Opportunities for Observing and Sensing Atmospheric Electromagnetic Anomalies

The Intelligence Advanced Research Projects Activity (IARPA) seeks information regarding innovative signal processing algorithms and sensing approaches used to characterize the D-region of the ionosphere (60–90 km).

Currently, the D-region is characterized by remote sensing of very low frequency (VLF) signals and comparing that propagation with modeling results.[i]-[ii],[iii] However, this approach is highly nonlinear and fits the definition of an ill-posed problem in that itis quite difficult to solve and does not yield a unique one-to-one correspondence between electron density profiles in the Earth-ionosphere waveguide and signals at the receiver(s).

In the past decade, inversions of highly nonlinear, ill-posed problems have been successfully tackled with machine learning and artificial neural networks (ANNs).[iv]-[v],[vi],[vii] While significant progress has been made in signal processing,[viii]-[ix][x][xi] the data used by these algorithms is obtained by sensors that are uncoordinated and loosely networked.

Additionally, the uncoordinated environmental monitoring sensors used to collect data cannot provide real-time estimates of the D-region, needed to support high-accuracy detection, geolocation and classification solutions.

IARPA seeks to characterize and better understand the D-region and the interaction of this region of the ionosphere with electromagnetic (EM) anomalies caused by space weather events, lightning, manmade sources, etc.[xii],[xiii] and whether the resulting D-region characterization can be used to identify, map, and track these EM anomalies. Current capabilities do not allow for the rapid parametrization of the D-region.

Any methodology that could lead to this rapid parametrization is of interest as we seek to facilitate a domain awareness capability that currently does not exist.[xiv] Responses to this RFI are due no later than 5:00 p.m., Eastern Time, Monday, 17 JUN 2024. All submissions must be electronically submitted to dni-iarpa-rfi-24-05@iarpa.gov as a PDF document.

Inquiries to this RFI must be submitted to dni-iarpa-rfi-24-05@iarpa.gov no later than 5:00 p.m., Eastern Time, Monday, 17 JUN 2024. Do not send questions with proprietary content. No telephone inquiries will be accepted. [i] (U) S. A. Cummer, Lightning and ionospheric remote sensing using VLF/ELF radio atmospherics. Stanford University, 1997. [ii] (U) M. Go?kowski, S. Sarker, C. Renick, R. Moore, M. Cohen, A. Ku?ak,J.

M?ynarczyk, and J. Kubisz, “Ionospheric d region remote sensing using elf sferic group velocity,” Geophysical Research Letters, vol. 45, no. 23, pp.

12–739, 2018. [iii] (U) J. C. McCormick, “D region tomography: A technique for ionospheric imaging using lightning-generated sferics and inverse modeling,” Ph.D. dissertation, Georgia Institute of Technology, 2019. [iv] (U) C. Zhang, C. Frogner, M. Araya-Polo, and D. Hohl, “Machine-learning based automated fault detection in seismic traces,” in 76th EAGE Conference and Exhibition 2014, vol. 2014, no. 1. European Association of Geoscientists & Engineers, 2014, pp.

1–5. [v] (U) Y. Wu, Y. Lin, Z. Zhou, and A. Delorey, “Seismic-net: A deep densely connected neural network to detect seismic events,” arXiv preprint arXiv:1802.02241, 2018. [vi] (U) M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” Journal of Computational physics, vol. 378, pp.

686–707, 2019. [vii] (U) C. Jiang, D. Zhang, and S. Chen, “Lithology identification from well log curves via neural networks with additional geologic constraint,” Geophysics, vol. 86, no. 5, pp.

IM85–IM100, 2021. [viii] (U) J. C. McCormick, “D region tomography: A technique for ionospheric imaging using lightning-generated sferics and inverse modeling,” Ph.D. dissertation, Georgia Institute of Technology, 2019. [ix] (U) N. Gross and M. Cohen, “Vlf remote sensing of the d region ionosphere using neural networks,” Journal of Geophysical Research: Space Physics, vol. 125, no. 1, p. e2019JA027135, 2020. [x] (U) D. K. Richardson and M. B. Cohen, “Seasonal variation of the d-region ionosphere: Very low frequency (vlf) and machine learning models,” Journal of Geophysical Research: Space Physics, vol. 126, no. 9, p.e2021JA029689, 2021. [xi] (U) J. R. Wait, Characteristics of the Earth-ionosphere waveguide for VLF radio waves.

US Department of Commerce, National Bureau of Standards, 1964, vol. 300. [xii] (U) A. P. Mitra, Ionospheric effects of solar flares. Springer, 1974, vol. 46. [xiii] (U) R. Helliwell, J. Katsufrakis, and M. Trimpi, “Whistler-induced amplitude perturbation in vlf propagation,” Journal of Geophysical Research, vol. 78, no. 22, pp. 4679–4688, 1973. [xiv] (U) J. R. Wait, Characteristics of the Earth-ionosphere waveguide for VLF radio waves. US Department of Commerce, National Bureau of Standards, 1964, vol. 300.