Sponsors Meeting Posters 2022

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Amundaray, N., 2022, Log-guided parameterization in full waveform inversion: tuning a two parameter case: CREWES Meeting Poster, 34, no. 1.
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Bertram, K. L., 2022, Field school 2022, exposing the geoscientists of tomorrow to field acquisition methods: CREWES Meeting Poster, 34, no. 2.
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Bertram, K. L., 2022, Recent upgrades to the seismic physical modelling system: CREWES Meeting Poster, 34, no. 3.
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Cai, X., 2022, Analysis of the FWI workflow for accelerometer and DAS data from the 2018 CaMI VSP survey: CREWES Meeting Poster, 34, no. 4.
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Chen, H., 2022, Azimuthal seismic inversion for fracture weaknesses constrained by facies: CREWES Meeting Poster, 34, no. 5.
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Chen, H., 2022, Estimating reservoir parameters using first- and second-order derivatives of EI: CREWES Meeting Poster, 34, no. 6.
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Emery, D. J., 2022, Machine learning mineralogy classification comparison to empirical log relationship and implication for physics informed modeling: CREWES Meeting Poster, 34, no. 7.
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Fontes, P. H. L., 2022, The use of U-Net and Radon transforms for multiple attenuation: CREWES Meeting Poster, 34, no. 8.
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Fu, X., 2022, A robust source-independent full-waveform inversion: CREWES Meeting Poster, 34, no. 9.
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Fu, X., 2022, Non-repeatability effects on time-lapse elastic full-waveform inversion for VSP seismic data: CREWES Meeting Poster, 34, no. 10.
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Guarido, M., 2022, Oil spills identification on satellite radar data using deep learning: CREWES Meeting Poster, 34, no. 11.
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Hall, K. W., 2022, Cvictus VSP: CREWES Meeting Poster, 34, no. 12.
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Henley, D. C., 2022, Shadow Imaging: attenuation tomography without arrival picking for physical model data from a circular array: CREWES Meeting Poster, 34, no. 13.
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Hess, S., 2022, Data science for geothermal drilling optimization collaboration with GeoS: CREWES Meeting Poster, 34, no. 14.
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Hu, Q., 2022, Bayesian approaches to estimating rock physics properties from seismic attributes: CREWES Meeting Poster, 34, no. 15.
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Hu, Q., 2022, Time-lapse FWI prediction of CO2 saturation and pore pressure: CREWES Meeting Poster, 34, no. 16.
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Huang, S., 2022, Discrete wavelet transform application in a CNN-based reverse time migration with multiple energy: CREWES Meeting Poster, 34, no. 17.
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Innanen, K. A., 2022, Comparing two basic approaches to decorrelation transforms: CREWES Meeting Poster, 34, no. 18.
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Keating, S., 2022, Targeted nullspace shuttling for time-lapse FWI: CREWES Meeting Poster, 34, no. 19.
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Li, J., 2022, Robust reconstruction via group sparsity with Radon operators: CREWES Meeting Poster, 34, no. 20.
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Li, J. L., 2022, Instruction for C++ package of visco-elastic multiparameter FWI in the frequency domain: CREWES Meeting Poster, 34, no. 21.
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Liu, H., 2022, Time-lapse FWI using simultaneous sources: CREWES Meeting Poster, 34, no. 22.
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Lume, M., 2022, Empirical radiation patterns as a method to assess crosstalk under scenarios of heterogeneous reference media: CREWES Meeting Poster, 34, no. 23.
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Monsegny, J. E., 2022, Geophysical inversion quantum style: CREWES Meeting Poster, 34, no. 24.
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Qu, L., 2022, A 2D full-waveform inversion using trench-deployed surface and VSP DAS data from CaMI FRS: CREWES Meeting Poster, 34, no. 25.
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Reid, S., 2022, Poroelastic modeling of soap hole formation: CREWES Meeting Poster, 34, no. 26.
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Sanchez, I., 2022, Attentuation surface noise with autoencoders: CREWES Meeting Poster, 34, no. 27.
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Trad, D. O., 2022, Combining classical processing with Machine Learning : CREWES Meeting Poster, 34, no. 28.
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Wang, J., 2022, Enhanced reverse time migration of walkway VSP data: CREWES Meeting Poster, 34, no. 29.
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Wang, Y., 2022, Acoustic and exact elastic impedance variations during CO2 injection at the CaMI.FRS: CREWES Meeting Poster, 34, no. 30.
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Wong, J., 2022, Back-projection of physically-modelled seismic data: CREWES Meeting Poster, 34, no. 31.
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Zhang, T., 2022, Auto-adjoint time domain elastic full waveform inversion: CREWES Meeting Poster, 34, no. 32.
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Zhang, T., 2022, Elastic full waveform inversion results uncertainty analysis: a comparison between the model uncertainty given by conventional FWI and machine learning methods: CREWES Meeting Poster, 34, no. 33.
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Zhuang, K., 2022, DFT GPU operators for 5D interpolation : CREWES Meeting Poster, 34, no. 34.
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