Staff profile

Education

• 2006 - 2010: DPhil Earth Sciences, University of Oxford. Supervisor: Prof. J.H. Woodhouse
• 2002 - 2006: BA/MSci Natural Sciences (Physics), University of Cambridge.

Academic Employment

• 2023 - present: Associate Professor, Department of Earth Sciences, Durham University
• 2021 - 2023: Assistant Professor, Department of Earth Sciences, Durham University
• 2016 - 2021: Fellow, Research School of Earth Sciences, The Australian National University
• 2011 - 2016: Postdoctoral Researcher, Department of Earth Sciences, Universiteit Utrecht

Other Significant Roles and Responsibilities

• Secretary, IUGG Commission on Mathematical Geophysics (2023-present)
• Honorary Senior Lecturer, The Australian National University (2021-present)
• Editor, Geophysical Journal International (2020-present)
• CIG Seismology Working Group committee member (2020-present)

• Geophysical inverse theory
• Global seismology
• Machine learning and its applications in the Earth Sciences

• 2023: Secretary, IUGG Commission on Mathematical Geophysics:
• 2021: Honorary Senior Lecturer, The Australian National University:
• 2020: Editor, Geophysical Journal International:

Chapter in book

• Emerging Directions in Geophysical Inversion
  
  Valentine, A. P., & Sambridge, M. (2023). Emerging Directions in Geophysical Inversion. In A. Ismail-Zadeh, F. Castelli, D. Jones, & S. Sanchez (Eds.), Applications of Data Assimilation and Inverse Problems in the Earth Sciences (pp. 9-26). Cambridge University Press. https://doi.org/10.1017/9781009180412.003
• Observations and models of dynamic topography: Current status and future directions
  
  Davies, D. R., Ghelichkhan, S., Hoggard, M., Valentine, A. P., & Richards, F. D. (2023). Observations and models of dynamic topography: Current status and future directions. In J. Duarte (Ed.), Dynamics of plate tectonics and mantle convection (pp. 223-270). Elsevier. https://doi.org/10.1016/B978-0-323-85733-8.00017-2

Journal Article

• GEORGIA: A Graph Neural Network Based EmulatOR for Glacial Isostatic Adjustment
  
  Lin, Y., Whitehouse, P. L., Valentine, A. P., & Woodroffe, S. A. (2023). GEORGIA: A Graph Neural Network Based EmulatOR for Glacial Isostatic Adjustment. Geophysical Research Letters, 50(18). https://doi.org/10.1029/2023gl103672
• Reply to comment on Geophysical inversion and Optimal Transport, 231, 172–198, by Okazaki & Ueda
  
  Sambridge, M., Jackson, A., & Valentine, A. P. (2023). Reply to comment on Geophysical inversion and Optimal Transport, 231, 172–198, by Okazaki & Ueda. Geophysical Journal International, 233(2), 1488-1491. https://doi.org/10.1093/gji/ggad002
• Overcomplete tomography: a novel approach to imaging
  
  Turunçtur, B., Valentine, A., & Sambridge, M. (2023). Overcomplete tomography: a novel approach to imaging. RAS Techniques and Instruments, 2(1), 207-215. https://doi.org/10.1093/rasti/rzad010
• Geophysical inversion and optimal transport
  
  Sambridge, M., Jackson, A., & Valentine, A. P. (2022). Geophysical inversion and optimal transport. Geophysical Journal International, 231(1), 172-198. https://doi.org/10.1093/gji/ggac151
• Upscaling and downscaling Monte Carlo ensembles with generative models
  
  Scheiter, M., Valentine, A., & Sambridge, M. (2022). Upscaling and downscaling Monte Carlo ensembles with generative models. Geophysical Journal International, 230(2), 916-931. https://doi.org/10.1093/gji/ggac100
• pyprop8: A lightweight code to simulate seismic observables in a layered half-space
  
  Valentine, A. P., & Sambridge, M. (2022). pyprop8: A lightweight code to simulate seismic observables in a layered half-space. Journal of Open Source Software, 7(76), Article 4217. https://doi.org/10.21105/joss.04217
• Structure and properties of alkali aluminosilicate glasses and melts: insights from deep learning
  
  Le Losq, C., Valentine, A., Mysen, B. O., & Neuville, D. R. (2021). Structure and properties of alkali aluminosilicate glasses and melts: insights from deep learning. Geochimica Et Cosmochimica Acta, 314, 27-54. https://doi.org/10.1016/j.gca.2021.08.023
• Inferring material properties of the lower mantle minerals using Mixture Density Networks
  
  Rijal, A., Cobden, L., Trampert, J., Jackson, J. M., & Valentine, A. (2021). Inferring material properties of the lower mantle minerals using Mixture Density Networks. Physics of the Earth and Planetary Interiors, 319, Article 106784. https://doi.org/10.1016/j.pepi.2021.106784
• Global models from sparse data: A robust estimate of Earth's residual topography spectrum
  
  Valentine, A., & Davies, D. (2020). Global models from sparse data: A robust estimate of Earth’s residual topography spectrum. Geochemistry, Geophysics, Geosystems, 21(8), Article e2020GC009240. https://doi.org/10.1029/2020gc009240
• Gaussian process models—I. A framework for probabilistic continuous inverse theory
  
  Valentine, A., & Sambridge, M. (2020). Gaussian process models—I. A framework for probabilistic continuous inverse theory. Geophysical Journal International, 220, 1632-1647. https://doi.org/10.1093/gji/ggz520
• Gaussian process models—II. Lessons for discrete inversion
  
  Valentine, A., & Sambridge, M. (2020). Gaussian process models—II. Lessons for discrete inversion. Geophysical Journal International, 220, 1648-1656. https://doi.org/10.1093/gji/ggz521
• Earth's multi-scale topographic response to global mantle flow
  
  Davies, D., Valentine, A., Kramer, S., Rawlinson, N., Hoggard, M., Eakin, C., & Wilson, C. (2019). Earth’s multi-scale topographic response to global mantle flow. Nature Geoscience, 12, 845-850. https://doi.org/10.1038/s41561-019-0441-4
• Hydrogeological Bayesian hypothesis testing through trans-dimensional sampling of a stochastic water balance model
  
  Enemark, T., Peeters, L., Mallants, D., Batelaan, O., Valentine, A., & Sambridge, M. (2019). Hydrogeological Bayesian hypothesis testing through trans-dimensional sampling of a stochastic water balance model. Water, 11. https://doi.org/10.3390/w11071463
• Optimal regularisation for a class of linear inverse problem
  
  Valentine, A., & Sambridge, M. (2018). Optimal regularisation for a class of linear inverse problem. Geophysical Journal International, 215, 1003-1021. https://doi.org/10.1093/gji/ggy303
• Hamilton's principle and normal mode coupling in an aspherical planet with a fluid core
  
  Al-Attar, D., Crawford, O., Valentine, A., & Trampert, J. (2018). Hamilton’s principle and normal mode coupling in an aspherical planet with a fluid core. Geophysical Journal International, 214, 485-507. https://doi.org/10.1093/gji/ggy141
• Exact free oscillation spectra, splitting functions and the resolvability of Earth's density structure
  
  Akbarashrafi, F., Al-Attar, D., Deuss, A., Trampert, J., & Valentine, A. (2018). Exact free oscillation spectra, splitting functions and the resolvability of Earth’s density structure. Geophysical Journal International, 213, 58-76. https://doi.org/10.1093/gji/ggx539
• Probabilistic point source inversion of strong-motion data in 3-D media using pattern recognition: A case study for the 2008 Mw5.4 Chino Hills earthquake
  
  Käufl, P., Valentine, A., & Trampert, J. (2016). Probabilistic point source inversion of strong-motion data in 3-D media using pattern recognition: A case study for the 2008 Mw5.4 Chino Hills earthquake. Geophysical Research Letters, 43, 8492-8498. https://doi.org/10.1002/2016gl069887
• Using pattern recognition to infer parameters governing mantle convection
  
  Atkins, S., Valentine, A., Tackley, P., & Trampert, J. (2016). Using pattern recognition to infer parameters governing mantle convection. Physics of the Earth and Planetary Interiors, 257, 171-186. https://doi.org/10.1016/j.pepi.2016.05.016
• Solving probabilistic inverse problems rapidly with prior samples
  
  Käufl, P., Valentine, A., de Wit, R., & Trampert, J. (2016). Solving probabilistic inverse problems rapidly with prior samples. Geophysical Journal International, 205, 1710-1728. https://doi.org/10.1093/gji/ggw108
• An introduction to learning algorithms and potential applications in geomorphometry and earth surface dynamics
  
  Valentine, A., & Kalnins, L. (2016). An introduction to learning algorithms and potential applications in geomorphometry and earth surface dynamics. Earth Surface Dynamics, 4, 445-460. https://doi.org/10.5194/esurf-2016-6
• The impact of approximations and arbitrary choices on geophysical images
  
  Valentine, A., & Trampert, J. (2016). The impact of approximations and arbitrary choices on geophysical images. Geophysical Journal International, 204, 59-73. https://doi.org/10.1093/gji/ggv440
• Robust and fast probabilistic source parameter estimation from near-field displacement waveforms using pattern recognition
  
  Käufl, P., Valentine, A., de Wit, R., & Trampert, J. (2015). Robust and fast probabilistic source parameter estimation from near-field displacement waveforms using pattern recognition. Bulletin of the Seismological Society of America, 105, 2299-2312. https://doi.org/10.1785/0120150010
• Bayesian inversion of free oscillations for Earth's radial (an)elastic structure
  
  de Wit, R., Käufl, P., Valentine, A., & Trampert, J. (2014). Bayesian inversion of free oscillations for Earth’s radial (an)elastic structure. Physics of the Earth and Planetary Interiors, 237, 1-17. https://doi.org/10.1016/j.pepi.2014.09.004
• A framework for fast probabilistic centroid-moment-tensor determination -- Inversion of regional static displacement measurements
  
  Käufl, P., Valentine, A., O’Toole, T., & Trampert, J. (2014). A framework for fast probabilistic centroid-moment-tensor determination -- Inversion of regional static displacement measurements. Geophysical Journal International, 196, 1676-1693. https://doi.org/10.1093/gji/ggt473
• Bayesian inference of Earth's radial seismic structure from body wave travel times using neural networks
  
  de Wit, R., Valentine, A., & Trampert, J. (2013). Bayesian inference of Earth’s radial seismic structure from body wave travel times using neural networks. Geophysical Journal International, 195(1), 408–422. https://doi.org/10.1093/gji/ggt220
• Discovery and analysis of topographic features using learning algorithms: A seamount case-study
  
  Valentine, A., Kalnins, L., & Trampert, J. (2013). Discovery and analysis of topographic features using learning algorithms: A seamount case-study. Geophysical Research Letters, 40, 3048-3054. https://doi.org/10.1002/grl.50615
• Earthquake source parameters from GPS-measured static displacements with potential for real‐time application
  
  O’Toole, T., Valentine, A., & Woodhouse, J. (2013). Earthquake source parameters from GPS-measured static displacements with potential for real‐time application. Geophysical Research Letters, 40, 60-65. https://doi.org/10.1029/2012gl054209
• Assessing the uncertainties on seismic source parameters: Towards realistic error estimates for centroid-moment-tensor determinations
  
  Valentine, A., & Trampert, J. (2012). Assessing the uncertainties on seismic source parameters: Towards realistic error estimates for centroid-moment-tensor determinations. Physics of the Earth and Planetary Interiors, 210-211, 36-49. https://doi.org/10.1016/j.pepi.2012.08.003
• Centroid-Moment Tensor Inversions Using High-Rate GPS Waveforms
  
  O’Toole, T., Valentine, A., & Woodhouse, J. (2012). Centroid-Moment Tensor Inversions Using High-Rate GPS Waveforms. Geophysical Journal International, 191, 257-270. https://doi.org/10.1111/j.1365-246x.2012.05608.x
• Data space reduction, quality assessment and searching of seismograms: autoencoder networks for waveform data
  
  Valentine, A., & Trampert, J. (2012). Data space reduction, quality assessment and searching of seismograms: autoencoder networks for waveform data. Geophysical Journal International, 189, 1183-1202. https://doi.org/10.1111/j.1365-246x.2012.05429.x
• Approaches to automated data selection for global seismic tomography
  
  Valentine, A., & Woodhouse, J. (2010). Approaches to automated data selection for global seismic tomography. Geophysical Journal International, 182, 1001-1012. https://doi.org/10.1111/j.1365-246x.2010.04658.x
• Reducing errors in seismic tomography: combined inversion for sources and structure
  
  Valentine, A., & Woodhouse, J. (2010). Reducing errors in seismic tomography: combined inversion for sources and structure. Geophysical Journal International, 180, 847-857. https://doi.org/10.1111/j.1365-246x.2009.04452.x