Dealing with the ill-posed and non-unique nature of the non-linear geophysical inverse problem via local optimizers requires the use of some regularization methods, constraints, and prior information about the Earth's complex interior. Another difficulty is that the success of local search algorithms depends on a well-designed initial model located close to the parameter set providing the global minimum. On the other hand, global optimization and metaheuristic algorithms that have the ability to scan almost the entire model space do not need an assertive initial model. Thus, these approaches are increasingly incorporated into parameter estimation studies and are also gaining more popularity in the geophysical community. In this study we present the Barnacles Mating Optimizer (BMO), a recently proposed global optimizer motivated by the special mating behavior of barnacles, to interpret magnetic anomalies. This is the first example in the literature of BMO application to a geophysical inverse problem. After performing modal analyses and parameter tuning processes, BMO has been tested on simulated magnetic anomalies generated from hypothetical models and subsequently applied to three real anomalies that are chromite deposit, uranium deposit and Mesozoic dike. A second moving average (SMA) scheme to eliminate regional anomalies from observed anomalies has been examined and certified. Post-inversion uncertainty assessment analyses have been also implemented to understand the reliability of the solutions achieved. Moreover, BMO’s solutions for convergence rate, stability, robustness and accuracy have been compared with the solutions of the commonly used standard Particle Swarm Optimization (sPSO) algorithm. The results have shown that the BMO algorithm can scan the model parameter space more extensively without affecting its ability to consistently approach the unique global minimum in this presented inverse problem. We, therefore, recommend the use of competitive BMO in model parameter estimation studies performed with other geophysical methods.

Summary A gravity inversion procedure using the success–history–based adaptive differential evolution (SHADE) algorithm is presented to reconstruct the 3D basement relief geometry in sedimentary basins. We introduced exponential population size (number) reduction (EPSR) to reduce the computational cost and used self–adaptive control parameters to solve this highly nonlinear inverse problem. Model parameterization was carried out by discretizing the sedimentary cover via juxtaposed right prisms, each placed below each observation point. Resolvability characteristics of the 3D inverse problem were revealed through some cost function topography landscapes. The fine–tuned control parameter namely, population number allowed us to get best benefit from the algorithm. Additionally, a stabilizing function as a relative constraint was used to avoid undesired effects originated from the ill–posedness of the problem. In the synthetic data cases, the strategy we propose outperformed the linear population number reduction (LPSR) strategy which has won various IEEE–CEC competitions so far. Thorough uncertainty assessments via probability density function (PDF) and principal component analysis (PCA) demonstrated the solidity of the obtained inverse models. In the real data case, residual gravity anomalies of two well–known major grabens of Aegean Graben System (Türkiye), calculated thanks to the finite element method (FEM), were inverted. It was determined that the inverse solutions obtained for these basement reliefs, whose depths are still controversial, are statistically reliable. Moreover, these depths were found to be less than the depths reported in most previous studies. We conclude that the SHADE using EPSR strategy that we propose is a powerful alternative inversion tool for highly nonlinear geophysical problems.

No abstract

We propose a novel scheme that applies a multitasking convolutional neural network to learn the back azimuthal behavior from receiver function seismograms, which can effectively predict the depth and occurrence of the Moho beneath a single seismic station. Our scheme consists of three main steps: 1. Based on the style transfer technique, we generate 9000 synthetic receiver function seismograms blended by realistic noise as training data sets. 2. A multitasking convolutional neural network is trained to predict the depth and occurrence of the Moho. 3. All real receiver function seismograms are reconstructed by the accelerated joint iterative method before prediction. We apply the scheme to study the middle-southern of the Tanlu fault zone and adjacent regions and successfully achieve the depth and occurrence of the Moho beneath 10 permanent seismic stations. The predicted depths are in agreement with the results computed by conventional methods, and the predicted strikes and dip angles present an undulating Moho with near NE-striking. Moreover, the predicted strikes are nearly consistent with the strikes of the normal faults in the upper crust, which implies that intense continental extension in the Cretaceous play a prominent role in the tectonic deformation of the brittle upper crust and the ductile lower crust simultaneously. Besides, it helps to illustrate that the stress field orientation of the major geological event can be recorded and preserved in the lower crust.