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Apsu: A wireless multichannel receiver system for surface nuclear magnetic resonance groundwater investigations

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DOI

Surface nuclear magnetic resonance (surface NMR) has the potential to be an important geophysical method for groundwater investigations, but the technique suffers from a poor signal-to-noise ratio (SNR) and long measurement times. We present a new wireless, multichannel surface-NMR receiver system (called Apsu) designed to improve field deployability and minimize instrument dead time. It is a distributed wireless system consisting of a central unit and independently operated data acquisition boxes each with three channels that measure either the NMR signal or noise for reference noise cancellation. Communication between the central unit and the data acquisition boxes is done through long-distance Wi-Fi and recordings are retrieved in real time. The receiver system employs differential coils with low-noise preamplifiers and high-resolution wide dynamic-range acquisition boards. Each channel contains multistage amplifiers, short settling-time filters, and two 24 bit analog-to-digital converters in dual-gain mode sampling at 31.25 kHz. The system timing is controlled by GPS clock, and sample jitter between channels is less than 12 ns. Separated transmitter/receiver coils and continuous acquisition allow NMR signals to be measured with zero instrument dead time. In processed data, analog and digital filters cause an effective dead time of 5.8 ms including excitation current decay. Synchronization with an independently operated transmitter system is done with a current probe monitoring the NMR excitation pulses. The noise density measured in a shorted-input test is 1.8 nV Hz-1/2.We verify the accuracy of the receiver system with measurements of a magnetic dipole source and by comparing our NMR data with data obtained using an existing commercial instrument. The applicability of the system for reference noise cancellation is validated with field data.

Original languageEnglish
JournalGeoscientific Instrumentation, Methods and Data Systems
Volume8
Issue1
Pages (from-to)1-11
Number of pages11
ISSN2193-0856
DOIs
Publication statusPublished - Jan 2019

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