25 research outputs found
FaultSSL: Seismic Fault Detection via Semi-supervised learning
The prevailing methodology in data-driven fault detection leverages synthetic
data for training neural networks. However, it grapples with challenges when it
comes to generalization in surveys exhibiting complex structures. To enhance
the generalization of models trained on limited synthetic datasets to a broader
range of real-world data, we introduce FaultSSL, a semi-supervised fault
detection framework. This method is based on the classical mean teacher
structure, with its supervised part employs synthetic data and a few 2D labels.
The unsupervised component relying on two meticulously devised proxy tasks,
allowing it to incorporate vast unlabeled field data into the training process.
The two proxy tasks are PaNning Consistency (PNC) and PaTching Consistency
(PTC). PNC emphasizes the feature consistency of the overlapping regions
between two adjacent views in predicting the model. This allows for the
extension of 2D slice labels to the global seismic volume. PTC emphasizes the
spatially consistent nature of faults. It ensures that the predictions for the
seismic, whether made on the entire volume or on individual patches, exhibit
coherence without any noticeable artifacts at the patch boundaries. While the
two proxy tasks serve different objectives, they uniformly contribute to the
enhancement of performance. Experiments showcase the exceptional performance of
FaultSSL. In surveys where other mainstream methods fail to deliver, we present
reliable, continuous, and clear detection results. FaultSSL breaks the shackles
of synthetic data, unveiling a promising route for incorporating copious
amounts of field data into training and fostering model generalization across a
broader spectrum of surveys.Comment: This work has been submitted to journal for possible publication.
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ContrasInver: Voxel-wise Contrastive Semi-supervised Learning for Seismic Inversion
Recent studies have shown that learning theories have been very successful in
hydrocarbon exploration. Inversion of seismic into various attributes through
the relationship of 1D well-logs and 3D seismic is an essential step in
reservoir description, among which, acoustic impedance is one of the most
critical attributes, and although current deep learningbased impedance
inversion obtains promising results, it relies on a large number of logs (1D
labels, typically more than 30 well-logs are required per inversion), which is
unacceptable in many practical explorations. In this work, we define acoustic
impedance inversion as a regression task for learning sparse 1D labels from 3D
volume data and propose a voxel-wise semisupervised contrastive learning
framework, ContrasInver, for regression tasks under sparse labels. ConstraInver
consists of several key components, including a novel pre-training method for
3D seismic data inversion, a contrastive semi-supervised strategy for diffusing
well-log information to the global, and a continuous-value vectorized
characterization method for a contrastive learning-based regression task, and
also designed the distance TopK sampling method for improving the training
efficiency. We performed a complete ablation study on SEAM Phase I synthetic
data to verify the effectiveness of each component and compared our approach
with the current mainstream methods on this data, and our approach demonstrated
very significant advantages. In this data we achieved an SSIM of 0.92 and an
MSE of 0.079 with only four well-logs. ConstraInver is the first purely
data-driven approach to invert two classic field data, F3 Netherlands (only
four well-logs) and Delft (only three well-logs) and achieves very reasonable
and reliable results.Comment: This work has been submitted to journal for possible publication.
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CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification
Recently, seismic facies classification based on convolutional neural
networks (CNN) has garnered significant research interest. However, existing
CNN-based supervised learning approaches necessitate massive labeled data.
Labeling is laborious and time-consuming, particularly for 3D seismic data
volumes. To overcome this challenge, we propose a semi-supervised method based
on pixel-level contrastive learning, termed CONSS, which can efficiently
identify seismic facies using only 1% of the original annotations. Furthermore,
the absence of a unified data division and standardized metrics hinders the
fair comparison of various facies classification approaches. To this end, we
develop an objective benchmark for the evaluation of semi-supervised methods,
including self-training, consistency regularization, and the proposed CONSS.
Our benchmark is publicly available to enable researchers to objectively
compare different approaches. Experimental results demonstrate that our
approach achieves state-of-the-art performance on the F3 survey
MDA GAN: Adversarial-Learning-based 3-D Seismic Data Interpolation and Reconstruction for Complex Missing
The interpolation and reconstruction of missing traces is a crucial step in
seismic data processing, moreover it is also a highly ill-posed problem,
especially for complex cases such as high-ratio random discrete missing,
continuous missing and missing in fault-rich or salt body surveys. These
complex cases are rarely mentioned in current sparse or low-rank priorbased and
deep learning-based approaches. To cope with complex missing cases, we propose
Multi-Dimensional Adversarial GAN (MDA GAN), a novel 3-D GAN framework. It
employs three discriminators to ensure the consistency of the reconstructed
data with the original data distribution in each dimension. The feature
splicing module (FSM) is designed and embedded into the generator of this
framework, which automatically splices the features of the unmissing part with
those of the reconstructed part (missing part), thus fully preserving the
information of the unmissing part. To prevent pixel distortion in the seismic
data caused by the adversarial learning process, we propose a new
reconstruction loss Tanh Cross Entropy (TCE) loss to provide smoother
gradients. We experimentally verified the effectiveness of the individual
components of the study and then tested the method on multiple publicly
available data. The method achieves reasonable reconstructions for up to 95% of
random discrete missing, 100 traces of continuous missing and more complex
hybrid missing. In surveys of fault-rich and salt bodies, the method can
achieve promising reconstructions with up to 75% missing in each of the three
directions (98.2% in total).Comment: This work has been submitted to journal for possible publication.
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COVID-19 causes record decline in global CO2 emissions
The considerable cessation of human activities during the COVID-19 pandemic
has affected global energy use and CO2 emissions. Here we show the
unprecedented decrease in global fossil CO2 emissions from January to April
2020 was of 7.8% (938 Mt CO2 with a +6.8% of 2-{\sigma} uncertainty) when
compared with the period last year. In addition other emerging estimates of
COVID impacts based on monthly energy supply or estimated parameters, this
study contributes to another step that constructed the near-real-time daily CO2
emission inventories based on activity from power generation (for 29
countries), industry (for 73 countries), road transportation (for 406 cities),
aviation and maritime transportation and commercial and residential sectors
emissions (for 206 countries). The estimates distinguished the decline of CO2
due to COVID-19 from the daily, weekly and seasonal variations as well as the
holiday events. The COVID-related decreases in CO2 emissions in road
transportation (340.4 Mt CO2, -15.5%), power (292.5 Mt CO2, -6.4% compared to
2019), industry (136.2 Mt CO2, -4.4%), aviation (92.8 Mt CO2, -28.9%),
residential (43.4 Mt CO2, -2.7%), and international shipping (35.9Mt CO2,
-15%). Regionally, decreases in China were the largest and earliest (234.5 Mt
CO2,-6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO2, -12.0%) and the U.S.
(162.4 Mt CO2, -9.5%). The declines of CO2 are consistent with regional
nitrogen oxides concentrations observed by satellites and ground-based
networks, but the calculated signal of emissions decreases (about 1Gt CO2) will
have little impacts (less than 0.13ppm by April 30, 2020) on the overserved
global CO2 concertation. However, with observed fast CO2 recovery in China and
partial re-opening globally, our findings suggest the longer-term effects on
CO2 emissions are unknown and should be carefully monitored using multiple
measures
Near-real-time monitoring of global COâ‚‚ emissions reveals the effects of the COVID-19 pandemic
The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO₂) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO₂ emissions (−1551 Mt CO₂) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic’s effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially
Incidence and Etiology of Drug-Induced Liver Injury in Mainland China
Background & Aims: We performed a nationwide, retrospective study to determine the incidence and causes of drug-induced liver injury (DILI) in mainland China.Methods: We collected data on a total of 25,927 confirmed DILI cases, hospitalized from 2012 through 2014 at 308 medical centers in mainland China. We collected demographic, medical history, treatment, laboratory, disease severity, and mortality data from all patients. Investigators at each site were asked to complete causality assessments for each case whose diagnosis at discharge was DILI (n=29,478) according to the Roussel Uclaf Causality Assessment Method.Results: Most cases of DILI presented with hepatocellular injury (51.39%; 95% CI, 50.76–52.03), followed by mixed injury (28.30%; 95% CI, 27.73–28.87) and cholestatic injury (20.31%; 95% CI, 19.80–20.82). The leading single classes of implicated drugs were traditional Chinese medicines or herbal and dietary supplements (26.81%) and anti-tuberculosis medications (21.99%). Chronic DILI occurred in 13.00% of the cases and, although 44.40% of the hepatocellular DILI cases fulfilled Hy’s Law criteria, only 280 cases (1.08%) progressed to hepatic failure, 2 cases underwent liver transplantation (0.01%), and 102 patients died (0.39%). Among deaths, DILI was judged to have a primary role in 72 (70.59%), a contributory role in 21 (20.59%), and no role in 9 (8.82%). Assuming the proportion of DILI in the entire hospitalized population of China was represented by that observed in the 66 centers where DILI capture was complete, we estimated the annual incidence in the general population to be 23.80 per 100,000 persons (95% CI, 20.86–26.74). Only hospitalized patients were included in this analysis, so the true incidence is likely to be higher.Conclusions: In a retrospective study to determine the incidence and causes of drug-induced liver injury (DILI) in mainland China, the annual incidence in the general population was estimated to be 23.80 per 100,000 persons—higher than that reported from western countries. Traditional Chinese medicines, herbal and dietary supplements, and anti-tuberculosis drugs were the leading causes of DILI in mainland Chin
Designing Quantum Dot Interfaces for Photoredox Catalysis
Thesis (Ph.D.)--University of Washington, 2023Chemical manufacturing accounts for 10% of total global energy consumption and 7% of greenhouse emissions. Thermodynamically uphill reactions may be driven by photoredox catalysts under ambient conditions, converting solar to chemical energy. Semiconductor quantum dots (QDs) are efficient photoredox catalysts due to their high absorptivity and easily tuneable redox potentials. They are routinely used in the hydrogen evolution reaction, but there remains much to be understood when applying QDs to organic synthesis. In this work, we present several strategies to synthesise more active QD photocatalysts for organic reactions via modulation of the semiconductor interface. In the first chapter, we explore catalyst speciation under reaction conditions and elucidate a mechanism for unwanted reactions happening at the catalyst surface that affect its stability. In the second chapter, we make more stable and active catalysts via ligand shell engineering using charge transfer mediators. In the third chapter, we use infrared (IR) spectroscopy to measure relative binding energies of common aliphatic ligands on nanocluster surfaces, which appear to be more labile than previously thought. Lastly in chapter four, we make anisotropic heterostructures with long-lived excited states and study their optoelectronic properties and charge transfer rates to molecular acceptors. This work increases our atomistic understanding of mechanisms involved in QD organic photoredox catalysis and provides design principles for making stable and more active catalysts
CONSS: Contrastive Learning Method for Semisupervised Seismic Facies Classification
Recently, convolutional neural networks (CNNs) have been widely applied in the seismic facies classification. However, even state-of-the-art CNN architectures often encounter classification confusion distinguishing seismic facies at their boundaries. In addition, the annotation is a highly time-consuming task, especially when dealing with 3-D seismic data volumes. While traditional semisupervised methods reduce dependence on annotation, they are susceptible to interference from unreliable pseudolabels. To address these challenges, we propose a semisupervised seismic facies classification method called CONSS, which effectively mitigates classification confusion through contrastive learning. Our proposed method requires only 1% of labeled data, significantly reducing the demand for annotation. To minimize the influence of unreliable pseudolabels, we also introduce a confidence strategy to select positive and negative sample pairs from reliable regions for contrastive learning. Experimental results on the publicly available seismic datasets, the Netherlands F3 and SEAM AI challenge datasets, demonstrate that the proposed method outperforms classic semisupervised methods, including self-training and consistency regularization, achieving exceptional classification performance