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Timber structures are characterized by a pinching phenomenon that leads to reduced dissipative capability. A few hysteretic models have been proposed to simulate the mechanical behavior of timber structures, among which the one composed of a bilinear element and a slip element in parallel has been popular in practice. Based on this model, this paper expands on the existing seismic control design methodology to determine the capacity of hysteretic dampers for multi-story timber structures. The equivalent linearization method for a single-degree-of-freedom timber structure with added hysteretic damper is established and is verified through nonlinear timber history analysis over a wide range of structural parameters. The design formulas for determining the damper capacity for a multi-degree-of-freedom system are derived, based on the concept of adjusting the distribution of equivalent stiffness of structure. The seismic control design is applied to many buildings with randomly generated parameters and the effectiveness is confirmed through a nonlinear time history analysis with four sets of seismic excitations. An extended study has shown that the shear force pattern plays an important role in the seismic control design results and thus the performance of structures. The effectiveness of the control of residual deformations by adding dampers is also studied.

Seismic interpretation is one of key steps in the hydrocarbon predication. However, amplitude interpretation using post-stack data is ambiguous because P-wave, S-wave velocity and density can be given by different rock case. A high-porosity brine-filled rock can produce a response same as a mid-low-porosity and hydrocarbon-filled rock as shown in Figure 1.

With the development of spatial techniques in geographic information systems(GIS),new methods have allowed for robust and detailed preparation of digital models of the earth′s surface elevation,interpolation of climate parameters,and remote sensing of surface conditions in terrestrial environments.These methods in turn have led to greatly enhanced species distribution models(also called species niche models) by providing estimates of environmental conditions and predictions of potential and actual species distribution areas across entire landscapes.Species distribution models have become the subject of active field of research in large-scale ecology and biogeography,and have been used to solve many ecological issues in recent decades.Models are used for biodiversity assessment;biological reserve design;habitat management and restoration;population viability analysis;environmental risk assessment;invasive species management;community and ecosystem modeling;and predicting the effects of global environmental change on species and ecosystems.Species distribution models using species occurrence records(presence only or presence/absence data) associated with environmental variables seek to determine the fundamental niche or realized niche of a particular species,and then to project this niche onto the landscape of interest to reflect the potential distribution area of the species.Results could be interpreted as the probability of occurrence of the species,species habitat suitability or species relative richness.However,there is still insufficient knowledge of the theoretical basis of species distribution models,as some of the key ecological processes have not been incorporated into the framework of these models.This generates substantial confusion when the predicted results of species distribution models are explained.For more efficient use and further development of species distribution models,this study provides: 1) a full overview of the history and recent theoretical advances in the field of species distribution models;2) a systematic discussion of the relationship between species distribution model and species distribution area;3) a highlight of the critical limitations inherent in species distribution models;and 4) a focus on challenges of species distribution models for future research.Results from this study suggest that the theoretical basis of species distribution models is strongly related to niche theory,source-sink theory,population dynamics theory,metapopulation theory,and evolutionary theory.Proper understanding of the relationship between the predicted and actual species distribution area depends on separation of three factors(environmental condition,species interactions and species migration ability) affecting the distribution area of species.The main problems of current species distribution models are that they fail to efficiently integrate species interaction and species migration ability into the model building process,which creates gaps between the predicted and actual species distribution area under normal circumstances.Future development of species distribution models should focus on strengthening their inherent theoretical framework,and must integrate species interactions process,population dynamics process,migration process and evolutionary process into models.This study also suggests that simulating functional groups and community structure from higher theoretical levels is important for the development of species distribution models.We believe that through the efforts of scientists,future species distribution models can overcome the above-mentioned drawbacks and can dynamically model the multi-species potential distribution area,thus providing a more in-depth theoretical study of community ecology and biogeography.

At present, quantitative AVO plays a significant role in hydrocarbon prediction in many basins. Hydrocarbon prediction from seismic amplitude and AVO is a daunting task. Many AVO attributes are presented for this purpose. Based on the Mudrock equation, Smith and Gidlow (1987) defined an attribute named fluid factor as: ΔF=ΔVp/Vp-1.16ΔVs/Vs/γ (1) Where γ is the background Vp/Vs ratio, the constant 1.16 can be local value of Vp-Vs relation. Fatti et al. (1994) redefined the fluid factor as: ΔF=Rp-1.16Rs/γ (2) Where Rp is P-reflectivity and Rs is S-reflectivity. However, there exist several pitfalls in the AVO technique. One of the pitfalls is that a high porosity and good quality brine sand can give a rising AVO response. In this paper, we propose a new attribute called “J” for hydrocarbon prediction as follow: J=Jp sinα-Js cosα (3) Numerical and Marmousi II model are used to test the new method in this paper. In the numerical simulation, brine responses of J attribute are relatively stable with varying of porosity whereas hydrocarbon responses decrease under effect of porosity. In these two fluid factor cases, water response with high porosity can equal to hydrocarbon response with lower porosity which cause an ambiguity in interpretation. A part of Marmousi II model is used to compare performances of different attributes. The results show that all three attribute can detect hydrocarbon sands. However, Smith and Gidlow's and Fatti's fluid factor also show anomalies for water-bearing layer which can be misleading whereas J attribute is more sensitive to hydrocarbon. J attribute is less ambiguous in hydrocarbon detection. In summary, this study presents a new AVO attribute J and we compare it with Smith and Gidlow's and Fatti's fluid factor. This method is simple, fast, and an effective exploration tool. Through Marmousi II model study, we demonstrated that J attribute can detect hydrocarbons and has fewer anomalies from a non-pay zone. In this case, J attribute has better performance than both Smith and Gidlow's and Fatti's fluid factor in hydrocarbon prediction. It can predict presence of possible hydrocarbon sand effectively and reduce the ambiguity caused by lithology. Further, AVO attribute has to be interpreted as a guideline in exploration. Predicting hydrocarbon from amplitude has to be geologically/rock physics considered with structure and petroleum system playing a major role in reducing exploration risk. Introduction AVO interpretation for hydrocarbon prediction is a daunting task because pore-fill fluid and porosity affect amplitude simultaneously. A useful AVO attribute called “fluid factor” is presented for hydrocarbon prediction by Smith and Gidlow (1987) and Fatti et al. (1994) respectively. This study defines a new attribute “J” to detect hydrocarbons. It is supposed to be more sensitive to the existence of hydrocarbons and has less ambiguity caused by porosity. Numerical simulation and Marmousi II dataset are used to test the new attribute “J” and the results illustrate the effectiveness and less ambiguity of the J attribute. J attribute is a useful tool for hydrocarbon prediction and risk reduction in exploration. At present, AVO attribute analysis plays a significant role in hydrocarbon prediction in many basins where rocks are generally soft, unconsolidated and are sensitive to fluid replacement and response as per Gassmann (Ghosh et al., 2014). However, there exist several pitfalls in amplitude interpretation. One of the pitfalls is that good quality brine sands can give rising AVO responses in line with Gassmann fluid replacement algorithm as shown in Figure 1 (Ghosh et al., 2010). One of the widely-used AVO attributes for hydrocarbon prediction is fluid factor. Smith and Gidlow 1987 defined an attribute named fluid factor based on the Mudrock line (Castagna et al., 1985). The fluid factor is written as: ΔF ∆ 1.16 ∆ / (1) where, γ is the background Vp/Vs ratio and the constant 1.16 can be a local value of Mudrock line. Fatti et al., 1994 redefined the fluid factor as

AVO technique is already widely used in petroleum exploration and proved itself in many cases. There are a few AVO attributes presented based on Shuey’s approximation. This paper is attempted to present a new AVO attribute, J, for fluid detection which is different with existed ones. The J attribute is focused on Poisson reflectivity instead of the difference between V p and V s . We compared the performances of J and one of the existed fluid factors in models, well logs, and seismic. From the result, J attribute provides a new way to detect gas reservoir and has better performance than existed fluid factor in some cases.