煤层气储层含气量与其弹性参数之间的关系——思考与初探

首先研究了天然气AVO技术的岩石物理基础--Gassmann方程和Biot理论(以下统称为"Gassmann-Biot理论")--对煤层气AVO技术的适用性.根据该理论创始人在建立其理论时的假设条件、煤层气储层的双相孔隙特征、煤层气的双相赋存特征,本文推理认为,由于煤层气储层的基质型孔隙体系的连通性差,裂隙性孔隙在宏观上是非均质的,并且是各向异性的,因此,总体上来说,Gassmann-Biot理论不完全适用于煤层气储层.特别地,当煤层的水饱和状态保持不变而煤层的含气量变化时,因为煤层中孔(裂)隙中流体的体积压缩模量不变或基本不变,即使煤层的弹性参数发生变化,Gassmann-Biot理论也不可能预测其变化趋势.因此,天然气AVO技术的岩石物理基础完全地不适用于水饱和煤层气储层.研究发现,煤层气储层含气量与其密度、纵波速度、横波速度之间存在负相关关系,即含气量高,密度小、纵波速度小、横波速度小;含气量低,密度大、纵波速度大、横波速度大.但是,这些负相关关系不是现有岩石物理理论能够预测或能够解释的.依据煤层气地质学理论以及负相关关系与现有岩石物理理论的一致性,本文提供了对某勘探区A号煤层含气量与其密度、纵波速度、横波速度之间负相关关系的解释,并认为煤层气储层的含气量与其弹性参数之间的负相关关系可能是这类储层内在的固有的规律性的关系.本文证明含气量与其密度、纵波速度、横波速度之间负相关关系可以作为煤层气AVO技术的岩石物理基础,建立了AVO异常与煤层气储层"甜点(即富集高渗部位)"之间的关系,从而能够使用AVO异常探测煤层气富集高渗部位.     

Factors affecting grassland succession retardation in the Juifang area

This study classifies the vegetation status to determine the factors affecting grassland succession retardation in the Juifang area. Data derived from Land Use Investigation of Taiwan, GIS techniques, multivariate analysis, and on-site investigation was applied in this research. Discriminant analysis shows that forests that have low human disturbance cannot be correctly identified and classified by the topography and soil depth. This indicates that the topography and soil depth in the Juifang area are not the limiting factors of forest growth. Grasslands are usually susceptible to periodic wildfires. This can be used as an environmental index for disturbed and/or polluted soils. Comparing the current vegetation status with the vegetation distribution derived from discriminant analysis, potential wildfire frequency can be estimated. Acid sulfate soils and copper-contaminated soils are the major polluted soils in the Juifang area. Because the Juifang area is an important coal field in northern Taiwan, acid sulfate can be easily found in the spoiled soils of several mining spots due to sulfur oxidation from the soil and/or rock layers dug out during mining operations. Copper-contaminated soils were detected at the sites near the Li-Lo Copper Refining Plant and Taiwan Metal Mining Company. Polluted soils and periodic wildfires inhibiting the invasion of trees are the major factors retarding the succession of grasslands in the Juifang area.     

Symplectic partitioned Runge-Kutta method based on the eighth-order nearly analytic discrete operator and its wavefield simulations

We propose a symplectic partitioned Runge-Kutta （SPRK） method with eighth-order spatial accuracy based on the extended Hamiltonian system of the acoustic waveequation. Known as the eighth-order NSPRK method, this technique uses an eighth-orderaccurate nearly analytic discrete （NAD） operator to discretize high-order spatial differentialoperators and employs a second-order SPRK method to discretize temporal derivatives.The stability criteria and numerical dispersion relations of the eighth-order NSPRK methodare given by a semi-analytical method and are tested by numerical experiments. We alsoshow the differences of the numerical dispersions between the eighth-order NSPRK methodand conventional numerical methods such as the fourth-order NSPRK method, the eighth-order Lax-Wendroff correction （LWC） method and the eighth-order staggered-grid （SG）method. The result shows that the ability of the eighth-order NSPRK method to suppress thenumerical dispersion is obviously superior to that of the conventional numerical methods. Inthe same computational environment, to eliminate visible numerical dispersions, the eighth-order NSPRK is approximately 2.5 times faster than the fourth-order NSPRK and 3.4 timesfaster than the fourth-order SPRK, and the memory requirement is only approximately47.17% of the fourth-order NSPRK method and 49.41% of the fourth-order SPRK method,which indicates the highest computational efficiency. Modeling examples for the two-layermodels such as the heterogeneous and Marmousi models show that the wavefields generatedby the eighth-order NSPRK method are very clear with no visible numerical dispersion.These numerical experiments illustrate that the eighth-order NSPRK method can effectivelysuppress numerical dispersion when coarse grids are adopted. Therefore, this methodcan greatly decrease computer memory requirement and accelerate the forward modelingproductivity. In general, the eighth-order NSPRK method has tremendous potential value forseismic exploration and seismology research.     

Assessment of potential sediment disasters and resilience management of mountain roads using environmental indicators

In recent years, extreme rainfall events occur frequently, causing serious watershed sediment disasters, destroying mountain roads, and endangering the safety of residents' lives and property. This study aims to deal with the spatial change of potential sediment movement on the road slope pre-disaster and to screen disaster hot spots for early warning and control system. The conceptual model is used to simulate the distribution of primary and/or derived disasters on a watershed scale to assess the impact of sediment disasters caused by heavy rain event. Correlation analysis shows that the models in assessment of primary disaster and derived disaster are significantly correlated with the collapse ratio and disaster ratio, respectively. Since the primary disaster has been considered when calculating the derived disaster risk, the terrain subdivision along Provincial Highway 21 (Tai-21) is extracted to understand the derived sediment disaster on the road slope. The model can effectively evaluate the road sections prone to disasters. According to the risk level, the hot spot of road slope disasters and the management of disaster resilience are determined and can be the reference for disaster prevention and control.

     

Earthquake-induced landslide hazard and vegetation recovery assessment using remotely sensed data and a neural network-based classifier: a case study in central Taiwan

A catastrophic earthquake with a Richter magnitude of 7.3 occurred in the Chi-Chi area of Nantou County on 21 September 1999. Large-scale landslides were generated in the Chiufenershan area of Nantou County in central Taiwan. This study used a neural network-based classifier and the proposed NDVI-based quantitative index coupled with multitemporal SPOT images and digital elevation models (DEMs) for the assessment of long-term landscape changes and vegetation recovery conditions at the sites of these landslides. The analyzed results indicate that high accuracy of landslide mapping can be extracted using a neural network-based classifier, and the areas affected by these landslides have gradually been restored from 211.52 ha on 27 September 1999 to 113.71 ha on 11 March 2006, a reduction of 46.24%, after six and a half years of assessment. In accordance with topographic analysis at the sites of the landslides, the collapsed and deposited areas of the landslide were 100.54 and 110.98 ha, with corresponding debris volumes of 31,983,800 and 39,339,500 m³. Under natural vegetation succession, average vegetation recovery rate at the sites of the landslides reached 36.68% on 11 March 2006. The vegetation recovery conditions at the collapsed area (29.17%) are shown to be worse than at the deposited area (57.13%) due to topsoil removal and the steep slope, which can be verified based on the field survey. From 1999 to 2006, even though the landslide areas frequently suffered from the interference of typhoon strikes, the vegetation succession process at the sites of the landslides was still ongoing, which indicates that nature, itself, has the capability for strong vegetation recovery for the denudation sites. The analyzed results provide very useful information for decision-making and policy-planning in the landslide area.     

The Predictability of Ocean Environments that Contributed to the 2020/21 Extreme Cold Events in China:2020/21 La Niña and 2020 Arctic Sea Ice Loss

Several consecutive extreme cold events impacted China during the first half of winter 2020/21,breaking the low-temperature records in many cities.How to make accurate climate predictions of extreme cold events is still an urgent issue.The synergistic effect of the warm Arctic and cold tropical Pacific has been demonstrated to intensify the intrusions of cold air from polar regions into middle-high latitudes,further influencing the cold conditions in China.However,climate models failed to predict these two ocean environments at expected lead times.Most seasonal climate forecasts only predicted the 2020/21 La Ni?a after the signal had already become apparent and significantly underestimated the observed Arctic sea ice loss in autumn 2020 with a 1-2 month advancement.In this work,the corresponding physical factors that may help improve the accuracy of seasonal climate predictions are further explored.For the 2020/21 La Ni?a prediction,through sensitivity experiments involving different atmospheric-oceanic initial conditions,the predominant southeasterly wind anomalies over the equatorial Pacific in spring of 2020 are diagnosed to play an irreplaceable role in triggering this cold event.A reasonable inclusion of atmospheric surface winds into the initialization will help the model predict La Ni?a development from the early spring of 2020.For predicting the Arctic sea ice loss in autumn 2020,an anomalously cyclonic circulation from the central Arctic Ocean predicted by the model,which swept abnormally hot air over Siberia into the Arctic Ocean,is recognized as an important contributor to successfully predicting the minimum Arctic sea ice extent.     

Environmental indicators combined with risk analysis to evaluate potential wildfire incidence on the Dadu Plateau in Taiwan

Natural Hazards - Wildfire is a common disaster in the world, and it has a considerable impact on the safety of residents and ecological disturbance. Periodic wildfires are an urgent problem to be...     

Vegetation recovery and landscape change assessment at Chiufenershan landslide area caused by Chichi earthquake in central Taiwan

This study discusses vegetation recovery and land cover change with reference to the Chiufenershan landslide, a major disaster caused by the Chichi earthquake, 21 September 1999. Image classification technology, landscape indicators from multi-temporal remotely sensed data and a field survey provide the data. Image differencing methods and threshold values coupled with pre- and post-quake satellite images were used. Multi-temporal images in combination with various vegetation indices were drawn on to classify land cover patterns and discuss differences and suitability of indices. Landscape indicators and field investigations fed into an investigation of vegetation recovery and landscape change. The study results show that the best image classification system is original wavebands coupled with a cropping management factor index (CMFI). The land cover analysis shows that areas of forest and grass are increasing and areas of landslide are decreasing. From the field investigation, because the left and right sides of the landslide area were not disturbed by the earthquake, their calculated similarity index is the highest (30.08%). Miscanthus floridulus is the most dominant pioneer plant at the landslide collapse area with an importance value index (IVI) of 63.6%.