Wavelet estimation is an important part of high-resolution seismic data processing. However, it is difficult to preserve the lateral continuity of geological structures and effectively recover weak geological bodies using conventional deterministic wavelet inversion methods, which are based on the joint inversion of wells with seismic data. In this study, starting from a single well, on the basis of the theory of single-well and multi-trace convolution, we propose a steady-state seismic wavelet extraction method for synchronized inversion using spatial multi-well and multi-well-side seismic data. The proposed method uses a spatially variable weighting function and wavelet invariant constraint conditions with particle swarm optimization to extract the optimal spatial seismic wavelet from multi-well and multi-well-side seismic data to improve the spatial adaptability of the extracted wavelet and inversion stability. The simulated data demonstrate that the wavelet extracted using the proposed method is very stable and accurate. Even at a low signal-to-noise ratio, the proposed method can extract satisfactory seismic wavelets that reflect lateral changes in structures and weak effective geological bodies. The processing results for the field data show that the deconvolution results improve the vertical resolution and distinguish between weak oil and water thin layers and that the horizontal distribution characteristics are consistent with the log response characteristics.
Low-frequency vibroseis acquisition has become a routine operation in land seismic surveys, given the advantages of low-frequency signals in characterizing geological structures and enhancing the imaging of deep exploration targets. The two key points of low-frequency sweep design techniques include controlling the distortion and improving the output energy during the low-frequency stage. However, the vibrators are limited by the maximum flow provided by the hydraulic systems at the low-frequency stage, causing difficulty in satisfying exploration energy requirements. Initially, a theoretical analysis of the low-frequency acquisition performance of vibrators is conducted. A theoretical maximum output force below 10 Hz is obtained by guiding through theoretical formulas and combining actual vibrator parameters. Then, the signal is optimized according to the surface characteristics of the operation area. Finally, detailed application quality control and operational procedures are established. The new low-frequency sweep design method has overcome the maximum flow limitations of the hydraulic system, increased the low-frequency energy, and achieved broadband acquisition. The designed signal has been tested and applied on various types of ground surfaces in the Middle East desert region, yielding good performance. The proposed low-frequency sweep design method holds considerable value for the application of conventional vibroseis in low-frequency acquisition.
Ground source heat pump systems demonstrate significant potential for northern rural heating applications; however, the effectiveness of these systems is often limited by challenging geological conditions. For instance, in certain regions, the installation of buried pipes for heat exchangers may be complicated, and these pipes may not always serve as efficient low-temperature heat sources for the heat pumps of the system. To address this issue, the current study explored the use of solar-energy-collecting equipment to supplement buried pipes. In this design, both solar energy and geothermal energy provide low-temperature heat to the heat pump. First, a simulation model of a solar-ground source heat pump coupling system was established using TRNSYS. The accuracy of this model was validated through experiments and simulations on various system configurations, including varying numbers of buried pipes, different areas of solar collectors, and varying volumes of water tanks. The simulations examined the coupling characteristics of these components and their influence on system performance. The results revealed that the operating parameters of the system remained consistent across the following configurations: three buried pipes, burial depth of 20 m, collector area of 6 m2, and water tank volume of 0.5 m3; four buried pipes, burial depth of 20 m, collector area of 3 m2, and water tank volume of 0.5 m3; and five buried pipes with a burial depth of 20 m. Furthermore, the heat collection capacity of the solar collectors spanning an area of 3 m2 was found to be equivalent to that of one buried pipe. Moreover, the findings revealed that the solar-ground source heat pump coupling system demonstrated a lower annual cumulative energy consumption compared to the ground source heat pump system, presenting a reduction of 5.31% compared to the energy consumption of the latter.
A parallel finite element scheme for 3D resistivity method forward modeling is introduced in this article. The domain decomposition algorithm, along with a message passing interface, is used to implement parallelism. The computational domain is divided into subdomains, and mesh partitioning is combined with load balancing. Unstructured meshes and local mesh refinement strategies are used to realize high precision for complex topography models. Furthermore, an improved linear solver for multi-electrode resistivity method modeling is adopted. Recycling preconditioned conjugate gradient, which is a linear solver, is based on the similarity of linear systems between point sources. The multiple right-hand-side linear systems corresponding to different point source positions are constructed, and the accelerated convergence is obtained through recycling subspace using the linear solver. The computational accuracy and efficiency of the forward scheme for complex topography models are verified using the numerical test results.
To fully exploit the technical advantages of the large-depth and high-precision artificial source electromagnetic method in the complex structure area of southern Sichuan and compensate for the shortcomings of the conventional electromagnetic method in exploration depth, precision, and accuracy, the large-depth and high-precision wide field electromagnetic method is applied to the complex structure test area of the Luochang syncline and Yuhe nose anticline in the southern Sichuan. The advantages of the wide field electromagnetic method in detecting deep, low-resistivity thin layers are demonstrated. First, on the basis of the analysis of physical property data, a geological–geoelectric model is established in the test area, and the wide field electromagnetic method is numerically simulated to analyze and evaluate the response characteristics of deep thin shale gas layers on wide field electromagnetic curves. Second, a wide field electromagnetic test is conducted in the complex structure area of southern Sichuan. After data processing and inversion imaging, apparent resistivity logging data are used for calibration to develop an apparent resistivity interpretation model suitable for the test area. On the basis of the results, the characteristics of the electrical structure change in the shallow longitudinal formation of 6 km are implemented, and the transverse electrical distribution characteristics of the deep shale gas layer are delineated. In the prediction area near the well, the subsequent data verification shows that the apparent resistivity obtained using the inversion of the wide field electromagnetic method is consistent with the trend of apparent resistivity revealed by logging, which proves that this method can effectively identify the weak response characteristics of deep shale gas formations in complex structural areas. This experiment, it is shown shows that the wide field electromagnetic method with a large depth and high precision can effectively characterize the electrical characteristics of deep, low-resistivity thin layers in complex structural areas, and a new set of low-cost evaluation technologies for shale gas target layers based on the wide field electromagnetic method is explored.
Since April 2002, the Gravity Recovery and Climate Experiment Satellite (GRACE) has provided monthly total water storage anomalies (TWSAs) on a global scale. However, these TWSAs are discontinuous because some GRACE observation data are missing. This study presents a combined machine learning-based modeling algorithm without hydrological model data. The TWSA time-series data for 11 large regions worldwide were divided into training and test sets. Autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), and an ARIMA–LSTM combined model were used. The model predictions were compared with GRACE observations, and the model accuracy was evaluated using five metrics: the Nash–Sutcliffe efficiency coefficient (NSE), Pearson correlation coefficient (CC), root mean square error (RMSE), normalized RMSE (NRMSE), and mean absolute percentage error. The results show that at the basin scale, the mean CC, NSE, and NRMSE for the ARIMA–LSTM model were 0.93, 0.83, and 0.12, respectively. At the grid scale, this study compared the spatial distribution and cumulative distribution function curves of the metrics in the Amazon and Volga River basins. The ARIMA–LSTM model had mean CC and NSE values of 0.89 and 0.61 and 0.92 and 0.61 in the Amazon and Volga River basins, respectively, which are superior to those of the ARIMA model (0.86 and 0.48 and 0.88 and 0.46, respectively) and the LSTM model (0.80 and 0.41 and 0.89 and 0.31, respectively). In the ARIMA–LSTM model, the proportions of grid cells with NSE > 0.50 for the two basins were 63.3% and 80.8%, while they were 54.3% and 51.3% in the ARIMA model and 53.7% and 43.2% in the LSTM model. The ARIMA–LSTM model significantly improved the NSE values of the predictions while guaranteeing high CC values in the GRACE data reconstruction at both scales, which can aid in filling in discontinuous data in temporal gravity field models..
NLLoc is a nonlinear search positioning method. In this study, we use simulated arrival time data to quantitatively evaluate the NLLoc method from three aspects: arrival time picking accuracy, station distribution, and velocity model. The results show that the NLLoc method exhibits high positioning accuracy and stability in terms of arrival time picking accuracy and station distribution; however, it is sensitive to the velocity model. The positioning accuracy is higher when the velocity model is smaller than the true velocity. We combined absolute and relative positioning methods. First, we use the NLLoc method for absolute positioning of seismic data and then the double difference positioning method for relative positioning to obtain a more accurate relocation result. Furthermore, we used the combined method to locate the earthquake sequence after collecting dense seismic array data on the Luanzhou MS 4.3 earthquake that occurred on April 16, 2021, in Hebei Province. By fitting the fault plane with the relocated earthquake sequences, the results show that the strike and dip angles of the seismogenic fault of the Luanzhou MS 4.3 earthquake are 208.5° and 85.6°, respectively. This indicates a high-dip angle fault with North–North–East strike and North–West dip directions. Furthermore, we infer that the seismogenic fault of the Luanzhou MS 4.3 earthquake is the Lulong fault.
The selection of input variables and their amount has been an important issue in big data load forecasting. Taking heating load forecasting as an example, this paper proposed a method for data filtering based on information entropy. First, the heating data from an air source heat pump system adopted by a rural residence in northern China were employed. Moreover, the training data were classified based on linear or nonlinear variations of outdoor temperature and its changing ranges, while the validation data included three different types of weather conditions, namely, cold, cool, and mild. Then, the information entropy under 2-h, 4-h, 6-h and 8-h training window was quantified to be 1.811, 1.839, 1.877 and 1.856, respectively. For the employed rural residence, an equivalent three-resistance and two-capacity model was established to validate the effectiveness of the training window. Using the derived optimal thermal resistance and capacity, the various selection of outdoor temperature variation trend and range were compared and optimized. Results showed that 6 h of training data had the maximum information entropy and the most abundant information, the minimum errors between actual and forecasting data were observed under 6 h of training data, linear change, and lower outdoor temperature. The mean absolute percentage errors for the load forecasting of three typical days were 5.63%, 8.46%, and 12.10%, respectively.
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