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Grombacher, D., Griffiths, M. P., Kass, M. A. & Larsen, J. J. (2024). An investigation of factors affecting the depths of steady-state surface nuclear magnetic resonance signals. Geophysics, 89(2), J9-J17. https://doi.org/10.1190/geo2023-0068.1

Khare, S. K., McLachlan, P., Maurya, P. K. & Larsen, J. J. (2024). An optimized and hybrid gating scheme for the suppression of very low-frequency radios in transient electromagnetic systems. Geoscientific Instrumentation, Methods and Data Systems, 13(1), 27-41. https://doi.org/10.5194/gi-13-27-2024

Larsen, J. J., Kass, M. A., Grombacher, D. & Griffiths, M. P. (2024). Feasibility of Surface Nuclear Magnetic Resonance With Absurdly Long Excitation Pulses. Geophysical Research Letters, 51(20), Article e2024GL110826. https://doi.org/10.1029/2024GL110826

Naveed, K., Madsen, L. M., Grombacher, D. & Larsen, J. J. (2024). Processing Time-domain Induced Polarization Data Using Variational Mode Decomposition. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-13. Article 5905713. https://doi.org/10.1109/TGRS.2024.3362290

Griffiths, M. P., Grombacher, D., Kass, M. A., Vang, M., Liu, L. & Larsen, J. J. (2023). A surface NMR forward in a dot product. Geophysical Journal International, 234(3), 2284-2290. https://doi.org/10.1093/gji/ggad203

Asif, M. R., Foged, N., Bording, T. S., Larsen, J. J. & Christiansen, A. V. (2023). DL-RMD: a geophysically constrained electromagnetic resistivity model database for deep learning applications. Earth System Science Data, 15(3), 1389-1401. https://doi.org/10.5194/essd-2022-345, https://doi.org/10.5194/essd-15-1389-2023

Liu, L., Grombacher, D., Griffiths, M., Vang, M. & Larsen, J. J. (2023). Signal Processing Steady-State Surface NMR Data. IEEE Transactions on Instrumentation and Measurement, 72, Article 6502313. https://doi.org/10.1109/TIM.2023.3264033

Vang, M., Grombacher, D., Griffiths, M. P., Liu, L. & Larsen, J. J. (2023). Technical note: High-density mapping of regional groundwater tables with steady-state surface nuclear magnetic resonance - three Danish case studies. Hydrology and Earth System Sciences, 27(16), 3115-3124. https://doi.org/10.5194/hess-27-3115-2023

Naveed, K., Meldgaard Madsen, L., Grombacher, D. & Larsen, J. J. (2023). Variational Mode Decomposition-Based Processing of Time-Domain Induced Polarization Data. Abstract from NSG2023 29th European Meeting of Environmental and Engineering Geophysics, Edinburgh , United Kingdom. https://doi.org/10.3997/2214-4609.202320133

Asif, M. R., Bording, T. S., Maurya, P. K., Zhang, B., Fiandaca, G., Grombacher, D. J., Christiansen, A. V., Auken, E. & Larsen, J. J. (2022). A Neural Network-Based Hybrid Framework for Least-Squares Inversion of Transient Electromagnetic Data. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 4503610. https://doi.org/10.1109/TGRS.2021.3076121

Kass, M. A., Grombacher, D., Griffiths, M., Vang, M. Ø., Liu, L. & Larsen, J. J. (2022). A steady-state approach to surface nuclear magnetic resonance. In Proceedings of the Symposium on the Application of Geophyics to Engineering and Environmental Problems, SAGEEP (pp. 54). J and N Group, Ltd.. https://www.eegs.org/assets/docs/Symposium/2022SAGEEP/AbstractsbyAuthor/Kass%2C%20A%20STEADY-STATE%20APPROACH%20TO%20SURFACE%20NUCLEAR%20MAGNETIC%20RESONANCE%20-%20SAGEEP%202022%20-%20205367449.pdf

Asif, M. R., Maurya, P. K., Foged, N., Larsen, J. J., Auken, E. & Christiansen, A. V. (2022). Automated transient electromagnetic data processing for ground-based and airborne systems by a deep learning expert system. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5919814. https://doi.org/10.1109/TGRS.2022.3202304

Asif, M. R., Larsen, J. J., Auken, E. & Christiansen, A. V. (2022). Deep Convolutional Auto-Encoder for Automated Processing of Airborne Time-Domain Electromagnetic Data. 1-5. Paper presented at NSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics, Beograd, Serbia. https://doi.org/10.3997/2214-4609.202220038

Asif, M. R., Maurya, P. K., Christiansen, A. V., Larsen, J. J. & Auken, E. (2022). Deep learning based expert system to automate time-domain electromagnetic data processing. In 34th Symposium on the Application of Geophysics to Engineering and Environmental Problems, SAGEEP 2022 (pp. 6). J and N Group, Ltd..

Larsen, J. J., Langhof, R. B., Kjær, M. W., Vang, M., Liu, L., Griffiths, M. & Grombacher, D. (2022). Efficient processing of surface NMR data with spectral analysis. Geophysical Journal International, 229(1), 286-298. https://doi.org/10.1093/gji/ggab472

Kjaer-Rasmussen, A., Griffiths, M. P., Grombacher, D. & Larsen, J. J. (2022). Fast removal of powerline harmonic noise from surface NMR datasets using a projection-based approach on graphical processing units. IEEE Geoscience and Remote Sensing Letters, 19, Article 8024005. https://doi.org/10.1109/LGRS.2021.3118064

Griffiths, M. P., Grombacher, D., Liu, L., Vang, M. Ø. & Larsen, J. J. (2022). Forward Modeling Steady-State Free Precession in Surface NMR. IEEE Geoscience and Remote Sensing Letters, 60, Article 4513510. https://doi.org/10.1109/TGRS.2022.3221624

Grombacher, D., Griffiths, M. P., Liu, L., Vang, M. & Larsen, J. J. (2022). Frequency Shifting Steady-State Surface NMR Signals to Avoid Problematic Narrowband-Noise Sources. Geophysical Research Letters, 49(7), Article e2021GL097402. https://doi.org/10.1029/2021GL097402

Asif, M. R., Foged, N., Maurya, P. K., Grombacher, D. J., Christiansen, A. V., Auken, E. & Larsen, J. J. (2022). Integrating neural networks in least-squares inversion of airborne time-domain electromagnetic data. Geophysics, 87(4), E177-E187. https://doi.org/10.1190/geo2021-0335.1

Asif, M. R., Maurya, P. K., Larsen, J. J. & Christiansen, A. V. (2022). Investigating the Integration of Neural Networks in Least-Squares Method for Airborne Electromagnetic Data Inversion. 1-5. Paper presented at NSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics, Beograd, Serbia. https://doi.org/10.3997/2214-4609.202220034

McLachlan, P. J., Khare, S. K., Grombacher, D., Larsen, J. J., A. Christiensen, A. & Luria, J. C. Z. (2022). On The Presence of Correlated Noise in Transient Electromagnetic (Tem) Monitoring Data. 1-5. Abstract from NSG2022 28th European Meeting of Environmental and Engineering Geophysics, Belgrade , Serbia. https://doi.org/10.3997/2214-4609.202220119

McLachlan, P. J., Khare, S. K., Grombacher, D., Larsen, J. J., Christensen, A. & Zamora Luria, J. C. (2022). On The Presence of Correlated Noise in Transient Electromagnetic (Tem) Monitoring Data. In NSG2022 28th European Meeting of Environmental and Engineering Geophysics (pp. 1-5). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202220119

Larsen, J. J., Lévy, L. & Asif, M. R. (2022). Removal of powerline noise in geophysical data sets with a scientific machine-learning based approach. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5923410. https://doi.org/10.1109/TGRS.2022.3223737

Kremer, T., Irons, T., Müller-Petke, M. & Juul Larsen, J. (2022). Review of Acquisition and Signal Processing Methods for Electromagnetic Noise Reduction and Retrieval of Surface Nuclear Magnetic Resonance Parameters. Surveys in Geophysics, 43(4), 999-1053. https://doi.org/10.1007/s10712-022-09695-3

Gaikwad, N., Liu, L., Griffiths, M. P., Vang, M. Ø., Grombacher, D. & Larsen, J. J. (2022). Thermal Model of the Apsu Transmitter for Lightweight and Compact Heat Sink Design. 11-13. Abstract from The 8th International Workshop on Magnetic Resonance Sounding, Strasbourg, France. https://mrs2021.sciencesconf.org/data/pages/proceedings_MRS2021_distrib_v2.pdf

Larsen, J. J., Griffiths, M., Vang, M., Liu, L. & Grombacher, D. (2021). Apsu - A compact surface NMR instrument for groundwater investigations. SEG Technical Program Expanded Abstracts, 2021-September, 3135-3139. https://doi.org/10.1190/segam2021-3582046.1

Kass, M. A., Auken, E., Larsen, J. J. & Christiansen, A. V. (2021). A towed magnetic gradiometer array for rapid, detailed imaging of utility, geological, and archaeological targets. Geoscientific Instrumentation, Methods and Data Systems, 10(2), 313-323. https://doi.org/10.5194/gi-10-313-2021

Barfod, A. A. S., Lévy, L. & Larsen, J. J. (2021). Automatic processing of time domain induced polarization data using supervised artificial neural networks. Geophysical Journal International, 224(1), 312-325. https://doi.org/10.1093/gji/ggaa460

Asif, M. R., Larsen, J. J., Maurya, P. K., Christiansen, A. V. & Auken, E. (2021). Complete automation of time-domain transient electromagnetic data processing using deep convolutional autoencoder. Abstract from AGU 2021, United States.

Asif, M. R., Bording, T. S., Barfod, A. S., Grombacher, D. J., Maurya, P. K., Christiansen, A. V., Auken, E. & Larsen, J. J. (2021). Effect of Data Pre-Processing on the Performance of Neural Networks for 1-D Transient Electromagnetic Forward Modeling. IEEE Access, 9, 34635-34646. https://doi.org/10.1109/ACCESS.2021.3061761

Griffiths, M. P., Grombacher, D. J. & Larsen, J. J. (2021). Efficient numerical Bloch solutions for multipulse surface NMR. Geophysical Journal International, 227(3), 1905-1916. https://doi.org/10.1093/gji/ggab321

Kass, M. A., Christiansen, A. V., Auken, E. & Larsen, J. J. (2021). Efficient reduction of powerline signals in magnetic data acquired from a moving platform. IEEE Transactions on Geoscience and Remote Sensing, 59(8), 7137-7146. Article 9246269. https://doi.org/10.1109/TGRS.2020.3029658

Bording, T. S., Asif, M. R., Barfod, A. S., Larsen, J. J., Zhang, B., Grombacher, D. J., Christiansen, A. V., Engebretsen, K. W., Pedersen, J. B., Maurya, P. K. & Auken, E. (2021). Machine learning based fast forward modelling of ground-based time-domain electromagnetic data. Journal of Applied Geophysics, 187, Article 104290. https://doi.org/10.1016/j.jappgeo.2021.104290

Grombacher, D., Liu, L., Osterman, G. K. & Larsen, J. J. (2021). Mitigating Narrowband Noise Sources Close to the Larmor Frequency in Surface NMR. IEEE Geoscience and Remote Sensing Letters, 18(8), 1376-1380. https://doi.org/10.1109/LGRS.2020.3000639

Grombacher, D., Liu, L., Griffiths, M. P., Vang, M. & Larsen, J. J. (2021). Steady-State Surface NMR for Mapping of Groundwater. Geophysical Research Letters, 48(23), Article e2021GL095381. https://doi.org/10.1029/2021GL095381

Larsen, J. J., Pedersen, S. S., Foged, N. & Auken, E. (2021). Suppression of very low frequency radio noise in transient electromagnetic data with semi-tapered gates. Geoscientific Instrumentation, Methods and Data Systems, 10(1), 81-90. Article 8. https://doi.org/10.5194/gi-10-81-2021

Osterman, G., Haldrup, J., Larsen, J. J., Auken, E. & Grombacher, D. (2020). A cautionary tale: How phase compensation during surface nuclear magnetic resonance inversion conceals forward modelling errors. Journal of Applied Geophysics, 173, Article 103905. https://doi.org/10.1016/j.jappgeo.2019.103905

Grombacher, D., Kass, M. A., Auken, E. & Larsen, J. J. (2020). An approximate fast-mapping approach to the surface NMR forward problem. Geophysical Journal International, 221(2), 928-937. https://doi.org/10.1093/gji/ggaa044

Larsen, J. J., Liu, L., Grombacher, D., Osterman, G. K. & Auken, E. (2020). Apsu — A new compact surface nuclear magnetic resonance system for groundwater investigation. Geophysics, 85(2), JM1-JM11. https://doi.org/10.1190/geo2018-0779.1

Hansen, B., Christiansen, A. V., Dalgaard, T., Jørgensen, F., Iversen, B. V., Larsen, J. J., Kjærgaard, C., Jacobsen, B. H., Auken, E., Hojberg, A. L. & Schaper, S. (2020). Danish review on advances in assessing: N retention in the subsurface in relation to future targeted N-regulation of agriculture. GEUS, Geological Survey of Denmark and Greenland. GEUS Rapport Vol. 2020 No. 11 https://mapfield.dk/media/21858/d26_synthesis_report_mapfield.pdf

Asif, M. R., Bording, T. S., Barfod, A. S., Auken, E. & Larsen, J. J. (2020). Effect of Data Normalization on Neural Networks for the Forward Modelling of Transient Electromagnetic Data,. Abstract from Near Surface Geoscience 2020.

Griffiths, M. P., Grombacher, D. & Larsen, J. J. (2020). Efficient Bloch-equation solvers for surface nuclear magnetic Resonance. Abstract from AGU Fall Meeting 2020.

Asif, M. R., Bording, T. S., Barfod, A. A. S., Zhang, B., Larsen, J. J. & Auken, E. (2020). Improving computational efficiency of forward modelling for ground-based time-domain electromagnetic data using neural networks. Abstract from EGU:General assembly 2020 , Vienna , Austria. https://doi.org/10.5194/egusphere-egu2020-7067

Grombacher, D., Liu, L., Kass, M. A., Osterman, G., Fiandaca, G., Auken, E. & Larsen, J. J. (2020). Inverting surface NMR free induction decay data in a voltage-time data space. Journal of Applied Geophysics, 172, Article 103869. https://doi.org/10.1016/j.jappgeo.2019.103869

Griffiths, M. P., Grombacher, D. & Larsen, J. J. (2020). Parallelized Hybrid Bloch Solver for Surface Nuclear Magnetic Resonance. Abstract from Near Surface Geoscience 2020.

Liu, L., Grombacher, D., Auken, E. & Juul Larsen, J. (2019). Apsu: A wireless multichannel receiver system for surface nuclear magnetic resonance groundwater investigations. Geoscientific Instrumentation, Methods and Data Systems, 8(1), 1-11. https://doi.org/10.5194/gi-8-1-2019

Liu, L., Grombacher, D. J., Auken, E. & Larsen, J. J. (2019). Complex envelope retrieval for surface nuclear magnetic resonance data using spectral analysis. Geophysical Journal International, 217(2), 894-905. https://doi.org/10.1093/gji/ggz068

Ma, X., Hegarty, P. J., Jørgensen, K. F. & Larsen, J. J. (2019). Nonlinear distortion reduction in sound zones by constraining individual loudspeaker control effort. Journal of the Audio Engineering Society, 67(9), 641-654. https://doi.org/10.17743/jaes.2019.0015

Kremer, T., Juul Larsen, J. & Nguyen, F. (2019). Processing harmonic EM noise with multiple or unstable frequency content in surface NMR surveys. Geophysical Journal International, 219(2), 753-775. https://doi.org/10.1093/gji/ggz307

Auken, E., Foged, N., Larsen, J. J., Lassen, K. V. T., Maurya, P. K., Møller Dath, S. & Eiskjær, T. T. (2019). tTEM — A towed transient electromagnetic system for detailed 3D imaging of the top 70 m of the subsurface. Geophysics, 84(1), E13-E22. https://doi.org/10.1190/geo2018-0355.1

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Jakob Juul Larsen