The energy consumption and carbon footprint of cryptocurrencies have always been a popular topic. However, most of the existing studies only focus on one cryptocurrency, Bitcoin, and there is a lack of long-term monitoring studies that summarize all cryptocurrencies. By constructing a time series hash rate/power model, this research obtained the 10-year time series data on energy consumption dataset of global top-25 cryptocurrencies for the first time. Both the temporal coverage and the spatiotemporal resolution of the data exceed previous studies. The results show that Bitcoin’s power consumption only accounts for 58% of the top-25 cryptocurrencies. After China bans cryptocurrencies, the conservative change in global CO₂ emissions from 2020 will be between −0.4% and 4.4%, and Central Asian countries such as Kazakhstan are likely to become areas of rapid growth in carbon emissions from cryptocurrencies.

To distinguish debris flow infrasound from other infrasound sources, previous works have used one-dimensional infrasound shapes and parameters. In this study, we converted infrasound signals into two-dimensional signal time–frequency graphs and created a time–frequency graph dataset containing five common kinds of infrasound. We used deep learning to distinguish debris flow infrasound from other infrasound and improved the deep learning model to enhance the accuracy of debris flow infrasound identification. By improving the LeNet-5 network, we obtained an infrasound signal recognition method for debris flows based on deep learning. After signal preprocessing and model training, this method was able to differentiate target infrasound from environmental infrasound, and a debris flow infrasound recognition accuracy of 84.1% was achieved. The method described in this paper can effectively recognize debris flow infrasound and distinguish it from other environmental infrasound. By such means, more accurate and more timely debris flow disaster warnings may be obtained.

Representational state transfer (REST) is a resource-based service architectural style. It abstracts data and services as resources and accesses them through a unique Uniform Resource Identifier (URI). Compared with traditional Simple Object Access Protocol (SOAP) methods, REST is more concise. It takes full advantage of HyperText Transfer Protocol (HTTP) and has better scalability and extensibility. Based on REST services, this article integrates geographic information, real-time disaster monitoring data, and warning services in a three-dimensional (3D) digital Earth infrastructure and establishes a three-dimensional geological disaster monitoring GIS platform with good service compatibility and extensibility. The platform visually displays geographical and geological information and real-time monitoring data in a three-dimensional Earth, accesses warning model services to implement disaster warnings, and realizes comprehensive information management, monitoring, and warnings of multiple types of geological disasters. This can provide decision support for disaster prevention and relief and improve the informatization of geological disaster prevention and control.