CMG Collaborative Research: Model Integration and Joint Inversion for Large-Scale Multi-Modal Geophysical Data

地球物理数据分析与反演是一个涉及建模、数据处理、反演和可视化的高度量化的领域。在大多数情况下，进行地球物理实验是为了收集对地球特定物理属性敏感的数据。对数据进行处理和反转，以生成或测试所讨论的物理属性的地球模型。为了更好地了解地球的结构，使用不同的成像模式进行了不同的实验。通常，每次实验的数据都会被分别倒置，以生成一组地球模型。共享所有物理属性的地球模型通常被称为公共地球模型。公共地球模型在科学和商业应用中非常重要，因为集成所有物理信息可以让地球科学家更好地了解重要的地质和地球物理过程。由于可以理解，利用不同的模态可以改善反演结果，因此许多算法依赖于不同物理模型之间的经验本构关系。然而，这种关系通常依赖于地点，不准确，而且很难获得。这阻碍了通用地球模型的使用和人们对它们的理解。这是一个跨学科的研究项目，目的是为多模式地球物理数据的联合反演建立一个更系统的框架。我们追求两种不同且互补的方法：一种基于统计学，另一种基于几何学。虽然我们的方法适用于广泛的反演模式，但我们专注于地震和电磁数据的联合反演。我们开发的方法将在这里针对的地震-电磁联合反演问题之外具有更广泛的适用性，更广泛地超出医学成像等领域的地球科学。

Geophysical data analysis and inversion is a highly quantitative field that involves modeling, data processing, inversion, and visualization. In most cases, a geophysical experiment is conducted to collect data that are sensitive to a particular physical property of the earth. The data are processed and inverted to generate or test an earth model of the physical property in question. To better understand the earth's structure, different experiments are conducted using different imaging modalities. Usually the data of each experiment are inverted separately to generate a collection of earth models. An earth model that shares all physical attributes is usually called a common earth model.Common earth models are very important in scientific and commercial applications because integrating all physical information allows earth scientists to better understand important geological and geophysical processes. Since it is understood that utilizing different modalities may improve inversion results, many algorithms rely on empirical constitutive relationships between the different physical models. However, such relations are typically site-dependent, inexact, and hard to obtain. This hinders the use of common earth models and the understanding that could be obtained from them.This is an interdisciplinary research project to create a more systematic framework for joint inversion of multi-modal geophysical data. We pursue two different and complementary approaches: one based on statistics, and the other on geometry. While our methods have applicability across a wide spectrum of inversion modalities, we focus on the joint inversion of seismic and electromagnetic data. The methods we develop will have wider applicability beyond the joint seismic-electromagnetic inversion problems targeted here, and more generally beyond the geosciences in areas such as medical imaging.