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Magnitude estimation is a critical task in seismology, and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution. In this context, we propose the Earthquake Graph Network (EQGraphNet) to enhance the performance of single-station magnitude estimation. The backbone of the proposed model consists of eleven convolutional neural network layers and ten RCGL modules, where a RCGL combines a Residual Connection and a Graph convolutional Layer capable of mitigating the over-smoothing problem and simultaneously extracting temporal features of seismic signals. Our work uses the STanford EArthquake Dataset for model training and performance testing. Compared with three existing deep learning models, EQGraphNet demonstrates improved accuracy for both local magnitude and duration magnitude scales. To evaluate the robustness, we add natural background noise to the model input and find that EQGraphNet achieves the best results, particularly for signals with lower signal-to-noise ratios. Additionally, by replacing various network components and comparing their estimation performances, we illustrate the contribution of each part of EQGraphNet, validating the rationality of our approach. We also demonstrate the generalization capability of our model across different earthquakes occurring environments, achieving mean errors of ±0.1 units. Furthermore, by demonstrating the effectiveness of deeper architectures, this work encourages further exploration of deeper GNN models for both multi-station and single-station magnitude estimation.

Data-driven prediction of time series is significant in many scientific research fields such as global climate change and weather forecast. For global monthly mean temperature series, considering the strong potential of deep neural network for extracting data features, this paper proposes a data-driven model, ResGraphNet, which improves the prediction accuracy of time series by an embedded residual module in GraphSAGE layers. The experimental results of a global mean temperature dataset, HadCRUT5, show that compared with 11 traditional prediction technologies, the proposed ResGraphNet obtains the best accuracy. The error indicator predicted by the proposed ResGraphNet is smaller than that of the other 11 prediction models. Furthermore, the performance on seven temperature datasets shows the excellent generalization of the ResGraphNet. Finally, based on our proposed ResGraphNet, the predicted 2022 annual anomaly of global temperature is 0.74722 °C, which provides confidence for limiting warming to 1.5 °C above pre-industrial levels.

The Sulige tight gas field is presently the largest gas field in China. Owing to the ultralow permeability and strong heterogeneity of the reservoirs in Sulige, the number of production wells has exceeded 3,000, keeping the stable gas supply in the decade. Thus, the daily production prediction of gas wells is significant for monitoring production and for implementing and evaluating stimulation measures. Therefore, on the basis of the three data-driven time series approaches, the daily production of 1692 wells over 10 years was mining for the daily production prediction of wells in Sulige. The jointed deep long short-term memory and fully connected neural network (DLSTM-FNN) model was proposed by introducing the recurrent neural network's sequential expression ability and was compared with random forest (RF) and support vector regression (SVR). After the daily production predictions of thousands of wells in Sulige, the proposed DLSTM-FNN model significantly improved the time series prediction accuracy and efficiency in the short training samples and had strong availability and practicability in the Sulige tight gas field.