河南薛湖煤矿在开采过程中受到了水害的影响,为了确保煤矿安全、高效生产,分析了矿井水文地质条件,研究了矿井冲水的主控因素,并对矿井涌水量进行预测计算。研究结果表明,薛湖煤矿矿区发育六大含水层（组）和三大隔水层（组）,煤系地层的二叠系砂岩裂隙含水层是危害矿井生产的主要含水层,随着生产的进行,顶板砂岩水多被疏干,对生产的安全不会造成很大的影响。二2主采煤层的直接充水水源为二叠系二2煤层顶板砂岩裂隙承压水,间接充水水源为二2煤层底板和奥陶系灰岩岩溶裂隙承压水,矿井的自身采空区积水是薛湖矿的充水水源之一。二2煤的导水途径主要有裂隙、断层和封闭不良钻孔3种,高角度正断层可能成为导水通道。越往深部开采水压将会越大,构造和裂隙的发育增加了底板水涌入矿井的危险。选取比拟法和稳定流解析法对采区矿井涌水量进行计算,比拟法计算的全矿井正常涌水量656 m 3/h、最大涌水量787 m 3/h比较符合近年来矿井充水的实际情况,可以作为下一步矿井开采的依据。但随着开采水平的不断延深,太灰岩溶水向矿井突水的概率也将大大提高,若出现短期内多点突水情况,将会超过比拟法预算的最大涌水量。 相似文献
The traditional emotion–cause extraction task needs to give the exact emotion annotation contained in the document before extracting the cause. Different from this, the emotion–cause pair extraction (ECPE) task, which aims to extract emotion–cause pairs with causal relationships directly from the document, is a task proposed in the natural language processing field recently. At present, the task of ECPE is divided into two steps: emotion annotations and cause clause extraction, emotion–cause clause pair combining and filtering. In this article, we optimize these two steps. On the one hand, in the first step of ECPE, a mutual assistance single-task model proposed by us is used to replace the original multi-task model. On the other hand, the position information of the clause is added as an additional feature in the second step of ECPE. Furthermore, based on different levels of semantic features, we design three filtering models and explore their performance on ECPE tasks. The experimental results on the benchmark corpus show that our approach can make the ECPE task achieve better performance. Compared with the referenced method, F1-score is increased by 5.3%. Moreover, these optimization strategies improve the subtasks contained in ECPE to varying degrees.
