Article
Geochemistry & Geophysics
Jidong Yang, Jianping Huang, Zhenchun Li, Hejun Zhu, George McMechan, James Zhang, Chaoshun Hu, Yang Zhao
Summary: Least-squares migration (LSM) can reduce finite-frequency effects, remove acquisition footprints and improve spatial resolution by solving a linear inverse problem for subsurface reflectivity. A novel imaging framework in the subsurface half-opening angle domain has been developed to mitigate velocity model inaccuracies in LSM.
SURVEYS IN GEOPHYSICS
(2021)
Review
Mathematics
Dmitriy Ivanov, Zaineb Yakoub
Summary: This paper provides an overview of the main methods used to identify autoregressive models with additive noises. It classifies the identification methods and discusses the advantages and disadvantages of each group. The article presents simulation results of various methods and offers recommendations for selecting the best methods.
Article
Geochemistry & Geophysics
Yuzhu Liu, Weigang Liu, Zheng Wu, Jizhong Yang
Summary: Reverse time migration (RTM) is widely used in oil and gas exploration for imaging complex subsurface structures. However, the amplitude of the output migration profile is biased due to the use of only the adjoint of the forward Born modeling operator in RTM. To partially balance the amplitude performance, RTM image can be preconditioned with the inverse of the diagonal of the Hessian operator. Existing preconditioning methods do not consider receiver-side effects accurately. Therefore, we have developed a frequency-domain scattering-integral reverse time migration (SI-RTM) to study the importance of incorporating receiver-side effects.
Article
Geochemistry & Geophysics
Yuzhu Liu, Weigang Liu, Zheng Wu, Jizhong Yang
Summary: Our study highlights the importance of incorporating receiver-side effects in RTM imaging, as it significantly improves the final migration images. The SI-RTM method explicitly computes the diagonal of the Hessian operator and solves the two-way wave equation to obtain source-side wavefields and receiver-side Green's functions. This approach is relatively affordable compared to least-squares RTM.
Article
Geochemistry & Geophysics
Cewen Liu, Mengyao Sun, Nanxun Dai, Wei Wu, Yanwen Wei, Mingjie Guo, Haohuan Fu
Summary: This study proposes a deep learning-based point-spread function deconvolution method for high-resolution seismic imaging. By training a convolutional neural network to predict deconvolution operators, computational and memory costs can be significantly reduced while improving the quality of the images.
Article
Statistics & Probability
Zahra Zafar, Muhammad Aslam
Summary: This article extensively discusses the issue of heteroscedasticity and its negative impact on linear regression model estimation. It introduces a relatively new approach called the least squares ratio method to estimate a linear regression model in the presence of heteroscedasticity and outliers. An adaptive version of this technique is proposed, taking advantage of existing adaptive estimators to address the issue of unknown heteroscedasticity. Monte Carlo simulation is used to evaluate the performance of the proposed estimator under varying degrees of heteroscedasticity and number of outliers.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2023)
Article
Geochemistry & Geophysics
Han Wu, Xintong Dong, Tie Zhong, Shukui Zhang, Shaoping Lu
Summary: The conventional least-squares reverse time migration (LSRTM) is a modeling-driven algorithm that aims to fit input data rather than produce high-quality imaging results. To overcome the limitations of the conventional LSRTM, a migration-driven LSRTM approach is proposed by formulating the migration process using an inverse scattering imaging condition (ISIC) to eliminate backscattering noise. Synthetic data tests and real data application demonstrate that the migration-driven LSRTM approach can solve the inversion problem more robustly and efficiently compared to the modeling-driven LSRTM.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Alejandro Cabrales-Vargas, Rahul Sarkar, Biondo L. Biondi, Robert G. Clapp
Summary: During linearized waveform inversion, small inaccuracies in the background subsurface model can cause unfocused seismic events and mislead the interpretation of amplitude. We have developed a joint inversion scheme to unify the inversion of the background and reflectivity models, resulting in a better estimate of reflectivity and improved background model.
Article
Engineering, Mechanical
Tianqi Gu, Hongxin Lin, Dawei Tang, Shuwen Lin, Tianzhi Luo
Summary: This article introduces a robust MTLS method, which can suppress multiple outliers within the whole domain by removing anomalous nodes through a two-step pre-process. The proposed method shows great robustness and accuracy in reconstructing simulation and experiment data.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Satwika Bhogavalli, K. V. S. Hari, Eric Grivel, Vincent Corretja
Summary: This paper presents a dual-function radar communication system based on orthogonal frequency division multiplexing (OFDM), which allows for radar detection and communication simultaneously. The paper proposes using subspace methods to accurately estimate the directions of arrival of targets and least-squares (LS) methods to estimate the ranges and/or velocities. Simulation results show that the proposed approaches outperform existing methods based on Fourier transform and/or Lasso algorithm.
Article
Engineering, Multidisciplinary
Peiliang Xu
Summary: The algebraic fitting of circles was originally proposed by Delogne (1972) and Kasa (1976). In this study, we extend their work and introduce fast and nearly unbiased weighted least squares methods to best fit circles. Simulation results demonstrate that the two non-iterative bias-corrected weighted LS methods outperform the naive weighted LS method, ordinary LS-based methods, and gradient-based weighted LS method in terms of both biases and mean squared errors, regardless of strong or weak geometric constraints. However, a weak geometric constraint leads to poor circle fitting. To address this, we propose regularized variants of the bias-corrected weighted LS method to fit circles with weak geometric constraints. Simulations also reveal that these two non-iterative regularized variants achieve satisfactory circle fitting and consistently perform the best among all regularization methods for ill-conditioned circle fitting problems.
Article
Mathematics
Doualeh Abdillahi-Ali, Nourddine Azzaoui, Arnaud Guillin, Guillaume Le Mailloux, Tomoko Matsui
Summary: This paper examines penalized least squares estimators with convex penalties or regularization norms, providing sparsity oracle inequalities for prediction errors in a general convex penalty context, as well as for specific cases like Lasso and Group Lasso estimators in regression. The main contribution is the establishment of oracle inequalities for scenarios where observation noise stems from probability measures with weak spectral gaps, as opposed to just Gaussian distributions. The results are demonstrated on heavy-tailed and sub-Gaussian examples, with explicit bounds given for these special cases.
ACTA MATHEMATICA SCIENTIA
(2021)
Article
Chemistry, Analytical
Dionisia Ortiz-Aguayo, Xavier Ceto, Karolien De Wael, Manel del Valle
Summary: This work evaluates the ability of an electronic tongue (ET) to resolve and quantify mixtures of different opiate compounds in the presence of common cutting agents. The study successfully resolved ternary mixtures of heroin, morphine, and codeine in the presence of caffeine and paracetamol. The developed sensors showed good linearity and low detection limits for the three drugs, and a quantitative model based on partial least squares regression (PLS) was able to accurately identify and quantify the individual substances from the voltammograms.
SENSORS AND ACTUATORS B-CHEMICAL
(2022)
Review
Engineering, Mechanical
Randall J. Allemang, Rohit S. Patwardhan, Murali M. Kolluri, Allyn W. Phillips
Summary: This paper outlines various FRF estimation techniques and compares algorithms that compute FRF using different methods. It also discusses inconsistencies in some conditioned coherence metrics and provides corrected interpretations.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Environmental Sciences
Yanbao Zhang, Yike Liu, Jia Yi
Summary: This paper proposes a least-squares optimized algorithm for multiples, called LSRTM-WM, which can significantly remove crosstalks and improve spatial resolution by reverse-time migration of water-bottom-related multiples.
