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Towards a Fast, Practical Alternative to Joint Inversion of Multiple Datasets: Model Fusion

机译:迈向多数据集联合反演的快速实用替代方案:模型融合

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摘要

In many areas of science and engineering, we have different sources of data. For example, in geophysics, there are many sources of data for Earth models: first-arrival passive seismic data (from the actual earthquakes), first-arrival controlled-source seismic data (from the seismic experiments), gravity data, etc.Datasets coming from different sources can provide complimentary information. In general, some of the datasets provide better accuracy and/or spatial resolution in some spatial areas and in some depths, while other datasets provide a better accuracy and/or spatial resolution in other areas or depths. For example: each gravity data points describes the result of measuring the gravity field at some spatial location; this field is generated by the joint effects of many locations; as a result, gravity generally measures the average density over a reasonably large spatial region. Thus, estimates based on gravity measurements have (relatively) low spatial resolution. In contrast, seismic waves generally travel a narrow trajectory from a seismic source (earthquake or explosion) to a recording senor. Thus, the spatial resolution corresponding to this data is much higher than gravity.At present, each of these datasets is often processed separately, resulting in several different models reflecting different aspects of the studied phenomena. It is therefore desirable to combine data from different datasets.An ideal approach would be to use all the datasets to produce a single model. However, in many research areas -- including geophysics -- there are no efficient algorithms for simultaneously processing all the different datasets. While such joint inversion methods are being developed, as a first step, we propose a practical solution: to fuse the models coming from different datasets.
机译:在科学与工程的许多领域,我们都有不同的数据源。例如,在地球物理学中,有许多地球模型的数据源:先到达被动地震数据(来自实际地震),先到达受控源地震数据(来自地震实验),重力数据等。来自不同来源的信息可以提供免费信息。通常,一些数据集在某些空间区域和某些深度提供更好的精度和/或空间分辨率,而其他数据集在其他区域或深度提供更好的精度和/或空间分辨率。例如:每个重力数据点描述在某个空间位置测量重力场的结果;这个场是由许多地方的共同作用产生的;结果,重力通常测量相当大的空间区域内的平均密度。因此,基于重力测量的估计具有(相对)较低的空间分辨率。相反,地震波通常从地震源(地震或爆炸)到记录传感器传播的轨迹很窄。因此,与该数据相对应的空间分辨率远比重力高。因此,需要组合来自不同数据集的数据。一种理想的方法是使用所有数据集来生成单个模型。但是,在许多研究领域(包括地球物理学)中,没有有效的算法可以同时处理所有不同的数据集。在开发这种联合反演方法的同时,第一步,我们提出一个实际的解决方案:融合来自不同数据集的模型。

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