Acta Geophysica最新文献

In this study, the annual, seasonal and monthly trends of total cloud cover (TCC) and associated climatic variables are investigated for a period of 63 (1959–2022) years in Iran based on ERA5 reanalysis data extracted from ECMWF. To analyze temporal trends, the Mann–Kendall test is used. The geographical location within Iran, especially distance from moisture sources and different atmospheric systems, influences cloudiness such that TCC decreases from north to south and from west to east. With respect to seasons, the highest and lowest average TCC is observed in winter and summer, respectively. The annual trend analysis reveals a decreasing trend in TCC (i.e., Mann–Kendall’s tau is negative: −0.40 per decade). On the monthly scale, a statistically significant decrease in TCC occurs during January, February, March, June, August, November, and December. There is a significant decreasing trend in all seasons, among which the maximum decreasing trend is observed in the summer season with a value of −0.31 per decade. Examining the trends of climatic variables shows that on all three temporal scales (annual, seasonal, and monthly) the number of rainy days (NRD) decreases and temperature (T) increases. Spatial analysis of trends (seasonal, annual) suggests the highest decrease in TCC in the west, northwest, east, and southeast, whereas the lowest decrease is in the center of Iran. Spatially, the T trend (annually and spring, summer, and winter seasons) indicates a consistent increase in temperature in the central and eastern parts of Iran. The spatial trend (annual and seasonal) of NRD in the limited parts of northwestern Iran exhibits the highest increasing trend. The results of investigating the anomalies in TCC relative to the long-term average amount of cloud cover on annual and seasonal scales show zero anomalies in most of the years (67% on an annual scale and 73% in summer and 71% in winter).

This study compared earthquake location estimation using grid search and manta ray foraging optimization algorithm for synthetic and real earthquakes data from Van, Turkey. Both locating methods worked well, and they achieved similar results. The horizontal coordinates (latitude and longitude) of the earthquake were obtained successfully with both methods, from the inversion of the arrival times calculated from the noisy and noise-free synthetic earthquake data. However, there was some deviation in depth parameter for the noisy data. The location parameters obtained from the inversion of the real earthquake data using grid search and manta ray foraging optimization methods were in accordance with the solutions presented in previous studies. The depth parameters for the Van earthquakes did not fully match those in the previous studies, possibly due to differences in crustal velocity models. The depth parameters obtained for both Van earthquakes using both methods performed in this study are self-consistent at around 24 km. In addition, Disaster and Emergency Management Presidency and German Research Centre seismology centres also reached depth solutions near those in this study. The grid search method has some disadvantages compared with the manta ray foraging method, as it must be applied gradually, and delays reaching a solution. The manta ray foraging method is an easy, fast way to determine the kinematic location of earthquake hypocentres.

The short-term rockburst prediction in underground engineering plays a significant role in the safety of the workers and equipment. Due to the complex link between microseismicity and the rockburst occurrence, prediction of short-term rockburst severity is always challenging. It is, therefore, necessary to develop an intelligent model that can predict rockbursts with high accuracy. Besides the predicting capability, it is essential to understand the model’s interpretability regarding the decisions to ensure reliability, trust and accountability. Accordingly, this paper employs the knowledge of explainable artificial intelligences (XAI) by proposing a novel glass-box machine learning model: explainable boosting machine (EBM) to predict the short-term rockburst. Microseismic (MS) data obtained from the underground engineering projects are utilized to build the model, which is also compared with the black-box random forest (RF) model. The result shows that EBM can accurately predict the rockburst severity with high accuracy, while providing with the underlined reasoning behind the prediction from the global and local perspectives. The EBM global explanation reveals that MS energy followed by MS apparent volume and the MS events is the most contributing factor to determining the Rockburst severity. It also gives insights into the relationship between MS factors and rockburst risks, delivering how various MS parameters impact the model predictions. The local explanation extracts the understanding of wrongly predicted samples. The interpretability and transparency of the proposed method will facilitate understanding the model’s decision which adds effective guidance evaluating the short-term rockburst risks.

Aflaj refers to a traditional irrigation system found in Oman, which has been used for centuries to sustainably manage groundwater resources. These resources play a vital role in meeting various consumption needs, including agriculture, domestic, and industrial. The article, for the first time, introduces the concept of “hydraulics of aflaj”, emphasizing the importance of accurate information about interaction of falaj and aquifer and also flow within their tunnels. The study utilizes the mechanisms of horizontal wells to simulate the interaction between the aquifer and the falaj tunnel, employing the meshless local Petrov–Galerkin numerical model to compute groundwater head of aquifer. The model is applied to a real test case in the Loba aquifer of Malaysia, demonstrating improved accuracy compared to previous models based on evaluation indices such as MAE, RMSE, MAPE, NSE and p-bias. The findings of the proposed model show good agreement.