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Automatic identification of faults on seismic structural images is a challenging yet crucial task in quantitative seismic interpretation. Human picking or attribute-based fault detection methods may misidentify faults on noisy, complex seismic images. We develop a new automatic fault detection method using a nested residual U-shaped convolutional neural network. Each of the encoders and decoders in this neural network is a residual U-Net, leading to a nested architecture. The final fault map results from the fusion of three fault maps with low, medium, and high fault resolutions. We demonstrate the excellent fault-detection capability of our nested neural network using a series of synthetic and field seismic images. We find that our approach produces clearer and more interpretable fault maps than the current state-of-the-art U-Net fault detection method, particularly on noisy seismic images. Our new automatic fault detection method can facilitate reliable quantitative seismic interpretation on field seismic images.

CoMoO4 with different morphologies can be covered on Co11(HPO3)8(OH)6 by simple hydrothermal and reflux methods, respectively, and this feasible strategy can enhance the practical application of cobalt phosphate salts.

We evaluate the performance of traveltime tomography and full-wave inversion (FWI) for near-surface modeling using the data from a shallow seismic field experiment. Eight boreholes up to 20-m depth have been drilled along the seismic line traverse to verify the accuracy of the P-wave velocity-depth model estimated by seismic inversion. The velocity-depth model of the soil column estimated by traveltime tomography is in good agreement with the borehole data. We used the traveltime tomography model as an initial model and performed FWI. Full-wave acoustic and elastic inversions, however, have failed to converge to a velocity-depth model that desirably should be a high-resolution version of the model estimated by traveltime tomography. Moreover, there are significant discrepancies between the estimated models and the borehole data. It is understandable why full-wave acoustic inversion would fail — land seismic data inherently are elastic wavefields. The question is: Why does full-wave elastic inversion also fail? The strategy to prevent full-wave elastic inversion of vertical-component geophone data trapped in a local minimum that results in a physically implausible near-surface model may be cascaded inversion. Specifically, we perform traveltime tomography to estimate a P-wave velocity-depth model for the near-surface and Rayleigh-wave inversion to estimate an S-wave velocity-depth model for the near-surface, then use the resulting pairs of models as the initial models for the subsequent full-wave elastic inversion. Nonetheless, as demonstrated by the field data example here, the elastic-wave inversion yields a near-surface solution that still is not in agreement with the borehole data. Here, we investigate the limitations of FWI applied to land seismic data for near-surface modeling.

We have developed a new data-driven algorithm that uses directional elastic wave packets as seismic sources to image subsurface voids (i.e., cavities). Compared to a point source, the advantage of the new approach is that the wave packet illuminates only a small volume of the medium around the raypath to significantly reduce multiple scattering effects in the imaging. We take the difference of traces at identical source-receiver offsets from each of two neighboring source packets. The difference mainly contains the void scattering events but not the direct waves, the layer reflections, refractions, nor layer-related multiples. We use P-to-P and P-to-S scattered waves to locate the voids, and the results using scattered P- and S-waves can cross-validate each other to reduce the possibility of false detections. The directional wave packet can be numerically synthesized using existing shot gathers; therefore, no special physical source is required. We determine our method using data calculated using a boundary element method to model the seismic wavefield in an irregularly layered medium containing several empty voids. We test the robustness of our method using the same data but with 15% root-mean-square random noise added. Furthermore, we compare our method with the reverse time migration imaging method using the same data and find that our method provides superior results that are not dependent on the construction of a velocity model.

This paper presents a novel workflow to enhance the interpretation of microseismic events by comparing the temporal evolution of the microseismic cloud between adjacent stages from two different wells stages. The stratigraphic properties of identified rock layers along with changes within the local stress field distribution were used to determine the propagation path and aperture of the hydraulic fracture. Hydraulic fractures however are largely aseismic, and thus identified microseismic signatures surrounding the hydraulic fracture may indicate important surrounding damage-zone fracture formation. A comparison of each microseismic event and towith the local rock stratigraphy of the loci determined certain regions where rock composition and larger formation layers influenced the moicroseismic signals of events. This analysis allowed for the classification of microseismic events by formation layers and can elicit different in-situ stress states during hydraulic stimulation. Principal Component Analysis of each formation microseismic cloud can quickly show dominating stresses in the microseismic signals. The changes in the microseismic cloud between the first and second stimulated and second wells during a zipper hydraulic fracture stimulations shows the significant changes in formation stress from one well to another in a multi-well system.

The EGS Collab project, supported by the US Department of Energy, is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory to address challenges in implementing enhanced geothermal systems (EGS). Data and observations from the field tests are compared to simulations to understand processes and build confidence in numerical modeling of the processes. Experiment 1 examined hydraulic fracturing in a well-characterized fractured phyllite 1.5 km deep at the Sanford Underground Research Facility (SURF). Testbed characterization included fracture mapping, borehole acoustic and optical televiewers, full waveform sonic, conductivity, resistivity, temperature, campaign p- and s-wave investigations and electrical resistance tomography. Borehole geophysical techniques including passive seismic, continuous active source seismic monitoring, electrical resistance tomography, fiber-based distributed strain, distributed temperature, and distributed acoustic monitoring, were used to carefully monitor stimulation events and flow tests. More than a dozen stimulations and nearly one year of flow tests were performed. Quality data and detailed observations were collected and analyzed during stimulation and water flow tests, and these data are available. We achieved adaptive control of the tests using real-time monitoring and rapid dissemination of data and near-real-time simulation. Experiment 2 examines the potential for hydraulic shearing in amphibolite 1.25 km deep at SURF. The testbed consists of nine subhorizontal boreholes, four of which surround the testbed with grouted-in ERT, seismic sensors, CASSM and distributed fiber sensors. The test wells include a “five-spot” set with an injection well and four production/monitoring wells. Like Experiment 1, the testbed was characterized geophysically and hydrologically, and three stimulations have been performed using new tools.

Elastic-wave imaging using multicomponent data can provide more useful subsurface information than acoustic-wave imaging, but it is usually algorithmically challenging. We have developed a vector elastic deconvolution migration method for high-resolution imaging of subsurface structures in isotropic and anisotropic elastic media. Our new method uses a vector deconvolution imaging condition based on dual wavefield decomposition, including an explicit directional wavefield separation using the Hilbert transform and a P/S vector wavefield decomposition using the low-rank decomposition method. Using three elastic models, we numerically determine that our new method produces notably higher resolution and more amplitude-balanced elastic images compared with a crosscorrelation-based vector elastic reverse time migration method.

SUMMARY The computational cost of elastic-waveform inversion is too high for inverting PP reflections, while using acoustic full-waveform inversion (FWI) is inaccurate because it does not depend on the shear modulus/velocity/impedance that affects elastic PP wavefield amplitudes. To solve this problem, we develop a waveform inversion method that uses acoustic least-squares reverse-time migration (LSRTM) to compensate the shear reflectivity for acoustic FWI. Our method is based on the quasi-elastic-wave equation developed by Chapman et al. (2014). The quasi-elastic-wave equation uses a linearized acoustic-wave equation with shear modulus μ as a virtual source to correct the acoustic PP wavefield amplitudes toward elastic ones. Our waveform inversion method inverts for elastic parameters by minimizing the L2 norm of the difference between recorded and predicted PP reflections modelled using the quasi-elastic-wave equation. Numerical tests on synthetic and field data show that our method can properly handle the amplitudes of elastic PP reflections and provides an accurate estimate of the P- and S-wave velocities/impedances and, in some cases, the density. The method does not need the computationally expensive numerical solution to the elastic-wave equation. It also gives a better estimate of elastic parameters than a pure LSRTM method for elastic PP reflections.

SUMMARY Wave-equation migration velocity analysis (WEMVA) is an image-domain inversion method for velocity model building. Automatic plane-wave WEMVA (PWEMVA) calculates the moveouts of plane-wave common-image gathers (CIGs) by searching a best-fitting parabola with semblance analysis and backprojects residual CIG moveouts into wavefield wave paths with a reflection tomographic kernel. However, 3-D PWEMVA is very computationally expensive because 3-D reflection tomographic inversion requires at least five 3-D reverse-time migrations per iteration and stores two types of source wavefields at model boundaries. We develop a joint inline and crossline PWEMVA method for efficient 3-D velocity model building. We alternatively implement the inline and crossline PWEMVAs with a constraint for each other, in which we iteratively construct the 3-D velocity model update through 1-D spline interpolation of 2-D gradients. The inline and crossline joint inversion is practical since PWEMVA only inverts for low-wavenumber velocity perturbations along wave paths, and the method can take less than 1 per cent of the computational cost of full 3-D PWEMVA. To construct unaliased plane waves for our joint inline and crossline PWEMVA, we develop a 3-D data interpolation method in the frequency–wavenumber (FK) domain to recover regularly and randomly missing traces. The method minimizes the misfit on sufficiently localized data subsets with iterative optimal step lengths and a gradient preconditioner that iteratively selects dominant dips along different azimuths. In numerical experiments, we use a 3-D synthetic seismic data set and a land 3-D field seismic data set acquired at the Farnsworth CO2-EOR (enhanced oil recovery) field to demonstrate the efficacy of our velocity model building and data interpolation methods.

Reverse time migration (RTM) can produce high-quality images of complex subsurface structures when using seismic data acquired by a reasonably dense data acquisition geometry. However, RTM produces significant image artifacts when using data from a sparse data acquisition geometry because of incomplete cancellation of migration “smiles.” These artifacts obscure migration images of actual geology, leading to possible misidentification of important geologic features of interest. A specularity filter based on the semblance equation is commonly used in the dip-angle angle-domain common image gather (ADCIG) to preserve signals while suppressing image artifacts. In dip-angle ADCIG, the signals are assumed to have higher semblance scores because they are horizontally more coherent than the artifacts. However, this assumption fails when the image artifacts are severe. We have developed a new approach to suppressing migration image artifacts using a support vector machine (SVM) method. We first develop multiple criteria to distinguish between the signals and artifacts in the dip-angle ADCIG, rather than using only the semblance criterion. We then calculate the weights using a supervised SVM method. The weights approach one for valid signal points, and approach zero for artifact points. Finally, we apply the weights to the dip-angle ADCIG to preserve the effective signals and suppress the image artifacts. We verify the effectiveness of our method, denoted as SVM filtering, using numerical tests on synthetic and field data to produce migration images with improved signal-to-noise ratios and reduced aliasing artifacts.

Accurate imaging of subsurface complex structures with faults is crucial for geothermal exploration because faults are generally the primary conduit of hydrothermal flow. It is very challenging to image geothermal exploration areas because of complex geologic structures with various faults and noisy surface seismic data with strong and coherent ground-roll noise. In addition, fracture zones and most geologic formations behave as anisotropic media for seismic-wave propagation. Properly suppressing ground-roll noise and accounting for subsurface anisotropic properties are essential for high-resolution imaging of subsurface structures and faults for geothermal exploration. We develop a novel wavenumber-adaptive bandpass filter to suppress the ground-roll noise without affecting useful seismic signals. This filter adaptively exploits both characteristics of the lower frequency and the smaller velocity of the ground-roll noise than those of the signals. Consequently, this filter can effectively differentiate the ground-roll noise from the signal. We use our novel filter to attenuate the ground-roll noise in seismic data along five survey lines acquired by the U.S. Navy Geothermal Program Office at Pirouette Mountain and Eleven-Mile Canyon in Nevada, United States. We then apply our novel anisotropic least-squares reverse-time migration algorithm to the resulting data for imaging subsurface structures at the Pirouette Mountain and Eleven-Mile Canyon geothermal exploration areas. The migration method employs an efficient implicit wavefield-separation scheme to reduce image artifacts and improve the image quality. Our results demonstrate that our wavenumber-adaptive bandpass filtering method successfully suppresses the strong and coherent ground-roll noise in the land seismic data, and our anisotropic least-squares reverse-time migration produces high-resolution subsurface images of Pirouette Mountain and Eleven-Mile Canyon, facilitating accurate fault interpretation for geothermal exploration.

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.

Vector elastic reverse time migration (ERTM) produces subsurface elastic images with correct polarities using multicomponent seismic data. However, the decomposition of elastic wavefields into vector P- and S-wavefields is computationally expensive, particularly in heterogeneous and complex anisotropic media. We have developed a computationally efficient vector ERTM method in the hybrid time and frequency domain by combining three existing techniques. Rather than decomposing elastic wavefields into vector qP- and qS-wavefields during time-domain wavefield propagation, we conduct the wavefield decomposition in the frequency domain for several selected frequencies. In general, the number of selected frequencies needed for migration imaging is much smaller than the number of time steps during forward and backward wavefield propagation, leading to greatly reduced computational costs associated with the qP-/qS-wavefield vector separation in complex heterogeneous anisotropic media. We further combine an implicit directional wavefield separation into the vector ERTM to enhance the image quality. The numerical results demonstrate that our method produces high-quality elastic-wave migration images with notably reduced computational costs compared to the conventional vector ERTM method.

Conventional joint PP and PS velocity analysis is based on ray tomography. We develop a joint PP and PS wave-equation migration-velocity-analysis method using plane-wave common-image gathers (CIGs) to produce accurate P- and S-wave velocity models. The objective function of our new method consists of three terms: The first and second terms penalize the moveout residuals computed from PP and PS plane-wave CIGs, respectively, and the third term constrains the nonzero relative depth shifts between the PP and PS migration images. The moveout of plane-wave CIGs is automatically picked using a semblance analysis method, and the relative depth shifts between the PP and PS images are automatically computed using dynamic warping or manually picking the depths of certain primary reflectors. The moveout residuals and the relative depth shifts are transformed into weighted image perturbations, and they are then projected into the velocity models to update the P- and S-wave velocity models using the scalar-wave equations and their linearized forms. Numerical tests with synthetic and multicomponent field data demonstrate that our method can simultaneously invert for accurate P- and S-wave velocity models for elastic migration.

SUMMARY Moment-tensor inversion of induced microseismic events can provide valuable information for tracking CO2 plumes at geological carbon storage sites, and study the physical mechanism of induced microseismicity. Accurate moment-tensor inversion requires a wide-azimuthal coverage of geophones. Cost-effective microseismic monitoring for geological carbon storage often uses only one geophone array within a borehole, leading to a large uncertainty in moment-tensor inversion. We develop a new adaptive moment-tensor joint inversion method to reduce the inversion uncertainty, when using limited but typical geophone receiver geometries. We first jointly invert a number of clustered microseismic events using a uniform focal mechanism to minimize the waveform misfit between observed and predicted P and S waveforms. We then invert the moment tensor for each event within a limited searching range around the joint inversion result. We apply our adaptive joint inversion method to microseismic data acquired using a single borehole geophone array at the CO2-Enhanced Oil Recovery field at Aneth, Utah. We demonstrate that our inversion method is capable of reducing the inversion uncertainty caused by the limited azimuthal coverage of geophones. Our inverted strikes of focal mechanisms of microseismic events are consistent with the event spatial distribution in subparallel pre-existing fractures or geological imperfections. The large values up to 40 per cent of the CLVD components might indicate crack opening induced by CO2/wastewater injection or rupture complexity.

High-angle or steeply-dipping faults, salt flanks, and subsalt sediments are important structures for many applications such as oil/gas and geothermal energy exploration. However, conventional seismic imaging has poor illuminations in these areas because of using singly scattered waves or primary reflections. By using high-order scattered waves in the reverse time migration (RTM), particularly the secondary scattered waves, we can enhance the seismic illumination for imaging the steep faults and subsalt areas. In our method, we first identify reflection features such as geological layer boundaries and discontinuities, based on the conventional RTM images. We then modify the migration model by adding potential point scatterers into the reflection features. These artificial point scatterers could be randomly placed on and around the reflection features to illuminate the structures where primaries cannot reach. The random placement of the point scatterers differentiates our method from previous methods using duplex or prism waves to image high-angle structures where true reflector locations are particularly identified. In addition, we assign the artificial point scatterers as density perturbations without modifying the velocity model so that they do not affect the spatial position of the imaged reflectors. The proposed method of high-order scattering imaging only requires one additional implementation of RTM but can substantially improve the image quality around high-angle faults and subsalt regions. We verify the effectiveness of our method using two synthetic models, a trapezoidal model and the Sigsbee2B model. And the results demonstrate that our new method produces better images for steep faults and subsalt areas compared with those obtained using conventional RTM.