Structure‐Coupled 3‐D Imaging of Magnetotelluric and Wide‐Angle Seismic Reflection/Refraction Data With Interfaces

Peng, Miao; Tan, Handong; Moorkamp, Max

doi:10.1029/2019jb018194

Cited by 10 publications

(4 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the high computational costs associated with global optimization algorithms limit their practical application in 3-D electrical resistivity inversion problems [11,14,15]. Deterministic methods such as the Gauss-Newton method [16], limited memory quasi-Newton method [17], and conjugate gradient method [18][19][20][21] are the popular approaches for the 3-D electrical resistivity inversion due to their promising performance in terms of result accuracy, stability, and convergence speed [8,11,22].…”

Section: Introductionmentioning

confidence: 99%

Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

Tao,

Han,

Yang

et al. 2024

Minerals

View full text Add to dashboard Cite

The electrical resistivity method is widely used in near-surface mineral exploration. At present, the deterministic algorithm is commonly employed in three-dimensional (3-D) electrical resistivity inversion to obtain subsurface electrical structures. However, the accuracy and efficiency of deterministic inversion rely on the initial model. In practice, obtaining an initial model that approximates the true subsurface electrical structures remains challenging. To address this issue, we introduce a broad learning (BL) network to determine the initial model and utilize the limited memory quasi-Newton (L-BFGS) algorithm to conduct the 3-D electrical resistivity inversion task. The powerful mapping capability of the BL network enables one to find the model that elucidates the actual observed data. The single-layer BL network makes it efficient and easy to realize, leading to much faster network training compared to that using the deep learning network. Both the synthetic and field experiments suggest that the BL framework could effectively obtain the initial model based on observed data. Furthermore, in comparison to using a homogeneous medium as the initial model, the L-BFGS inversion with the BL framework-designed initial model improves the inversion accuracy of subsurface electrical structures and expedites the convergence speed of the iteration. This study provides an effective approach for fast initial model design in a data-driven manner when the prior information is unavailable. The proposed method can be useful in high-precision imaging of near-surface mineral electrical structures.

show abstract

Section: Introductionmentioning

confidence: 99%

Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

Tao,

Han,

Yang

et al. 2024

Minerals

View full text Add to dashboard Cite

show abstract

“…Historically, MT has often been integrated with other electromagnetic methods or with seismic data (e.g. Gustafson et al, 2019;Peng et al, 2019), and with gravity to a lower extent (see review of the topic of Moorkamp, 2017). It is, however, seldom modelled jointly with magnetic data unless a third dataset is considered (e.g.…”

Section: Introductionmentioning

confidence: 99%

Utilisation of probabilistic magnetotelluric modelling to constrain magnetic data inversion: proof-of-concept and field application

Giraud

Seillé²,

Lindsay

et al. 2023

Solid Earth

View full text Add to dashboard Cite

Abstract. We propose, test and apply a methodology integrating 1D magnetotelluric (MT) and magnetic data inversion, with a focus on the characterisation of the cover–basement interface. It consists of a cooperative inversion workflow relying on standalone inversion codes. Probabilistic information about the presence of rock units is derived from MT and passed on to magnetic inversion through constraints combining structural constraints with petrophysical prior information. First, we perform the 1D probabilistic inversion of MT data for all sites and recover the respective probabilities of observing the cover–basement interface, which we interpolate to the rest of the study area. We then calculate the probabilities of observing the different rock units and partition the model into domains defined by combinations of rock units with non-zero probabilities. Third, we combine these domains with petrophysical information to apply spatially varying, disjoint interval bound constraints (DIBC) to least-squares magnetic data inversion using the alternating direction method of multipliers (or ADMM). We demonstrate the proof-of-concept using a realistic synthetic model reproducing features from the Mansfield area (Victoria, Australia) using a series of uncertainty indicators. We then apply the workflow to field data from the prospective mining region of Cloncurry (Queensland, Australia). Results indicate that our integration methodology efficiently leverages the complementarity between separate MT and magnetic data modelling approaches and can improve our capability to image the cover–basement interface. In the field application case, our findings also suggest that the proposed workflow may be useful to refine existing geological interpretations and to infer lateral variations within the basement.

show abstract

“…Scholl et al applied the cross-gradient constraint to the joint inversion of three-dimensional airborne electromagnetic data and gravity data [15]. Peng et al combined magnetotelluric and wide-angle seismic reflection/refraction inversion to detect crustal structures and Moho discontinuities [16].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Joint Inversion of the Resistivity Method and Time-Domain-Induced Polarization Based on the Cross-Gradient Constraints

et al. 2023

Self Cite

View full text Add to dashboard Cite

The resistivity method and time-domain-induced polarization (TDIP) are two branches of electric exploration that are used to solve problems in mineral exploration, hydrogeology and engineering geology. In recent years, integrating different physical parameters for joint inversion to improve the accuracy of inversion results has been extensively examined; however, three-dimensional joint inversion of the two methods above has not been realized. To further address this issue, in this research, we used the limited-memory BFGS (L-BFGS) method to develop a three-dimensional joint inversion algorithm of the resistivity method and TDIP based on the cross-gradient constraints. In the new algorithm, the resistivity method and TDIP inversion were iteratively updated alternately to ensure that the inversion results can simultaneously meet the two conditions of obtaining minimum data misfits and finding structural similarity. The three-dimensional synthetic dataset inversion results showed that the models obtained by joint inversion are more accurate in the recovery of both the boundaries and the values of the anomalies. Especially in the background of high noise, joint inversion has higher resolution for the target body. The joint inversion algorithm was also successfully applied to a groundwater detection practice in Beijing, China, in which the practicability of the algorithm was confirmed.

show abstract

Structure‐Coupled 3‐D Imaging of Magnetotelluric and Wide‐Angle Seismic Reflection/Refraction Data With Interfaces

Cited by 10 publications

References 67 publications

Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

Utilisation of probabilistic magnetotelluric modelling to constrain magnetic data inversion: proof-of-concept and field application

Three-Dimensional Joint Inversion of the Resistivity Method and Time-Domain-Induced Polarization Based on the Cross-Gradient Constraints

Contact Info

Product

Resources

About