ABSTRACTThe spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns. 相似文献
Mineral potential prediction is a process of establishing a statistical model that describes the relationship between evidence variables and mineral occurrences. In this study, evidence variables were constructed from geological, remote sensing, and geochemical data collected from the Lalingzaohuo district, Qinghai Province, China. Based on these evidence variables, a conjugate gradient logistic regression (CG-LR) model was established to predict exploration targets in the study area. The receiver operating characteristic (ROC) and prediction–area (P-A) curves were used to evaluate the effectiveness of the CG-LR model in mineral potential mapping. The difference between the vertical and horizontal coordinates of each point on the ROC curve was used to determine the optimal threshold for classifying the exploration targets. The optimal threshold corresponds to the point on the ROC curve where the difference between the vertical coordinate and the horizontal coordinate is the largest. In exploration target prediction in the study area, the CG algorithm was used to optimize iteratively the LR coefficients, and the prediction effectiveness was tested for different epochs. With increasing iterations, the prediction performance of the model becomes increasingly better. After 60 iterations, the LR model becomes stable and has the best performance in exploration target prediction. At this point, the exploration targets predicted by the CG-LR model occupy 14.39% of the study area and contain 93% of the known mineral deposits. The exploration targets predicted by the model are consistent with the metallogenic geological characteristics of the study area. Therefore, the CG-LR model can effectively integrate geological, remote sensing, and geochemical data for the study area to predict targets for mineral exploration.
Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.
Natural Resources Research - Some industrial activities, such as underground mining, hydraulic fracturing (HF), can cause microearthquakes and even damaging earthquakes. In recent years, with the... 相似文献
Soil CO_2 efflux, the second largest flux in a forest carbon budget, plays an important role in global carbon cycling. Forest logging is expected to have large effects on soil CO_2 efflux and carbon sequestration in forest ecosystems. However, a comprehensive understanding of soil CO_2 efflux dynamics in response to forest logging remains elusive due to large variability in results obtained across individual studies. Here, we used a meta-analysis approach to synthesize the results of 77 individual field studies to determine the impacts of forest logging on soil CO_2 efflux. Our results reveal that forest logging significantly stimulated soil CO_2 efflux of the growing season by 5.02%. However, averaged across all studies, nonsignificant effect was detected following forest logging. The large variation among forest logging impacts was best explained by forest type, logging type, and time since logging. Soil CO_2 efflux in coniferous forests exhibited a significant increase(4.38%) due to forest logging, while mixed and hardwood forests showed no significant change. Logging type also had a significant effect on soil CO_2 efflux, with thinning increasing soil CO_2 efflux by 12.05%, while clear-cutting decreasing soil CO_2 efflux by 8.63%. The time since logging also had variable effects, with higher soil CO_2 efflux for 2 years after logging, and lower for 3-6 years after logging; when exceeded 6 years, soil CO_2 efflux increased. As significantly negative impacts of forest logging were detected on fine root biomass, the general positive effects on soil CO_2 efflux can be explained by the accelerated decomposition of organic matter as a result of elevated soil temperature and organic substrate quality. Our results demonstrate that forest logging had potentially negative effects on carbon sequestration in forest ecosystems. 相似文献
The Micropile-Mechanically Stabilized Earth (MSE) wall, specially designed for mountain roads, is proposed to improve the MSE wall local stability, global stability and impact resistance of roadside barriers. Model tests and the corresponding numerical modeling were conducted to validate the serviceability of the Micropile-MSE wall and the reliability of the numerical method. Then, a parametric study of the stress and deformation of Micropile-MSE wall based on the backfill strength and interfacial friction angle between backfill and backslope is conducted to evaluate its performance. The test results indicate that the surcharge-induced horizontal earth pressure, base pressure and lateral displacement of the wall panel of Micropile-MSE wall decrease. The corresponding numerical results are nearly equal to the measured values. The basic failure mode of MSE wall in steep terrain is the sliding of backfill along the backslope, while A-frame style micropiles are capable of preventing the sliding trend. The maximum resultant displacement can be decreased by 6.25% to 46.9% based on different interfacial friction angles, and the displacement can be reduced by 6% ~ 56.1% based on different backfill strengths. Furthermore, the reduction increases when the interfacial friction angle and internal friction angle of backfill decrease. In addition, the lateral displacement of wall panel, the deformation of backfill decrease and the tension strain of geogrid obviously, which guarantees the MSE wall functions and provides good conditions for mountain roads. 相似文献
In this study, we propose a new method to determine full moment tensor solution for induced seismicity. This method generalizes the full waveform matching algorithm we have developed to determine the double-couple (DC) focal mechanism based on the neighbourhood algorithm. One major difference between the new method and the former one is that we adopt a new misfit function to constrain the candidate moment tensor solutions with respect to a reference DC solution in addition to other misfit terms characterizing the waveform matching. Through synthetic tests using a real passive seismic survey geometry, the results show the new constraint can help better recover the DC components of inverted moment tensors. We further investigate how errors in the velocity model and source location affect the moment tensor solution. The synthetic test results indicate that the constrained inversion is robust in recovering both the DC and non-DC components. We also test the proposed method on several real induced events in an oil/gas field in Oman using the same observation system as synthetic tests. While it is found that the full moment tensor solutions without using the DC constraints have much larger non-DC components than solutions with the DC constraints, both solutions are able to fit the observed waveforms at similar levels. The synthetic and real test results suggest the proposed DC constrained inversion method can reliably retrieve full moment tensor solutions for the induced seismicity. 相似文献
Doklady Earth Sciences - In October 2019, extremely impressive, fresh ruptures of the surface on the mud volcano of Mount Karabetova were discovered. The ruptures are represented by all the main... 相似文献
Improving the seismic time–frequency resolution is a crucial step for identifying thin reservoirs. In this paper, we propose a new high-precision time–frequency analysis algorithm, synchroextracting generalized S-transform, which exhibits superior performance at characterizing reservoirs and detecting hydrocarbons. This method first calculates time–frequency spectra using generalized S-transform; then, it squeezes all but the most smeared time–frequency coefficients into the instantaneous frequency trajectory and finally obtains highly accurate and energy-concentrated time–frequency spectra. We precisely deduce the mathematical formula of the synchroextracting generalized S-transform. Synthetic signal examples testify that this method can correctly decompose a signal and provide a better time–frequency representation. The results of a synthetic seismic signal and real seismic data demonstrate that this method can identify some reservoirs with thincknesses smaller than a quarter wavelength and can be successfully applied for hydrocarbon detection. In addition, examples of synthetic signals with different levels of Gaussian white noise show that this method can achieve better results under noisy conditions. Hence, the synchroextracting generalized S-transform has great application prospects and merits in seismic signal processing and interpretation. 相似文献
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