Changchun Yin

3D forward modeling and response analysis for marine CSEMs towed by two ships

A dual-ship-towed marine electromagnetic (EM) system is a new marine exploration technology recently being developed in China. Compared with traditional marine EM systems, the new system tows the transmitters and receivers using two ships, rendering it unnecessary to position EM receivers at the seafloor in advance. This makes the system more flexible, allowing for different configurations (e.g., in-line, broadside, and azimuthal and concentric scanning) that can produce more detailed underwater structural information. We develop a three-dimensional goal-oriented adaptive forward modeling method for the new marine EM system and analyze the responses for four survey configurations. Oceanbottom topography has a strong effect on the marine EM responses; thus, we develop a forward modeling algorithm based on the finite-element method and unstructured grids. To satisfy the requirements for modeling the moving transmitters of a dual-ship-towed EM system, we use a single mesh for each of the transmitter locations. This mitigates the mesh complexity by refining the grids near the transmitters and minimizes the computational cost. To generate a rational mesh while maintaining the accuracy for single transmitter, we develop a goal-oriented adaptive method with separate mesh refinements for areas around the transmitting source and those far away. To test the modeling algorithm and accuracy, we compare the EM responses calculated by the proposed algorithm and semi-analytical results and from published sources. Furthermore, by analyzing the EM responses for four survey configurations, we are confirm that compared with traditional marine EM systems with only in-line array, a dual-ship-towed marine system can collect more data.

Transdimensional Bayesian inversion of time-domain airborne EM data

To reduce the dependence of EM inversion on the choice of initial model and to obtain the global minimum, we apply transdimensional Bayesian inversion to time-domain airborne electromagnetic data. The transdimensional Bayesian inversion uses the Monte Carlo method to search the model space and yields models that simultaneously satisfy the acceptance probability and data fitting requirements. Finally, we obtain the probability distribution and uncertainty of the model parameters as well as the maximum probability. Because it is difficult to know the height of the transmitting source during flight, we consider a fixed and a variable flight height. Furthermore, we introduce weights into the prior probability density function of the resistivity and adjust the constraint strength in the inversion model by changing the weighing coefficients. This effectively solves the problem of unsatisfactory inversion results in the middle high-resistivity layer. We validate the proposed method by inverting synthetic data with 3% Gaussian noise and field survey data.

Gravity compression forward modeling and multiscale inversion based on wavelet transform

The main problems in three-dimensional gravity inversion are the non-uniqueness of the solutions and the high computational cost of large data sets. To minimize the high computational cost, we propose a new sorting method to reduce fluctuations and the high frequency of the sensitivity matrix prior to applying the wavelet transform. Consequently, the sparsity and compression ratio of the sensitivity matrix are improved as well as the accuracy of the forward modeling. Furthermore, memory storage requirements are reduced and the forward modeling is accelerated compared with uncompressed forward modeling. The forward modeling results suggest that the compression ratio of the sensitivity matrix can be more than 300. Furthermore, multiscale inversion based on the wavelet transform is applied to gravity inversion. By decomposing the gravity inversion into subproblems of different scales, the non-uniqueness and stability of the gravity inversion are improved as multiscale data are considered. Finally, we applied conventional focusing inversion and multiscale inversion on simulated and measured data to demonstrate the effectiveness of the proposed gravity inversion method.

3D regularized focusing inversion of gravity data with a new stabilizing functional

Regularized focusing inversion has been proposed to generate clearer and more focused images of geological structures with sharp boundaries. It is based on special stabilizing functionals called minimum support (MS) functional and minimum gradient support (MGS) functional. In this paper, we have developed a new stabilizing functional that minimizes the area where strong model parameter variations and discontinuity occur. Compared with the MS stabilizer, the new stabilizer presented in this paper greatly reduces computing cost by reducing the number of iterations. Finally, we apply the new stabilizer to synthetic 3D gravity data to prove the effectiveness and efficiency of the method.