Bulk Crustal Properties and Layered Velocity Structure in Fujian, SE China: Constraints From P and S Receiver Functions

Multiple tectonic events since the Neoproterozoic era have framed the present-day lithosphere in the Fujian province affiliated with the eastern part of the South China Block. Comprehensive information of the crustal structure and bulk properties can aid to understand the geological features and tectonic processes of still much debate in this region. An attempt is made in this study to explore crustal thickness and internal velocities across Fujian using the teleseismic receiver functions (RFs). The H-V stacking of joint P and S RFs improves to simultaneously estimate crustal thickness, average Vp and Vs, and derived Vp/Vs ratio and bulk sound speed in three backazimuth sectors for each of 17 stations. Furthermore, a Neighborhood Algorithm nonlinear inversion of P RFs is employed to determine the layered structures of Vs and Vp/Vs beneath all the stations. Results indicate the crustal thickness varies from at most ∼35 km in northwest Fujian to 30–35 km in the inland mountains and 27–30 km in the southeastern coasts. The inferred Moho geometry is nonplanar or inclined across the Zhenghe-Dapu (ZD) and Changle-Zhaoan (CZ) fault zones, especially in the southern ZD fault area. The average Vp/Vs suggests that the crust is predominantly felsic in the Wuyi-Yunkai orogen and intermediate-to-mafic in the Cretaceous magmatic and metamorphic zones. A high-velocity upper crust along the coastline is revealed, which attributes to the Pingtan-Dongshan metamorphic belt. At the sites near the ZD fault zone, the intracrustal negative discontinuity occurs at a shallower depth of ∼15 km marking an abrupt Vs decrease into the low-velocity mid-to-lower crustal layer, probably linked to the closed paleo-rift basin remnants. The lower crust across the Fujian is generally characterized by relatively lower Vs and higher Vp/Vs (1.80–1.84) consistent with those of the mafic-ultramafic rocks, which do not support the proposed extensive magmatic underplating in the Late Mesozoic.

Improved Hybrid Particle Swarm Optimizer With Sine-Cosine Acceleration Coefficients For Transient Electromagnetic Inversion

Background: Recently, particle swarm optimization (PSO) has been increasingly used in geophysics due to its simple operation and fast convergence. Objective: However, PSO lacks population diversity and may fall to local optima. Hence, an improved hybrid particle swarm optimizer with sine-cosine acceleration coefficients (IH-PSO-SCAC) is proposed and successfully applied to test functions and in transient electromagnetic (TEM) nonlinear inversion. Method: A reverse learning strategy is applied to optimize population initialization. The sine-cosine acceleration coefficients are utilized for global convergence. Sine mapping is adopted to enhance population diversity during the search process. In addition, the mutation method is used to reduce the probability of premature convergence. Results: The application of IH-PSO-SCAC in the test functions and several simple layered models are demonstrated with satisfactory results in terms of data fit. Two inversions have been carried out to test our algorithm. The first model contains an underground low-resistivity anomaly body and the second model utilized measured data from a profile of the Xishan landslide in Sichuan Province. In both cases, resistivity profiles are obtained, and the inverse problem is solved for verification. Conclusion: The results show that the IH-PSO-SCAC algorithm is practical, can be effectively applied in TEM inversion and is superior to other representative algorithms in terms of stability and accuracy.

An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration

The estimation of the subsurface acoustic impedance (AI) model is an important step of seismic data processing for oil and gas exploration. The full waveform inversion (FWI) is a powerful way to invert the subsurface parameters with surface acquired seismic data. Nevertheless, the strong nonlinear relationship between the seismic data and the subsurface model will cause nonconvergence and unstable problems in practice. To divide the nonlinear inversion into some more linear steps, a 2D AI inversion imaging method is proposed to estimate the broadband AI model based on a broadband reflectivity. Firstly, a novel scheme based on Gaussian beam migration (GBM) is proposed to produce the point spread function (PSF) and conventional image of the subsurface. Then, the broadband reflectivity can be obtained by implementing deconvolution on the image with respect to the calculated PSF. Assuming that the low-wavenumber part of the AI model can be deduced by the background velocity, we implemented the AI inversion imaging scheme by merging the obtained broadband reflectivity as the high-wavenumber part of the AI model and produced a broadband AI result. The developed broadband migration based on GBM as the computational hotspot of the proposed 2D AI inversion imaging includes only two GBM and one Gaussian beam demigraton (Born modeling) processes. Hence, the developed broadband GBM is more efficient than the broadband imaging using the least-squares migrations (LSMs) that require multiple iterations (every iteration includes one Born modeling and one migration process) to minimize the objective function of data residuals. Numerical examples of both synthetic data and field data have demonstrated the validity and application potential of the proposed method.

A METHOD OF WATER DEPTH INVERSION IN COASTAL AREA CONSIDERING TEMPERATURE INFORMATION

The remote sensing method for water depth inversion is fast, flexible, and low in cost, which has become an important means of method for water depth detection. This paper takes the coastal area where is around Gulangyu Island as the research area. Based on the spectral reflectance, sea surface temperature (SST) and measured water depth data, a nonlinear inversion model of water depth is established by using BP neural network. Combined with the tide data, the water depth and underwater topography in coastal area is obtained. The average relative error is 0.27. The root mean square error is 1.92. The results show that the participation of sea surface temperature in the model construction can improve the inversion error of offshore water depth to a certain extent, and can help improve the accuracy of the model.

Developing open-source tools for reproducible inverse problems: the PyLops journey

<p>Inverse problems lie at the core of many geophysical algorithms, from earthquake and exploration seismology, all the way to electromagnetics and gravity potential methods.</p><p>In 2018, we open-sourced PyLops, a Python-based framework for large-scale inverse problems. By leveraging the concept of matrix-free linear operators – together with the efficiency of numerical libraries such as NumPy, SciPy, and Numba – PyLops solves computationally intensive inverse problems with high-level code that is highly readable and resembles the underlying mathematical formulation. While initially aimed at researchers, its parsimonious software design choices, large test suite, and thorough documentation render PyLops a reliable and scalable software package easy to run both locally and in the cloud.</p><p>Since its initial release, PyLops has incorporated several advancements in scientific computing leading to the creation of an entire ecosystem of tools: operators can now run on GPUs via CuPy, scale to distributed computing through Dask, and be seamlessly integrated into PyTorch’s autograd to facilitate research in machine-learning-aided inverse problems. Moreover, PyLops contains a large variety of inverse solvers including least-squares, sparsity-promoting algorithms, and proximal solvers highly-suited to convex, possibly nonsmooth problems. PyLops also contains sparsifying transforms (e.g., wavelets, curvelets, seislets) which can be used in conjunction with the solvers. By offering a diverse set of tools for inverse problems under one unified framework, it expedites the use of state-of-the-art optimization methods and compressive sensing techniques in the geoscience domain.</p><p>Beyond our initial expectations, the framework is currently used to solve problems beyond geoscience, including astrophysics and medical imaging. Likewise, it has inspired the development of the occamypy framework for nonlinear inversion in geophysics. In this talk, we share our experience in building such an ecosystem and offer further insights into the needs and interests of the EGU community to help guide future development as well as achieve wider adoption.</p>

Interrogating Tomographic Uncertainties for Subsurface Structural Information

<p>The ultimate goal of a geophysical investigation is usually to find answers to scientific (often low-dimensional) questions: how large is a subsurface body? How deeply does lithosphere subduct? Does a certain subsurface feature exist? Background research reviews existing information, an experiment is designed and performed to acquire new data, and the most likely answer is estimated. Typically the answer is interpreted from geophysical inversions, but is usually biased because only one particular forward function (model-data relationship) is considered, one inversion method is used, and because human interpretation is a biased process. <strong><em>Interrogation theory </em></strong>provides a systematic way to answer specific questions. Answers balance information from multiple forward models, inverse methods and model parametrizations probabilistically, and optimal answers are found using decision theory.</p><p>Two examples illustrate interrogation of the Earth’s subsurface. In a synthetic test, we estimate the cross-sectional area of a subsurface low velocity anomaly by interrogating Bayesian probabilistic tomographic maps. By combining the results of four different nonlinear inversion algorithms, the optimal answer is very close to the true answer. In a field data application, we evaluate the extent of the Irish Sea Sedimentary Basin based on the uncertainties in velocity structure derived from Love wave tomography. This example shows that the computational expense of estimating uncertainties adds explicit value to answers.</p><p>This study demonstrates that interrogation theory answers realistic questions about the Earth’s subsurface. The same theory can be used to solve different types of scientific problem - experimental design, interpreting models, expert elicitation and risk estimation - and can be applied in any field of science. One of its most important contributions is to show that fully nonlinear estimates of uncertainty are critical for decision-making in real-world geoscientific problems, potentially justifying their computational expense.</p><p> </p>