arXiv subjects

Pengcheng Fu

Publications and source records attributed to Pengcheng Fu.

7 recordsLinked to original sources

arXiv2026

Quantum Squeezing Enhanced Photothermal Microscopy

Label-free optical microscopy through absorption or scattering spectroscopy provides fundamental insights across biology and materials science, yet its sensitivity remains fundamentally limited by photon shot noise. While recent demonstrations of quantum nonlinear microscopy show sub-shot-limited sensitivity, they are intrinsically limited by availability of high peak-power squeezed light sources. Here, we introduce squeezing-enhanced photothermal (SEPT) microscopy, a quantum imaging technique that leverages twin-beam quantum correlations to detect absorption induced signals with unprecedented sensitivity. SEPT achieves 3.5 dB noise suppression beyond the standard quantum limit, enabling a 2.5-fold increase in imaging throughput or 31% reduction in pump power, while providing an unmatched versatility through the intrinsic compatibility between continuous-wave squeezing and photothermal modulation. We showcase SEPT applications by providing high-precision characterization of nanoparticles and revealing subcellular structures, such as cytochrome c, that remain undetectable under shot-noise-limited imaging. By combining label-free contrast, quantum-enhanced sensitivity, and compatibility with existing microscopy platforms, SEPT establishes a new paradigm for molecular absorption imaging with far-reaching implications in cellular biology, nanoscience, and materials characterization.

physics.optics↗

arXiv2025

VisAlgae 2023: A Dataset and Challenge for Algae Detection in Microscopy Images

Microalgae, vital for ecological balance and economic sectors, present challenges in detection due to their diverse sizes and conditions. This paper summarizes the second "Vision Meets Algae" (VisAlgae 2023) Challenge, aiming to enhance high-throughput microalgae cell detection. The challenge, which attracted 369 participating teams, includes a dataset of 1000 images across six classes, featuring microalgae of varying sizes and distinct features. Participants faced tasks such as detecting small targets, handling motion blur, and complex backgrounds. The top 10 methods, outlined here, offer insights into overcoming these challenges and maximizing detection accuracy. This intersection of algae research and computer vision offers promise for ecological understanding and technological advancement. The dataset can be accessed at: https://github.com/juntaoJianggavin/Visalgae2023/.

cs.CV↗

arXiv2022

Vision meets algae: A novel way for microalgae recognization and health monitor

Marine microalgae are widespread in the ocean and play a crucial role in the ecosystem. Automatic identification and location of marine microalgae in microscopy images would help establish marine ecological environment monitoring and water quality evaluation system. We proposed a new dataset for the detection of marine microalgae and a range of detection methods, the dataset including images of different genus of algae and the same genus in different states. We set the number of unbalanced classes in the data set and added images of mixed water samples in the test set to simulate the actual situation in the field. Then we trained, validated and tested the, TOOD, YOLOv5, YOLOv8 and variants of RCNN algorithms on this dataset. The results showed both one-stage and two-stage object detection models can achieve high mean average precision, which proves the ability of computer vision in multi-object detection of microalgae, and provides basic data and models for real-time detection of microalgal cells.

cs.CV↗

arXiv2022

Deep Learning-Accelerated 3D Carbon Storage Reservoir Pressure Forecasting Based on Data Assimilation Using Surface Displacement from InSAR

Fast forecasting of reservoir pressure distribution in geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we propose to use low-cost Interferometric Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir pressure build up. We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynamic reservoir pressure. Employing an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates three-dimensional (3D) geologic properties and predicts reservoir pressure with quantified uncertainties. We use a synthetic commercial-scale GCS model with bimodally distributed permeability and porosity to demonstrate the efficacy of the workflow. A two-step CNN-PCA approach is employed to parameterize the bimodal fields. The computational efficiency of the workflow is boosted by two residual U-Net based surrogate models for surface displacement and reservoir pressure predictions, respectively. The workflow can complete data assimilation and reservoir pressure forecasting in half an hour on a personal computer.

stat.ML↗

arXiv2021

Machine learning-based porosity estimation from spectral decomposed seismic data

Estimating porosity models via seismic data is challenging due to the signal noise and insufficient resolution of seismic data. Although impedance inversion is often used by combining with well logs, several hurdles remain to retrieve sub-seismic scale porosity. As an alternative, we propose a machine learning-based workflow to convert seismic data to porosity models. A ResUNet++ based workflow is designed to take three seismic data in different frequencies (i.e., decomposed seismic data) and estimate their corresponding porosity model. The workflow is successfully demonstrated in the 3D channelized reservoir to estimate the porosity model with more than 0.9 in R2 score for training and validating data. Moreover, the application is extended for a stress test by adding signal noise to the seismic data, and the workflow results show a robust estimation even with 5\% of noise. Another two ResUNet++ are trained to take either the lowest or highest resolution seismic data only to estimate the porosity model, but they show under- and over-fitting results, supporting the importance of using decomposed seismic data in porosity estimation.

physics.geo-ph↗

arXiv2021

Transverse Hydraulic Fracture Initiation: Insights from Intermediate-Scale Hydraulic Fracture Tests

In a uniform stress field, a tensile hydraulic fracture will favor propagation in the plane formed by the maximum (most compressive) and intermediate principal stresses. For the propagation of hydraulic fractures in non-uniform stress fields, especially those where the principal stress directions change spatially, things become much more complex, are less well understood, and therefore are more difficult to predict. Such is often the case when a hydraulic fracture is initiated from a borehole drilled in the direction of the minimum principal stress. This is a well-known problem that has been the subject of several numerical and experimental studies. The purpose of this paper is to offer new insights from a series of hydraulic fracture tests conducted in sub-horizontal boreholes drilled in the Sanford Underground Research Facility. This project, called the Enhanced Geothermal Systems (EGS) Collab project, has developed a test bed at a depth of approximately 1478 meters underground consisting of sub-horizontal injection, production, and monitoring wells. This paper presents stimulation test data, and supporting geologic/geophysical characterization data. The test data are compared to previous numerical and experimental studies to determine how well this new experimental data compares with previous modeling, laboratory, and field tests.

physics.geo-ph↗

arXiv2021

A Deep Learning-Accelerated Data Assimilation and Forecasting Workflow for Commercial-Scale Geologic Carbon Storage

Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO2) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage. The high computational cost of data assimilation with a high-dimensional parameter space impedes fast decision-making for commercial-scale reservoir management. We propose to leverage physical understandings of porous medium flow behavior with deep learning techniques to develop a fast history matching-reservoir response forecasting workflow. Applying an Ensemble Smoother Multiple Data Assimilation framework, the workflow updates geologic properties and predicts reservoir performance with quantified uncertainty from pressure history and CO2 plumes interpreted through seismic inversion. As the most computationally expensive component in such a workflow is reservoir simulation, we developed surrogate models to predict dynamic pressure and CO2 plume extents under multi-well injection. The surrogate models employ deep convolutional neural networks, specifically, a wide residual network and a residual U-Net. The workflow is validated against a flat three-dimensional reservoir model representative of a clastic shelf depositional environment. Intelligent treatments are applied to bridge between quantities in a true-3D reservoir model and those in a single-layer reservoir model. The workflow can complete history matching and reservoir forecasting with uncertainty quantification in less than one hour on a mainstream personal workstation.

physics.geo-ph↗