efficient delineation and modeling of geological objects on the seismic image. The proposed method consists of extraction specific features by the histogram of oriented gradients (HOG) method, statistical analysis of the HOG features, integration of features through hybrid attribute analysis and image classification or segmentation. The final result is a binary model of the target under investigation. The HOG method here modified accordingly for extraction of the related features for delineation of salt dome and fault zones from seismic data. The extracted HOG parameter then is statically analyzed to define the best state of information integration. The integrated image, which is the hybrid attribute, then is used for image classification, or image segmentation by the image segmentation method. The seismic image labeling procedure performs on the related seismic attributes, evaluated by the extracted HOG feature. Number of HOG feature and the analyzing parameters are also accordingly optimized. The final image classification then is performed on an image which contains all the embedded information on all the related textural conventional and statistical attributes and features. The proposed methods here apply on four seismic data examples, synthetic model of salt dome and faults and two real data that contain salt dome and fault. Results have shown that the proposed method can more accurately model the targets under investigation, compared to advanced extracted attributes and manual interpretations
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