Seismic Data Strong Noise Attenuation Based on Diffusion Model and Principal Component Analysis

Seismic data noise processing is an important part of seismic exploration data processing, and the effect of noise elimination is directly related to the follow-up processing of data. In response to this problem, many authors have proposed methods based on rank reduction, sparse transformation, domain transformation, and deep learning. However, such methods are often not ideal when faced with strong noise. Therefore, we propose to use diffusion model theory for noise removal. The Bayesian equation is used to reverse the noise addition process, and the noise reduction work is divided into multiple steps to effectively deal with high-noise situations. Furthermore, we propose to evaluate the noise level of blind Gaussian seismic data using principal component analysis to determine the number of steps for noise reduction processing of seismic data. We train the model on synthetic data and validate it on field data through transfer learning. Experiments show that our proposed method can identify most of the noise with less signal leakage. This has positive significance for high-precision seismic exploration and future seismic data signal processing research.Comment: 10 pages, 13 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Ruthenium nanocrystal decorated vertical graphene nanosheets@Ni foam as highly efficient cathode catalysts for lithium-oxygen batteries

The electrochemical performance of lithium-oxygen (Li-O2) batteries can be markedly improved through designing the architecture of cathode electrodes with sufficient spaces to facilitate the diffusion of oxygen and accommodate the discharge products, and optimizing the cathode catalyst to promote the oxygen reduction reaction and oxygen evolution reaction (OER). Herein, we report the synthesis of ruthenium (Ru) nanocrystal-decorated vertically aligned graphene nanosheets (VGNS) grown on nickel (Ni) foam. As an effective binder-free cathode catalyst for Li-O2 batteries, the Ru-decorated VGNS@Ni foam can significantly reduce the charge overpotential via the effects on the OER and achieve high specific capacity, leading to an enhanced electrochemical performance. The Ru-decorated VGNS@Ni foam electrode has demonstrated low charge overpotential of ~0.45 V and high reversible capacity of 23 864 mAh g−1 at the current density of 200 mA g−1, which can be maintained for 50 cycles under full charge and discharge testing condition in the voltage range of 2.0-4.2 V. Furthermore, Ru nanocrystal decorated VGNS@Ni foam can be cycled for more than 200 cycles with a low overpotential of 0.23 V under the capacity curtained to be 1000 mAh g−1 at a current density of 200 mA g−1. Ru-decorated VGNS@Ni foam electrodes have also achieved excellent high rate and long cyclability performance. This superior electrochemical performance should be ascribed to the unique three-dimensional porous nanoarchitecture of the VGNS@Ni foam electrodes, which provide sufficient pores for the diffusion of oxygen and storage of the discharge product (Li2O2), and the effective catalytic effect of Ru nanocrystals on the OER, respectively. Ex situ field emission scanning electron microscopy, X-ray diffraction, Raman and Fourier transform infrared measurements revealed that Ru-decorated VGNS@Ni foam can effectively decompose the discharge product Li2O2, facilitate the OER and lead to a high round-trip efficiency. Therefore, Ru-decorated VGNS@Ni foam is a promising cathode catalyst for rechargeable Li-O2 batteries with low charge overpotential, long cycle life and high specific capacity

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

One-pot synthesis of carbon nanotube-polyaniline-gold nanoparticle and carbon nanotube-gold nanoparticle composites by using aromatic amine chemistry

Harsh oxidative treatment of single-walled carbon nanotubes (SWNTs) was used to generate carboxyl groups on SWNT sidewalls. The oxidized SWNTs can disperse well in ethanolic solutions containing aniline (or it derivative, 4-dodecylaniline), possibly due to the formation of proton-transfer complexes between carboxyl and amine groups. Addition of HAuCl4 into the above-mentioned solutions can readily produce SWNT-polyaniline (PANI)-Au narroparticle (NP) or SWNT-Au NP composites in an in situ one-pot fashion. Transmission electron microscopy, UV-vis spectroscopy and cyclic voltammetry have been employed to characterize the obtained composites. Our findings suggest that the obtained composites and electrodes modified with this material in ay find interesting applications in electrochemical sensors and conducting polymer coatings.</p

Impedance Response of Electrochemical Interfaces: Part IV─Low-Frequency Inductive Loop for a Single-Electron Reaction

The low-frequency inductive loop is usually attributed to relaxation of adsorbed intermediates of multistep reactions in electrocatalysis and corrosion. Herein, we report a low-frequency inductive loop for a single-electron reaction when the electrode potential (EM), the equilibrium potential (Eeq), and the potential of zero charge (Epzc) are different, namely, under nonequilibrium conditions. Interestingly enough, although both reactions involve only one electron, the metal deposition reaction (M+ + e ↔ M) and the redox couple reaction (Fe(CN)63– + e ↔ Fe(CN)64–) show different impedance shapes. The low-frequency inductive loop is observed only for the M+ + e ↔ M reaction in the oxidation direction because its faradaic current has a negative phase angle due to double layer effects. Moreover, we find that the low-frequency inductive loop occurs only when the polarization curve has no diffusion-limiting features