Master of Science

thesisThe electrical induced polarization (EIP) method has been used in mineral exploration for over half a century, but its application was limited due to the relatively high cost involved and a requirement of using a galvanic source and receivers having a direct contact with the ground. In order to overcome these limitations, the magnetic induced polarization (MIP) method was introduced. However, interpretation of the MIP data in a complex three-dimensional (3D) environment represents a very challenging problem. In this thesis, a new method of interpretation of 3D MIP data was introduced based on the generalized effective medium theory of induced polarization (GEMTIP). The GEMTIP model was established to describe the induced polarization (IP) effect in complex heterogeneous rock samples. I have incorporated the GEMTIP model with the integral equation (IE) method to simulate the magnetic field caused by the IP effect. I have also developed an inversion algorithm based on the regularized conjugate gradient method. The inversion produces a 3D distribution of the four parameters of the GEMTIP model-matrix conductivity (σ0) (or DC resistivity ρ0= 1/σ0), fraction volume (f), time constant (t), and relaxation parameter (C). The developed methods and computer codes were tested on synthetic MIP data. I have also applied this new method for interpretation of the synthetic MIP data computer simulated for the real geological structure of the Silver Bell area in Arizona

Multiscale Data-driven Seismic Full-waveform Inversion with Field Data Study

Seismic full-waveform inversion (FWI), which applies iterative methods to estimate high-resolution subsurface detail from seismograms, is a powerful imaging technique in exploration geophysics. In recent years the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a non-convex problem, and can become stuck in a local minima due to the limited accuracy of the initial velocity maps, the absence of low frequencies in the measurements, the presence of noise, and the approximate modeling of the wave-physics complexity. To overcome these computational issues, we develop a multiscale data-driven FWI method based on the fully convolutional network (FCN). In preparing the training data, we first develop a real-time style transform method to create a large set of physically realistic subsurface velocity maps from natural images. We then develop two convolutional neural networks with encoder-decoder structure to reconstruct the low- and high-frequency components of the subsurface velocity maps, respectively. To validate the performance of our new data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. These numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time, and yield more accurate subsurface velocity map in comparison with conventional FWI.Comment: 14 pages, 17 figure

InversionNet3D: Efficient and Scalable Learning for 3D Full Waveform Inversion

Seismic full-waveform inversion (FWI) techniques aim to find a high-resolution subsurface geophysical model provided with waveform data. Some recent effort in data-driven FWI has shown some encouraging results in obtaining 2D velocity maps. However, due to high computational complexity and large memory consumption, the reconstruction of 3D high-resolution velocity maps via deep networks is still a great challenge. In this paper, we present InversionNet3D, an efficient and scalable encoder-decoder network for 3D FWI. The proposed method employs group convolution in the encoder to establish an effective hierarchy for learning information from multiple sources while cutting down unnecessary parameters and operations at the same time. The introduction of invertible layers further reduces the memory consumption of intermediate features during training and thus enables the development of deeper networks with more layers and higher capacity as required by different application scenarios. Experiments on the 3D Kimberlina dataset demonstrate that InversionNet3D achieves state-of-the-art reconstruction performance with lower computational cost and lower memory footprint compared to the baseline

Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness

Full waveform inversion (FWI) infers the subsurface structure information from seismic waveform data by solving a non-convex optimization problem. Data-driven FWI has been increasingly studied with various neural network architectures to improve accuracy and computational efficiency. Nevertheless, the applicability of pre-trained neural networks is severely restricted by potential discrepancies between the source function used in the field survey and the one utilized during training. Here, we develop a Fourier-enhanced deep operator network (Fourier-DeepONet) for FWI with the generalization of seismic sources, including the frequencies and locations of sources. Specifically, we employ the Fourier neural operator as the decoder of DeepONet, and we utilize source parameters as one input of Fourier-DeepONet, facilitating the resolution of FWI with variable sources. To test Fourier-DeepONet, we develop two new and realistic FWI benchmark datasets (FWI-F and FWI-L) with varying source frequencies and locations. Our experiments demonstrate that compared with existing data-driven FWI methods, Fourier-DeepONet obtains more accurate predictions of subsurface structures in a wide range of source parameters. Moreover, the proposed Fourier-DeepONet exhibits superior robustness when dealing with noisy inputs or inputs with missing traces, paving the way for more reliable and accurate subsurface imaging across diverse real conditions

Does Full Waveform Inversion Benefit from Big Data?

This paper investigates the impact of big data on deep learning models for full waveform inversion (FWI). While it is well known that big data can boost the performance of deep learning models in many tasks, its effectiveness has not been validated for FWI. To address this gap, we present an empirical study that investigates how deep learning models in FWI behave when trained on OpenFWI, a collection of large-scale, multi-structural datasets published recently. Particularly, we train and evaluate the FWI models on a combination of 10 2D subsets in OpenFWI that contain 470K data pairs in total. Our experiments demonstrate that larger datasets lead to better performance and generalization of deep learning models for FWI. We further demonstrate that model capacity needs to scale in accordance with data size for optimal improvement

Communication-Assisted Sensing in 6G Networks

The exploration of coordination gain achieved through the synergy of sensing and communication (S&C) functions plays a vital role in improving the performance of integrated sensing and communication systems. This paper focuses on the optimal waveform design for communication-assisted sensing (CAS) systems within the context of 6G perceptive networks. In the CAS process, the base station actively senses the targets through device-free wireless sensing and simultaneously transmits the pertinent information to end-users. In our research, we establish a CAS framework grounded in the principles of rate-distortion theory and the source-channel separation theorem (SCT) in lossy data transmission. This framework provides a comprehensive understanding of the interplay between distortion, coding rate, and channel capacity. The purpose of waveform design is to minimize the sensing distortion at the user end while adhering to the SCT and power budget constraints. In the context of target response matrix estimation, we propose two distinct waveform strategies: the separated S&C and dual-functional waveform schemes. In the former strategy, we develop a simple one-dimensional search algorithm, shedding light on a notable power allocation tradeoff between the S&C waveform. In the latter scheme, we conceive a heuristic mutual information optimization algorithm for the general case, alongside a modified gradient projection algorithm tailored for the scenarios with independent sensing sub-channels. Additionally, we identify the presence of both subspace tradeoff and water-filling tradeoff. Finally, we validate the effectiveness of the proposed algorithms through numerical simulations

Sensing With Random Signals

Radar systems typically employ well-designed deterministic signals for target sensing. In contrast to that, integrated sensing and communications (ISAC) systems have to use random signals to convey useful information, potentially causing sensing performance degradation. This paper analyzes the sensing performance via random ISAC signals over a multi-antenna system. Towards this end, we define a new sensing performance metric, namely, ergodic linear minimum mean square error (ELMMSE), which characterizes the estimation error averaged over the randomness of ISAC signals. Then, we investigate a data-dependent precoding scheme to minimize the ELMMSE, which attains the {optimized} sensing performance at the price of high computational complexity. To reduce the complexity, we present an alternative data-independent precoding scheme and propose a stochastic gradient projection (SGP) algorithm for ELMMSE minimization, which can be trained offline by locally generated signal samples. Finally, we demonstrate the superiority of the proposed methods by simulations.Comment: 6 pages, 5 figures, submitted to ICASSP 202

EFWI\mathbf{\mathbb{E}^{FWI}}: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties

Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO$_2$ sequestration and energy exploration (e.g., hydrogen and geothermal). Elastic full waveform inversion (FWI) is widely applied for characterizing reservoir properties. In this paper, we introduce $\mathbf{\mathbb{E}^{FWI}}$, a comprehensive benchmark dataset that is specifically designed for elastic FWI. $\mathbf{\mathbb{E}^{FWI}}$ encompasses 8 distinct datasets that cover diverse subsurface geologic structures (flat, curve, faults, etc). The benchmark results produced by three different deep learning methods are provided. In contrast to our previously presented dataset (pressure recordings) for acoustic FWI (referred to as OpenFWI), the seismic dataset in $\mathbf{\mathbb{E}^{FWI}}$ has both vertical and horizontal components. Moreover, the velocity maps in $\mathbf{\mathbb{E}^{FWI}}$ incorporate both P- and S-wave velocities. While the multicomponent data and the added S-wave velocity make the data more realistic, more challenges are introduced regarding the convergence and computational cost of the inversion. We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data. The relation between P- and S-wave velocities provides crucial insights into the subsurface properties such as lithology, porosity, fluid content, etc. We anticipate that $\mathbf{\mathbb{E}^{FWI}}$ will facilitate future research on multiparameter inversions and stimulate endeavors in several critical research topics of carbon-zero and new energy exploration. All datasets, codes and relevant information can be accessed through our website at https://efwi-lanl.github.io/Comment: 20 pages, 11 figure

Rational design of interlaced Co 9 S 8 /carbon composites from ZIF-67/cellulose nanofibers for enhanced lithium storage

Abstract(#br)Cellulose nanofibers (CNFs) are used to string ZIF-67 particles and interlaced Co 9 S 8 /porous carbon composite (Co 9 S 8 /C-CNFs) is obtained via carbonization and sulphidation of ZIF-67/CNFs composites. The CNFs can effectively limit the growth of ZIF-67 particles and avoid the agglomeration and most importantly, serve as the conductive skeleton to “bridge” carbonized ZIF-67 particles after carbonization. Due to the unique structure and the improved conductivity, Co 9 S 8 /C-CNFs as anode of lithium-ion batteries exhibits enhanced electrochemical properties and the specific capacity is 700 mAh g −1 at current density of 500 mA g −1 after 150 cycles compared to that of 342 mAh g −1 for samples without CNFs incorporation. Such nanoscale design may boost to explore other nanocomposites for energy storage

Improved Biocompatibility of Novel Biodegradable Scaffold Composed of Poly-L-lactic Acid and Amorphous Calcium Phosphate Nanoparticles in Porcine Coronary Artery

Using poly-L-lactic acid for implantable biodegradable scaffold has potential biocompatibility issue due to its acidic degradation byproducts. We have previously reported that the addition of amorphous calcium phosphate improved poly-L-lactic acid coating biocompatibility. In the present study, poly-L-lactic acid and poly-L-lactic acid/amorphous calcium phosphate scaffolds were implanted in pig coronary arteries for 28 days. At the follow-up angiographic evaluation, no case of stent thrombosis was observed, and the arteries that were stented with the copolymer scaffold had significantly less inflammation and nuclear factor-κB expression and a greater degree of reendothelialization. The serum levels of vascular endothelial growth factor and nitric oxide, as well the expression of endothelial nitric oxide synthase and platelet-endothelial cell adhesion molecule-1, were also significantly higher. In conclusion, the addition of amorphous calcium phosphate to biodegradable poly-L-lactic acid scaffold minimizes the inflammatory response, promotes the growth of endothelial cells, and accelerates the reendothelialization of the stented coronary arteries