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Multi-Constrained Seismic Multi-Parameter Full Waveform Inversion Based on Projected Quasi-Newton Algorithm

Remote Sensing, 2023
The multi-parameter full waveform inversion (FWI) that integrates velocity and density can make full use of the kinematic and dynamic information of the measured data to reconstruct the underground model.
Deshan Feng, Bingchao Li, Cen Cao, Xun Wang, Dianbo Li, Cheng Chen   +5 more
doaj   +3 more sources

Seismic Elastic Parameter Inversion via a FCRN and GRU Hybrid Network with Multi-Task Learning

Applied Sciences, 2023
Seismic elastic parameter inversion translates seismic data into subsurface structures and physical properties of formations. Traditional model-based inversion methods have limitations in retrieving complex geological structures.
Qiqi Zheng, Chao Wei, Xinfei Yan, Housong Ruan, Bangyu Wu   +4 more
doaj   +3 more sources

Algorithm for the Simultaneous Measurement of Multiple Parameters Based on Wavelength Modulation Spectroscopy [PDF]

Sensors
To ensure personnel safety and prevent serious accidents, it is crucial to monitor parameters such as temperature, pressure, and gas composition concentrations in confined spaces.
Xiangyu Zhong, Qing Shi, Buqiang Zhang, Huiwen Niu, Gui Meng, Jianfa Zhou, Yongqing Peng   +6 more
doaj   +2 more sources

MAGInet based on deep learning for magnetic multi-parameter inversion

AIP Advances
This manuscript introduces MAGInet, a novel deep learning framework designed for the magnetic multi-parameter inversion of complex structures. The architecture of MAGInet integrates a classifier and several solvers, where the classifier performs a preliminary categorization of magnetic field signals and the solvers execute a detailed regression ...
Wudi Wen, Yi Li, Zhongle Liu
openaire   +2 more sources

Learned Regularizations for Multi‐Parameter Elastic Full Waveform Inversion Using Diffusion Models

Journal of Geophysical Research: Machine Learning and Computation
AbstractElastic full waveform inversion (EFWI) promises to account for the Earth's elastic nature and corresponding reflectivity, which is often disregarded in the commonly used acoustic FWI. However, EFWI usually requires a more sophisticated recording apparatus (beyond the usual single‐component data).
Mohammad H. Taufik, Fu Wang, Tariq Alkhalifah   +2 more
openaire   +3 more sources

LAI estimation based on physical model combining airborne LiDAR waveform and Sentinel-2 imagery

Frontiers in Plant Science, 2023
Leaf area index (LAI) is an important biophysical parameter of vegetation and serves as a significant indicator for assessing forest ecosystems. Multi-source remote sensing data enables large-scale and dynamic surface observations, providing effective ...
Zixi Shi, Shuo Shi, Shuo Shi, Shuo Shi, Wei Gong, Wei Gong, Wei Gong, Lu Xu, Binhui Wang, Jia Sun, Bowen Chen, Bowen Chen, Qian Xu   +12 more
doaj   +1 more source

Corrigenda: Inverse multi-parameter eigenvalue problems for matrices II [PDF]

Proceedings of the Edinburgh Mathematical Society, 1987
In the paper referred to above (see [1]), the following corrections should be made.(1) Hypothesis 2.2 should read:(2) The set E⊂ℝn should be defined as(3) F0(υ)=υ+x should read F(υ)=υ–x.(4) The unique solution of F0(υ)=0 is υ=x (not υ= –x as printed).(5) Page 345, line 14:
Browne, Patrick J., Sleeman, B. D.
openaire   +1 more source

Multi-Parameter Step-by-Step Inversion of Resistivity Anisotropy and Application

Cejing jishu, 2023
Electromagnetic resistivity logging while drilling is widely used, which has become a required item in the inclined and horizontal offshore wells. However, due to the comprehensive influence of reservoir anisotropy, drilling fluid invasion and other ...
HU Wenliang, ZHANG Guodong, LIU Baoyin, LUO Jian, WEI Xiaohan   +4 more
doaj   +1 more source

Simultaneous Inversion of Layered Velocity and Density Profiles Using Direct Waveform Inversion (DWI): 1D Case

Frontiers in Earth Science, 2022
To better interpret the subsurface structures and characterize the reservoir, a depth model quantifying P-wave velocity together with additional rock’s physical parameters such as density, the S-wave velocity, and anisotropy is always preferred by ...
Zhonghan Liu, Yingcai Zheng, Hua-Wei Zhou   +2 more
doaj   +1 more source

Improving the estimation of alpine grassland fractional vegetation cover using optimized algorithms and multi-dimensional features

Plant Methods, 2021
Background Fractional vegetation cover (FVC) is an important basic parameter for the quantitative monitoring of the alpine grassland ecosystem on the Qinghai-Tibetan Plateau.
Xingchen Lin, Jianjun Chen, Peiqing Lou, Shuhua Yi, Yu Qin, Haotian You, Xiaowen Han   +6 more
doaj   +1 more source