When modelling a stratified orebody, accurately representing the dip and dip direction is important for accurate resource estimation. In the banded iron formation-hosted iron ore deposits in the Pilbara region of Western Australia, these quantities can be determined using marker shales from nearby holes. These marker shales are identified using natural gamma logs and are generally manually processed. Therefore, an automated method for matching natural gamma logs between holes is desirable. Dynamic time warping (DTW) can match two signals where there is stretching or distortion. This study presents a modified, iterative version of DTW for matching downhole natural gamma logs. This new method accounts for large differences in length of the two signals by comparing different segments of the signals. Several metrics were then used to rank potential matches between signals. The proposed iterative DTW method had an accuracy of 90%, compared with 67% for the standard DTW. Once matched, signals can be used to estimate the bedding angle at each hole. A point in one hole was matched to as many nearby holes as possible, creating a set of points located on the same surface. A localized plane was then fitted to these points. These bedding angles were used to reconstruct a surface representing the bedding. While the signal matching was accurate, the sparsity of correctly matched holes limits the accuracy of the calculated surface. Even with sparse gradient fields, a reasonable approximation of the bedding could be achieved.

在对分层矿体进行建模时，准确表示倾角和倾角方向对于准确估算资源很重要。在西澳大利亚州皮尔巴拉地区的带状铁矿床铁矿床中，可使用附近孔眼中的标记页岩确定这些量。这些标记页岩是使用天然伽马测井仪识别的，通常是手动处理的。因此，需要一种用于在孔之间匹配自然伽玛测井曲线的自动化方法。动态时间规整（DTW）可以匹配存在拉伸或失真的两个信号。这项研究提出了DTW的修改版，迭代版，用于匹配井下天然伽马测井仪。通过比较信号的不同段，此新方法解决了两个信号的长度差异很大的问题。然后使用几个指标对信号之间的潜在匹配进行排名。所提出的迭代DTW方法的准确性为90％，而标准DTW的准确性为67％。匹配后，可以使用信号来估计每个孔的层理角度。一个孔中的一个点与尽可能多的邻近孔匹配，从而在同一曲面上创建了一组点。然后将局部平面拟合到这些点。这些层理角度用于重建代表层理的表面。尽管信号匹配是准确的，但正确匹配的孔的稀疏性限制了计算表面的准确性。即使在稀疏的梯度场中，也可以实现层理的合理近似。匹配后，可以使用信号来估计每个孔的层理角度。一个孔中的一个点与尽可能多的附近孔匹配，从而在同一曲面上创建了一组点。然后将局部平面拟合到这些点。这些层理角度用于重建代表层理的表面。尽管信号匹配是准确的，但正确匹配的孔的稀疏性限制了计算表面的准确性。即使在稀疏的梯度场中，也可以实现层理的合理近似。匹配后，可以使用信号来估计每个孔的层理角度。一个孔中的一个点与尽可能多的邻近孔匹配，从而在同一曲面上创建了一组点。然后将局部平面拟合到这些点。这些层理角度用于重建代表层理的表面。尽管信号匹配是准确的，但正确匹配的孔的稀疏性限制了计算表面的准确性。即使在稀疏的梯度场中，也可以实现层理的合理近似。这些层理角度用于重建代表层理的表面。尽管信号匹配是准确的，但正确匹配的孔的稀疏性限制了计算表面的准确性。即使在稀疏的梯度场中，也可以实现层理的合理近似。这些层理角度用于重建代表层理的表面。尽管信号匹配是准确的，但正确匹配的孔的稀疏性限制了计算表面的准确性。即使在稀疏的梯度场中，也可以实现层理的合理近似。