The Granada Basin (Spain) is a Neogene sedimentary depression with irregular geomorphology and deep depocenters. It is located in the most seismically hazardous part of the Iberian Peninsula with an historically experienced extremely destructive earthquakes, followed by periods of low to moderate seismicity. In 1980s the Chevron Oil Company collected a set of 30 deep seismic reflection sections in this Basin of which only the results on paper are kept. Due to the fact that many of these seismic profiles are currently located in urban areas and the economic cost of carrying out a similar exploration, it was decided to recover these old data and apply a post-stack treatment to improve their quality. The purpose of this study is to show the applied reprocessing flow and, with the new sections, to present a spatial model of the basin. The first stage of recovery and enhacement of seismic sections has consisted in three phases: first, high-resolution scanning of paper copies to TIFF images followed by the transformation of TIFF images to SEG-Y format; second, poststack processing workflow to increasing resolution and lateral coherence of these seismic lines; and third, it has been used a machine learning algorithm, among others, increasing the spatial resolution, signal-to-noise ratio, and coherence of the seismic signals. In addition, basement horizons, as well as three sedimentary sequences, were identified in all seismic sections and interpolated to create a three-dimensional basement model composed by normal faults, horst and grabens related to the seismotectonic behavior of the basin. As an overall assessment, this work is an example of the usefulness of ‘recycling’ legacy seismic data, which nowadays are usually in archived boxes, but at the time required a great economic and acquisition effort.

中文翻译：

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使用西班牙格拉纳达盆地的机器学习对遗留地震数据进行再处理和解释


格拉纳达盆地（西班牙）是一个新近纪沉积洼地，地貌不规则，沉积中心较深。它位于伊比利亚半岛地震最危险的地区，历史上曾经历过极具破坏性的地震，随后又经历过低至中度地震活动。 20世纪80年代，雪佛龙石油公司在该盆地采集了一组30个深层地震反射剖面，其中仅保留了纸质结果。由于许多地震剖面目前位于城市地区，并且进行类似勘探的经济成本很高，因此决定恢复这些旧数据并进行叠后处理以提高其质量。本研究的目的是展示所应用的后处理流程，并通过新剖面来呈现盆地的空间模型。地震剖面恢复和增强的第一阶段包括三个阶段：首先，将纸质副本高分辨率扫描为 TIFF 图像，然后将 TIFF 图像转换为 SEG-Y 格式；其次，叠后处理工作流程，以提高这些地震线的分辨率和横向相干性；第三，它使用了机器学习算法等，提高了地震信号的空间分辨率、信噪比和相干性。此外，在所有地震剖面中识别出基底层位以及三个沉积层序，并进行插值以创建由与盆地地震构造行为相关的正断层、地垒和地堑组成的三维基底模型。 作为总体评估，这项工作是“回收”遗留地震数据的有用性的一个例子，这些数据现在通常存放在存档的盒子里，但当时需要巨大的经济和采集工作。