煤田隐伏陷落柱空间形态地震属性解释方法

隐伏陷落柱是煤矿隐蔽致灾的主要地质因素，然而，常规三维反射地震中，单一顺层属性或层间属性难以可靠识别隐伏陷落柱，同时对其垂向展布及伴生断裂等三维空间特征刻画不清。为此，在基于OVT全方位处理数据体的基础上，计算相干、混沌等层间属性（奥灰顶界与目标煤层之间）与顺层两类属性，形成平面属性，采用波形聚类法对两类平面属性中的异常进行叠加处理，显著提高了隐伏陷落柱的平面位置解释精度；其次，引入断层概率体属性处理技术，优选构造导向滤波处理以及数据体融合技术，精细刻画了隐伏陷落柱垂向边界形态、陷落高度及伴生断裂特征。实例研究表明，采用波形聚类平面属性和断层概率体属性综合解释技术，可以有效提高隐伏陷落柱解释精度和效率，为隐伏陷落柱的识别提供了新的途径，具有广阔的应用前景。

Seismic attribute interpretation methods for spatial form of Hidden collapse columns in coalfields

Hidden collapse columns are the primary cause of potential disasters in coal mines. It is difficult to reliably identify hidden collapse columns only with single layer or inter-layer attributes of conventional three-dimensional seismic, Meanwhile, the three-dimensional features such as the vertical distribution and associated fractures of hidden collapse columns cannot be clearly depicted. To solve these problems, semblance, chaos and other inter-layer (between Ordovician Limestone and upper coal seams) attributes and layer attribute are extracted to forming plane attribute. The anomaly results are recognized and overlaid by Waveform clustering method. significant improving the interpretation accuracy of position in map of the hidden collapse. Furthermore, fault likehood attribute , the structure-oriented filtering processing and co-rendering technology are used to finely describe the shape, fall height and associated fractures of the hidden collapse column. Studies herein show that the comprehensive interpretation technology by using the wave clustering plane attribute and fault likehood attribute can effectively improve the interpretation accuracy and efficiency of the hidden collapse column, Thus it would provide a new way for the identification of the hidden collapse column, and also has broad application prospects.