A5062277151- Seismic Imaging and Inversion Techniques
- Seismology and Earthquake Studies
- Seismic Waves and Analysis
- Hydraulic Fracturing and Reservoir Analysis
- Drilling and Well Engineering
- earthquake and tectonic studies
- Electrocatalysts for Energy Conversion
- Reservoir Engineering and Simulation Methods
- Earthquake Detection and Analysis
- Electrochemical Analysis and Applications
- Advanced Photocatalysis Techniques
- Advanced battery technologies research
- Sparse and Compressive Sensing Techniques
- Advanced Memory and Neural Computing
- Geophysical Methods and Applications
- Anomaly Detection Techniques and Applications
- NMR spectroscopy and applications
- Image and Signal Denoising Methods
- Hydrocarbon exploration and reservoir analysis
- Advanced Cellulose Research Studies
- Advanced Neuroimaging Techniques and Applications
- Copper-based nanomaterials and applications
- Frequency Control in Power Systems
- Geological and Geophysical Studies
- Cancer Cells and Metastasis
Shandong University
2021-2024
State Key Laboratory of Crystal Materials
2021-2024
Suzhou Research Institute
2022-2024
École Polytechnique Fédérale de Lausanne
2024
Cardiff University
2024
ETH Zurich
2022-2024
University of Strathclyde
2024
Shanghai Dianji University
2023
Université Grenoble Alpes
2019-2022
Institut des Sciences de la Terre
2019-2022
Regardless of successful applications the convolutional neural networks (CNNs) in different fields, its application to seismic waveform classification and first-break (FB) picking has not been explored yet. This letter investigates CNNs for classifying time-space waveforms from shot gathers FBs both direct wave refracted wave. We use representative subimage samples with two types labeled supervise training. The goal is obtain optimal weights biases CNNs, which are solved by minimizing error...
Abstract Source locations provide fundamental information on earthquakes and lay the foundation for seismic monitoring at all scales. Seismic source location as a classical inverse problem has experienced significant methodological progress during past century. Unlike conventional traveltime‐based methods that mainly utilize kinematic information, new category of waveform‐based methods, including partial waveform stacking, time reverse imaging, wavefront tomography, full inversion, adapted...
Abstract Constructing and manipulating hetero‐interfaces for the electrocatalytic hydrogen evolution reaction (HER) is highly desirable, but still poses a significant challenge. Herein, this work adopts facile way to controllably synthesize three different by anchoring ultrafine Ru nanoparticles on various MoO x nanotube (NT) substrates, including 2 , /MoO 3 . Remarkably, @Ru NT displays excellent HER activity with tiny overpotentials of 89 131 mV delivering large current densities 500 1000...
Several catalyst design strategies for enhanced OER performance under acidic conditions were summarized, which could provide guidance the synthesis of more efficient electrocatalysts.
The electrocatalytic nitrate reduction reaction (NO3–RR) to ammonia (NH3) under ambient conditions not only has the benefit of lowering energy consumption, but also helps remove contamination. Inspired by unique structure nitrate/nitrite reductase with active spheroproteins encapsulated larger enzymes, herein, we develop an in situ synthetic strategy for construction metal cluster–conductive metal–organic framework (MOF) composite electrocatalysts. metallic Cu clusters are filled into...
Abstract Robust automatic event detection and location is central to real-time earthquake monitoring. With the increase of computing power data availability, automated workflows that utilize machine learning (ML) techniques have become increasingly popular; however, ML-based classical still face challenges when applied analysis microseismic data. These seismic sequences are often characterized by short interevent times and/or low signal-to-noise ratio (SNR). Full waveform methods do not rely...
Abstract Benefiting from ordered atomic structures and strong d‐orbital interactions, intermetallic compounds (IMCs) are promising electrocatalysts for hydrogen evolution reaction (HER) oxygen (OER). Herein, the body‐centered cubic IrGa IMCs with donor–acceptor architectures synthesized anchored on nitrogen‐doped reduced graphene oxide (i.e., IrGa/N‐rGO). Structural characterizations theoretical calculations reveal that electron‐rich Ir sites atomically dispersed in IrGa/N‐rGO, facilitating...
A hierarchical nanoarray comprising an inner crystalline CoP nanorod and outer amorphous CoB nanosheet exhibits excellent performance toward hydrogen evolution.
Time-frequency (TF) analysis is a useful tool for seismic data processing and interpretation. We introduce sparse Bayesian learning (SBL) to TF propose new SBL-based high-resolution method. The method decomposes the trace into series of Ricker wavelets using representations subsequently implements Wigner-Ville distribution (WVD) on decomposed produce spectra. By iteratively solving maximum posterior type-II likelihood, decomposition can sequentially obtain an optimal number with different...
The electron-rich Ru sites are isolated in body-centered cubic RuGa intermetallic compounds, which reduces the energy barrier of rate-limiting step HER process, thus promoting high-current-density activity.
Optimized Deep Learning (DL)-based workflows can improve the efficiency and accuracy of earthquake detection location processes. This paper introduces a six-step automated event detection, phase association, workflow, which integrates state-of-the-art Pair-Input DL model waveform Migration Location methods (IPIML). Applying IPIML on an 18-months dataset Ghana Digital Seismic Network (GHSDN) recorded from 2012-2014, catalog with 461 events is automatically obtained. Compared to other catalogs...
In this paper, a percentile-half-thresholding approach is proposed in the transformed domain thresholding process for iterative shrinkage (IST). The percentile-thresholding strategy more convenient implementing than constant-value, linear-decreasing, or exponential-decreasing because it's data-driven. novel half-thresholding inspired from recent advancement researches on optimization using non-convex regularization. We summarize general framework IST and show that only difference between...
Abstract The application of machine learning techniques in seismology has greatly advanced seismological analysis, especially for earthquake detection and seismic phase picking. However, approaches still face challenges generalizing to data sets that differ from their original training setting. Previous studies focused on retraining or transfer‐learning models these scenarios, but require high‐quality labeled sets. This paper demonstrates a new approach augmenting already trained without the...
Enhanced Geothermal Systems (EGS) comprise technologies aiming to harness geothermal energy from the Earth's subsurface by enhancing productivity of existing or naturally occurring reservoirs. Unlike conventional systems (hydrothermal systems) that rely on permeable rock formations, EGS involve creating fractures in low permeability impermeable mass through hydraulic stimulation. has potential expand geographical reach utilization and increase overall efficiency sustainability power...
Monitoring induced seismicity is an indispensable part of risk management during the creation and operation enhanced geothermal systems. Due to relative scarcity manually labeled, informative datasets seismicity, it can be challenging evaluate performance monitoring tools ahead time. We have created continuous synthetic seismic waveform data for sequence at Utah Frontier Observatory Research in Geothermal Energy (FORGE). The are based on a catalog that mimicks injection-induced FORGE...
The world's energy supply depends critically on hydraulic fracturing (HF): HF operations utilize microseismicity to enhance subsurface permeability, so that hydrocarbons or geothermal heat can be extracted economically.  Unfortunately, also has the potential induce larger earthquakes – with some projects being prematurely terminated because of perceived earthquake risks.  To de-risk HF, we use a suite novel statistical tests called CAP-tests discern if physical...
With the proliferation of dense seismic networks sampling full wavefield, recorded data volumes are getting bigger and automated analysis tools to locate events essential. Here, we propose a novel multichannel coherency migration (MCM) method earthquakes in continuous reveal location origin time directly from waveforms. By continuously calculating coherencies between waveforms different receiver pairs, MCM greatly expands available information which can be used for event location. does not...
Fracture monitoring is crucial for many geo-industrial applications, such as carbon dioxide storage and hydrocarbon exploration in tight reservoirs, because fractures can form space or leaking paths geological sealing. We propose a fracture identification framework applications by exploiting seismic reflection anisotropy automatic multisensitive attribute fusion. Anisotropy maps extracted from different attributes are automatically selected fused according to the correlation between...
Abstract From June to August 2021, we deployed a dense seismic nodal network across the Hengill geothermal area in southwest Iceland image and characterize faults high-temperature zones at high resolution. The comprised 498 geophone nodes spread northern Nesjavellir southern Hverahlíð fields was complemented by an existing permanent temporary backbone of total 44 short-period broadband stations. In addition, recorded distributed acoustic sensing data along two fiber optic telecommunication...
Profilin 2 (PFN2) functions as an actin cytoskeleton regulator and serves important role in cell motility. However, a for PFN2 the progression of colorectal cancer (CRC), particularly metastasis, has yet to be clarified. The aim present study was investigate whether served specific roles human CRC. results demonstrated that differentially expressed CRC tissues lines by reverse transcription-quantitative polymerase chain reaction western blotting. expression also negatively associated with...
Abstract We apply unsupervised machine learning to 3 years of continuous seismic data unravel the evolution wavefield properties in period 2009 L'Aquila earthquake. To obtain sensible representations variations, we extract features (i.e., entropy, coherency, eigenvalue variance, and first eigenvalue) from covariance matrix analysis data. The defined are insensitive site‐dependent local noise, inform spatiotemporal waves generated by sources inside array. perform a sensitivity these features,...