Chengping Chai

Bio: Chengping Chai is an academic researcher from Oak Ridge National Laboratory. The author has contributed to research in topics: Nuclear reactor & Geology. The author has an hindex of 6, co-authored 16 publications receiving 99 citations. Previous affiliations of Chengping Chai include Pennsylvania State University & University of Tennessee.

Using a Deep Neural Network and Transfer Learning to Bridge Scales for Seismic Phase Picking

Abstract: The important task of tracking seismic activity requires both sensitive detection and accurate earthquake location. Approximate earthquake locations can be estimated promptly and automatically; how...

Inverting interpolated receiver functions with surface wave dispersion and gravity: Application to the western U.S. and adjacent Canada and Mexico

Abstract: We use P wave receiver functions from the western U.S. and adjacent regions to construct a receiver function wavefield interpolation scheme that helps to equalize the lateral sampling of the receiver functions and the surface wave dispersion and to greatly simplify the receiver functions. Spatial interpolation and smoothing suppress poorly sampled and difficult to interpret back azimuthal variations and allow the extraction of the first-order features in the receiver function wavefield, including observations from several ray parameter ranges. We combine the interpolated receiver functions with Rayleigh wave dispersion estimates and surface gravity observations to estimate the 3-D shear wave speed beneath the region. Speed variations in the 3-D model correlate strongly with expected geologic variations and illuminate broad-scale features of the western U.S. crust and upper mantle. The model is smooth, self-consistent, and demonstrates the compatibility of the interpolated receiver functions and dispersion observations.

Creation of a Mixed-Mode Fracture Network at Mesoscale Through Hydraulic Fracturing and Shear Stimulation

Abstract: Author(s): Schoenball, M; Ajo-Franklin, JB; Blankenship, D; Chai, C; Chakravarty, A; Dobson, P; Hopp, C; Kneafsey, T; Knox, HA; Maceira, M; Robertson, MC; Sprinkle, P; Strickland, C; Templeton, D; Schwering, PC; Ulrich, C; Wood, T; Ajo-Franklin, J; Baumgartner, T; Beckers, K; Blankenship, D; Bonneville, A; Boyd, L; Brown, S; Burghardt, JA; Chai, C; Chakravarty, A; Chen, T; Chen, Y; Chi, B; Condon, K; Cook, PJ; Crandall, D; Dobson, PF; Doe, T; Doughty, CA; Elsworth, D; Feldman, J; Feng, Z; Foris, A; Frash, LP; Frone, Z; Fu, P; Gao, K; Ghassemi, A; Guglielmi, Y; Haimson, B; Hawkins, A; Heise, J; Hopp, C; Horn, M; Horne, RN; Horner, J; Hu, M; Huang, H; Huang, L; Im, KJ; Ingraham, M; Jafarov, E; Jayne, RS; Johnson, TC; Johnson, SE; Johnston, B; Karra, S; Kim, K; King, DK; Kneafsey, T; Knox, H; Knox, J; Kumar, D; Kutun, K; Lee, M; Li, K; Li, Z; Mackey, P; Makedonska, N; Marone, CJ; Mattson, E; McClure, MW; McLennan, J; McLing, T; Medler, C; Mellors, RJ; Metcalfe, E; Miskimins, J | Abstract: Enhanced Geothermal Systems could provide a substantial contribution to the global energy demand if their implementation could overcome inherent challenges. Examples are insufficient created permeability, early thermal breakthrough, and unacceptable induced seismicity. Here we report on the seismic response of a mesoscale hydraulic fracturing experiment performed at 1.5-km depth at the Sanford Underground Research Facility. We have measured the seismic activity by utilizing a 100-kHz, continuous seismic monitoring system deployed in six 60-m length monitoring boreholes surrounding the experimental domain in 3-D. The achieved location uncertainty was on the order of 1nm and limited by the signal-to-noise ratio of detected events. These uncertainties were corroborated by detections of fracture intersections at the monitoring boreholes. Three intervals of the dedicated injection borehole were hydraulically stimulated by water injection at pressures up to 33nMPa and flow rates up to 5nL/min. We located 1,933 seismic events during several injection periods. The recorded seismicity delineates a complex fracture network comprised of multistrand hydraulic fractures and shear-reactivated, preexisting planes of weakness that grew unilaterally from the point of initiation. We find that heterogeneity of stress dictates the seismic outcome of hydraulic stimulations, even when relying on theoretically well-behaved hydraulic fractures. Once hydraulic fractures intersected boreholes, the boreholes acted as a pressure relief and fracture propagation ceased. In order to create an efficient subsurface heat exchanger, production boreholes should not be drilled before the end of hydraulic stimulations.

Close Observation of Hydraulic Fracturing at EGS Collab Experiment 1: Fracture Trajectory, Microseismic Interpretations, and the Role of Natural Fractures

Abstract: Author(s): Fu, P; Schoenball, M; Ajo-Franklin, JB; Chai, C; Maceira, M; Morris, JP; Wu, H; Knox, H; Schwering, PC; White, MD; Burghardt, JA; Strickland, CE; Johnson, TC; Vermeul, VR; Sprinkle, P; Roberts, B; Ulrich, C; Guglielmi, Y; Cook, PJ; Dobson, PF; Wood, T; Frash, LP; Huang, L; Ingraham, MD; Pope, JS; Smith, MM; Neupane, G; Doe, TW; Roggenthen, WM; Horne, R; Singh, A; Zoback, MD; Wang, H; Condon, K; Ghassemi, A; Chen, H; McClure, MW; Vandine, G; Blankenship, D; Kneafsey, TJ | Abstract: The final version of the above article was posted prematurely on 16 July 2021, owing to a technical error. The final, corrected version of record will be made fully available at a later date.

Recent Large Increases in Freshwater Fluxes from Greenland Into the North Atlantic

Abstract: [1] Freshwater (FW) fluxes from river runoff and precipitation minus evaporation for the pan Arctic seas are relatively well documented and prescribed in ocean GCMs. Fluxes from Greenland on the other hand are generally ignored altogether, despite their potential impacts on ocean circulation and marine biology. Here, we present a reconstruction of the spatially distributed FW flux from Greenland for 1958–2010. We find a modest increase into the Arctic Ocean during this period. Fluxes into the Irminger Basin, however, have increased by fifty percent (6.3 ± 0.5 km3 yr−2) in less than twenty years. This greatly exceeds previous estimates. For the ice sheet as a whole the rate of increase since 1992 is 16.9 ± 1.8 km3 yr−2. The cumulative FW anomaly since 1995 is 3200 ± 358 km3, which is about a third of the magnitude of the Great Salinity Anomaly (GSA) of the 1970s. If this trend continues into the future, the anomaly will exceed that of the GSA by about 2025.

Obtaining free USArray data by multi-dimensional seismic reconstruction.

Abstract: USArray, a pioneering project for the dense acquisition of earthquake data, provides a semi-uniform sampling of the seismic wavefield beneath its footprint and greatly advances the understanding of the structure and dynamics of Earth. Despite continuing efforts in improving the acquisition design, network irregularity still causes spatial sampling alias and incomplete, noisy data, which imposes major challenges in array-based data analysis and seismic imaging. Here we employ an iterative rank-reduction method to simultaneously reconstruct the missing traces and suppress noise, i.e., obtaining free USArray recordings as well as enhancing the existing data. This method exploits the spatial coherency of three-dimensional data and recovers the missing elements via the principal components of the incomplete data. We examine its merits using simulated and real teleseismic earthquake recordings. The reconstructed P wavefield enhances the spatial coherency and accuracy of tomographic travel time measurements, which demonstrates great potential to benefit seismic investigations based on array techniques. The USArray of EarthScope is a seismic broadband network acquiring global seismic data. Here, the authors apply an iterative rank-reduction method to obtain free earthquake data at locations where no seismic stations are available as well as enhancing existing data recorded by the USArray.

Improving the Signal‐to‐Noise Ratio of Seismological Datasets by Unsupervised Machine Learning

Abstract: Seismic waves that are recorded by near-surface sensors are usually disturbed by strong noise. Hence, the recorded seismic data are sometimes of poor quality; this phenomenon can be characterized as a low signal-to-noise ratio (SNR). The low SNR of the seismic data may lower the quality of many subsequent seismological analyses, such as inversion and imaging. Thus, the removal of unwanted seismic noise has significant importance. In this article, we intend to improve the SNR of many seismological datasets by developing new denoising framework that is based on an unsupervised machine-learning technique. We leverage the unsupervised learning philosophy of the autoencoding method to adaptively learn the seismic signals from the noisy observations. This could potentially enable us to better represent the true seismic-wave components. To mitigate the influence of the seismic noise on the learned features and suppress the trivial components associated with low-amplitude neurons in the hidden layer, we introduce a sparsity constraint to the autoencoder neural network. The sparse autoencoder method introduced in this article is effective in attenuating the seismic noise. More importantly, it is capable of preserving subtle features of the data, while removing the spatially incoherent random noise. We apply the proposed denoising framework to a reflection seismic image, depth-domain receiver function gather, and an earthquake stack dataset. The purpose of this study is to demonstrate the framework’s potential in real-world applications. INTRODUCTION Seismic phases from the discontinuities in the Earth’s interior contain significant constraints for high-resolution deep Earth imaging; however, they sometimes arrive as weak-amplitude waveforms (Rost and Weber, 2001; Rost and Thomas, 2002; Deuss, 2009; Saki et al., 2015; Guan and Niu, 2017, 2018; Schneider et al., 2017; Chai et al., 2018). The detection of these weak-amplitude seismic phases is sometimes challenging because of three main reasons: (1) the amplitude of these phases is very small and can be neglected easily when seen next to the amplitudes of neighboring phases that are much larger; (2) the coherency of the weak-amplitude seismic phases is seriously degraded because of insufficient array coverage and spatial sampling; and (3) the strong random background noise that is even stronger than the weak phases in amplitude makes the detection even harder. As an example of the challenges presented, the failure in detecting the weak reflection phases from mantle discontinuities could result in misunderstanding of the mineralogy or temperature properties of the Earth interior. To conquer the challenges in detecting weak seismic phases, we need to develop specific processing techniques. In earthquake seismology, in order to highlight a specific weak phase, recordings in the seismic arrays are often shifted and stacked for different slowness and back-azimuth values (Rost and Thomas, 2002). Stacking serves as one of the most widely used approaches in enhancing the energy of target signals. Shearer (1991a) stacked long-period seismograms of shallow earthquakes that were recorded from the Global Digital Seismograph Network for 5 yr and obtained a gather that shows typical arrivals clearly from the deep Earth. Morozov and Dueker (2003) investigated the effectiveness of stacking in enhancing the signals of the receiver functions. They defined a signal-to-noise ratio (SNR) metric that was based on the multichannel coherency of the signals and the incoherency of the random noise, and they showed that the stacking can significantly improve the SNR of the stacked seismic trace. However, stacking methods have some drawbacks. First, they do not necessarily remove the noise present in the signal. Second, they require a large array of seismometers. Third, they require coherency of arrivals across the array, which are not always about earthquake seismology. From this point of view, a single-channel method seems to be a better substitute for improving the SNR of seismograms (Mousavi and Langston, 2016; 2017). In the reflection seismology community, many noise attenuation methods have been proposed and implemented in field applications over the past several decades. Prediction-based methods utilize the predictive property of the seismic signal to construct a predictive filter that prevents noise. Median filters and their variants use the statistical principle to reject Gaussian white noise or impulsive noise (Mi et al., 2000; Bonar and Sacchi, 2012). The dictionary-learning-based methods adaptively learn the basis from the data to sparsify the noisy seismic data, which will in turn suppress the noise (Zhang, van der Baan, et al., 2018). These methods require experimenters to solve the dictionary-updating and sparse-coding methods and can be very 1552 Seismological Research Letters Volume 90, Number 4 July/August 2019 doi: 10.1785/0220190028 Downloaded from https://pubs.geoscienceworld.org/ssa/srl/article-pdf/90/4/1552/4790732/srl-2019028.1.pdf by Seismological Society of America, Mattie Adam on 09 July 2019 expensive, computationally speaking. Decomposition-based methods decompose the noisy data into constitutive components, so that one can easily select the components that primarily represent the signal and remove those associated with noise. This category includes singular value decomposition (SVD)-based methods (Bai et al., 2018), empirical-mode decomposition (Chen, 2016), continuous wavelet transform (Mousavi et al., 2016), morphological decomposition (Huang et al., 2017), and so on. Rank-reduction-based methods assume that seismic data have a low-rank structure (Kumar et al., 2015; Zhou et al., 2017). If the data consist of κ complex linear events, the constructed Hankel matrix of the frequency data is a matrix of rank κ (Hua, 1992). Noise will increase the rank of theHankel matrix of the data, which can be attenuated via rank reduction. Such methods include Cadzow filtering (Cadzow, 1988; Zu et al., 2017) and SVD (Vautard et al., 1992). Most of the denoising methods are largely effective in processing reflection seismic images. The applications in more general seismological datasets are seldom reported, partially because of the fact that many seismological datasets have extremely low data quality. That is, they are characterized by low SNR and poor spatial sampling. Besides, most traditional denoising algorithms are based on carefully tuned parameters to obtain satisfactory performance. These parameters are usually data dependent and require a great deal of experiential knowledge. Thus, they are not flexible enough to use in application to many real-world problems. More research efforts have been dedicated to using machine-learning methods for seismological data processing (Chen, 2018a,b; Zhang, Wang, et al., 2018; Bergen et al., 2019; Lomax et al., 2019; McBrearty et al., 2019). Recently, supervised learning (Zhu et al., 2018) has been successfully applied for denoising of the seismic signals. However, supervised methods with deep networks require very large training datasets (sometimes to an order of a billion) of clean signals and their noisy contaminated realizations. In this article, we develop a new automatic denoising framework for improving the SNR of the seismological datasets based on an unsupervised machine-learning (UML) approach; this would be the autoencoder method. We leverage the autoencoder neural network to adaptively learn the features from the raw noisy seismological datasets during the encoding process, and then we optimally represent the data using these learned features during the decoding process. To effectively suppress the random noise, we use the sparsity constraint to regularize the neurons in the hidden layer. We apply the proposed UML-based denoising framework to a group of seismological datasets, including a reflection seismic image, a receiver function gather, and an earthquake stack. We observe a very encouraging performance, which demonstrates its great potential in a wide range of applications. METHOD Unsupervised Autoencoder Method Wewill first introduce the autoencoder neural network that we use for denoising seismological datasets. Autoencoders are specific neural networks that consist of two connected parts (decoder and encoder) that try to copy their input to the output layer. Hence, they can automatically learn the main features of the data in an unsupervised manner. In this article, the network is simply a three-layer architecture with an input layer, a hidden layer, and an output layer. The encoding process in the autoencoder neural network can be expressed as follows: EQ-TARGET;temp:intralink-;df1;323;673 ξ W1x b1 ; 1 in which x is the training sample (x∈Rn), ξ is the activation function. The decoding process can be expressed as follows: EQ-TARGET;temp:intralink-;df2;323;608 x ⌢ ξ W2x b2 : 2 In equations (1) and (2), W1 is the weighting matrix between the input layer and the hidden layer; b1 is the forward bias vector; W2 is the weighting matrix between the hidden layer and output layer; b2 is the backward bias vector; and ξ is the activation function. In this study, we use the softplus function as the activation function: EQ-TARGET;temp:intralink-;df3;323;505 ξ x log 1 e : 3 Sparsity Regularized Autoencoder To mitigate the influence of the seismic noise on the learned features and suppress the trivial components associated with low-amplitude neurons in the hidden layer, we apply a sparsity constraint to the hidden layer; that is, the output or last layer of the encoder. The sparsity constraint can help dropout the extracted nontrivial features that correspond to the noise and a small value in the hidden units. It can thus highlight the most dominant features in the data—the useful signals. The sparse penalty term can be written as follows: EQ-TARGET;temp:intralink-;df4;323;335~ R p ; 4 in which R is the penalty function: EQ-TARGET;temp: