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Bickel, V., Moseley, B., Lopez-Francos, I., Shirley, M. (2021). Peering into Lunar Permanently Shadowed Regions with Deep Learning. Nature Communications. [press coverage] Used machine learning to enhance images of permanently shadowed regions on the Moon, allowing us to see features within these regions down to 3 m in size for the first time.
Moseley, B., Bickel, V., Lopez-Francos, I. G., & Rana, L. (2021). Extreme Low-Light Environment-Driven Image Denoising Over Permanently Shadowed Lunar Regions With a Physical Noise Model. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). [blog post] Used a combination of physical modelling and deep learning to remove noise from images of permanently shadowed regions on the lunar surface, aiding the search for water and future mission planners.
Moseley, B., Markham, A., & Nissen-Meyer, T. (2021). Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations. ArXiv. [code] Proposed a new method for solving differential equations which is able to scale to large problems by using physics-informed neural networks and a divide-and-conquer strategy.
Reinwald, M., Moseley, B., Szenicer, A., Nissen-Meyer, T., Oduor, S., Vollrath, F., … Mortimer, B. (2021). Seismic localization of elephant rumbles as a monitoring approach. Journal of The Royal Society Interface. [press coverage] [blog post] [code] Showed that the location of elephants can be accurately determined by using the ground vibrations they make whilst calling each other and a positioning algorithm which is able to say how uncertain it is.
Moseley, B., Markham, A., & Nissen-Meyer, T. (2020). Solving the wave equation with physics-informed deep learning. ArXiv. Showed that physics-informed neural networks can be used to simulate complex wave physics, and that they potentially offer a faster approach than traditional simulation methods.
Moseley, B., Nissen-Meyer, T., & Markham, A. (2020). Deep learning for fast simulation of seismic waves in complex media. Solid Earth. [code] Discussed in detail the advantages and challenges of using deep learning for simulating seismic waves, and presented multiple deep neural networks which can significantly accelerate traditional methods.
Moseley, B., Bickel, V., Burelbach, J., & Relatores, N. (2020). Unsupervised Learning for Thermophysical Analysis on the Lunar Surface. The Planetary Science Journal. [blog post] Used machine learning to disentangle the underlying factors of variation in the Moon’s surface temperature, allowing us to identify thermal anomalies on the surface and verify existing physical models.
Moseley, B., Bickel, V., Zurich, E., Goettingen, M., López-Francos, I. G., Rana, L., … Zuniga, A. (2020). Low-light image enhancement of permanently shadowed lunar regions with physics-based machine learning. Workshop on Machine Learning and the Physical Sciences, NeurIPS.
Bickel, V., Burelbach, J., Moseley, B., & Relatores, N. (2020). A Big Data and AI-Driven Approach for Anomaly Detection on the Lunar Surface. 51st Lunar and Planetary Science Conference. Program and Abstracts. Used machine learning to classify and group craters on the Moon based on their different physical characteristics, which could aid the search for lunar resources.
Moseley, B., Bickel, V., Burelbach, J., Relatores, N., Angerhausen, D., Soboczenski, F., & Wingo, D. (2019). Unsupervised learning for thermal anomaly detection on the lunar surface. Workshop on Machine Learning and the Physical Sciences, NeurIPS.
Nissen-Meyer, T., Szenicer, A., Moseley, B., Leng, K., Markham, A., Nissen-Meyer, T., … Markham, A. (2019). Seismic wave propagation and inference using deep learning, numerical methods and wave physics. AGUFM.
Burelbach, J., Bickel, V., Moseley, B., & Relatores, N. C. (2019). NASA Frontier Development Lab: Lunar Resource Mapping – Data Fusion and AI-driven Anomaly Detection. Mining Space Summit.
Moseley, B., Markham, A., & Nissen-Meyer, T. (2018). Fast approximate simulation of seismic waves with deep learning. ArXiv. [code] Showed that deep neural networks can accelerate the simulation of seismic waves by multiple orders of magnitude compared to traditional simulation techniques.
Moseley, B., Markham, A., & Nissen-Meyer, T. (2018). Fast approximate simulation of seismic waves with deep learning. Workshop on Machine Learning for Geophysical & Geochemical Signals, NeurIPS.
Moseley, B., Osborne, M., & Benjamin, S. (2018). Bayesian optimisation for variational quantum eigensolvers. Working paper. [code] Showed that optimisation algorithms that are uncertain about their inputs can effectively combat noise in quantum computers, allowing them to accurately and efficiently compute the ground state energy of molecules.
Lecerf, D., Raistrick, D., Caselitz, B., Wingham, M., Bradley, J., & Moseley, B. (2018). 4D broadband towed-streamer assessment, West Africa deep water case study. 80th EAGE Conference and Exhibition.
Moseley, B. (2016). Stack Ghost Suppression. US Patent US20160202376A1. Invented an algorithm which significantly increases the resolution of seismic images by removing unwanted echos caused by reflections from the sea-surface.
Moseley, B. (2015). Post-stack 1-D based broadband processing – A simple and efficient method for removing the ghost. 77th EAGE Conference and Exhibition.
Blunn, L. P., Hodgson, L., Saxton, L., & Moseley, B. (2015). An evaluation of pre-stack broadband processing technologies offshore Namibia. 77th EAGE Conference and Exhibition.