Wireless communication is an essential part of daily life for users globally with applications in medical devices, cellular phones, Internet of Things nodes, and others. Accordingly, there is a need to understand the patterns and properties of radio frequency spectrum use by acquiring accurate spectrum utilization measurements. However, the massive storage volume needed to execute spectrum surveys-especially when a fast sampling rate is used-is an impeding factor in terms of cost and ease-of-access. In this article, a probabilistic efficient storage algorithm (PESA) is proposed to facilitate high-accuracy, time-domain spectrum surveys conducted at a fast sample acquisition rate to detect sporadic spectrum occupancy patterns that could be on the order of microseconds. PESA divides the dynamic range of a monitoring equipment into bins-each represented by one component of a Gaussian mixture model (GMM). Windows of activity and inactivity in the measurements are established by comparing with a threshold and then indicators to the GMM component that best describes a window are recorded. Hence, reducing required storage volume. Results demonstrate that ≈ 99% reduction in storage volume is achievable while maintaining an accurate estimation of channel utilization and activity/inactivity periods. Furthermore, a Lab-VIEW implementation of PESA on a hardware platform was executed and used to survey Wi-Fi channel 1 in a healthcare environment for seven consecutive hours. Although more than 25 billion samples were observed, resulting data only occupied 96.28 megabytes.