资源环境科技发展态势分析平台

Gathering and analyzing data in the most unbiased and reproducible fashion is crucial for successful scientific research. However, scientists are influenced by prior experiences and tend to introduce biases in data interpretation when solutions are nonunique. In Earth science, a key task is the reconstruction of paleoenvironmental conditions, which relies on measurable changes in certain attributes of the resultant landscapes or the sedimentary record. The attributes in use commonly have multiple explanations, which may result in biased interpretation. To mitigate the problem, we propose a general conceptual framework for linking the observed attribute changes to their possible causes based on Shannon's information theory. This framework further serves as a hypothesis generator. We then present a rigorous procedure for paleoenvironmental interpretation based on the multiple-hypotheses method and Bayesian inference. Application examples illustrate the benefits of this procedure in paleoenvironmental reconstructions and in understanding environmental signal propagation through sediment routing systems.