3D Imaging of Geothermal Faults from a Vertical DAS Fiber at Brady Hot Spring, NV USA

In March 2016, arguably the most ambitious 4D (3D space + over time) active-source seismic survey for geothermal exploration in the U.S. was acquired at Brady Natural Laboratory, outside Fernley, Nevada. The four-week experiment included 191 vibroseis source locations, and approximately 130 m of distributed acoustic sensing (DAS) in a vertical well, located at the southern end of the survey area. The imaging of the geothermal faults is done with reverse time migration of the DAS data for both P-P and P-S events in order to generate 3D models of reflectivity, which can identify subsurface fault locations. Three scenarios of receiver data are explored to investigate the reliability of the reflectivity models obtained: (1) Migration of synthetic P-P and P-S DAS data, (2) migration of the observed field DAS data and (3) migration of pure random noise to better assess the validity of our results. The comparisons of the 3D reflectivity models from these three scenarios confirm that sections of three known faults at Brady produce reflected energy observed by the DAS. Two faults that are imaged are ~1 km away from the DAS well; one of these faults (middle west-dipping) is well-constructed for over 400 m along the fault’s strike, and 300 m in depth. These results confirm that the DAS data, together with an imaging engine such as reverse time migration, can be used to position important geothermal features such as faults.

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Variable-grid reverse-time migration of different seismic survey data

Applied Geophysics ◽

10.1007/s11770-017-0652-7 ◽

2017 ◽

Vol 14 (4) ◽

pp. 517-522 ◽

Cited By ~ 1

Author(s):

Xiao-Dong Sun ◽

Zhen-Chun Li ◽

Yan-Rui Jia

Keyword(s):

Survey Data ◽

Seismic Survey ◽

Reverse Time ◽

Reverse Time Migration ◽

Time Migration

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Imaging Complex Subsurface Structures for Geothermal Exploration at Pirouette Mountain and Eleven-Mile Canyon in Nevada

Frontiers in Earth Science ◽

10.3389/feart.2021.782901 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yunsong Huang ◽

Miao Zhang ◽

Kai Gao ◽

Andrew Sabin ◽

Lianjie Huang

Keyword(s):

High Resolution ◽

Least Squares ◽

Seismic Data ◽

Seismic Wave Propagation ◽

Reverse Time ◽

Reverse Time Migration ◽

Geothermal Exploration ◽

Subsurface Structures ◽

Time Migration ◽

Ground Roll

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.

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Receiver grouping strategies for hybrid Geometric-mean Reverse-time migration

Geophysics ◽

10.1190/geo2021-0428.1 ◽

2021 ◽

pp. 1-52

Author(s):

Tong Bai ◽

Bin Lyu ◽

Paul Williamson ◽

Nori Nakata

Keyword(s):

Time Reversal ◽

Cross Correlation ◽

Geometric Mean ◽

Random Noise ◽

Imaging Method ◽

Reverse Time ◽

Reverse Time Migration ◽

Image Domain ◽

Time Migration ◽

Grouping Strategies

Geometric-mean Reverse-time migration (GmRTM), a powerful cross-correlation-based imaging method, generates higher-resolution source images and is more robust to noise compared to conventional time-reversal imaging. The price to pay is the higher computational costs. Alternatively, we can adopt hybrid strategies by dividing the receivers into different groups. Conventional time reversal (i.e., wavefield summation) is performed inside each group, followed by the application of cross-correlation imaging condition among different groups. Such hybrid strategies can retain the advantages of both GmRTM and time-reversal, and are often more practical than pure GmRTM. Yet, designing appropriate grouping strategy is not trivial. Here, we propose two grouping strategies (adjacent and scattered) and use synthetic and field-data examples to evaluate their performance with various group numbers. In addition to the spatial resolution of the source image, robustness to random noise is another important assessment criterion, for which we consider two distribution patterns, such as concentrated and scattered, of traces contaminated with strong random noise. We also evaluated their effectiveness to visualize events (in the image domain) that are not completely recorded by all receivers. Our comprehensive tests illustrate the respective advantages of the two grouping strategies.

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Mapping subsurface karsts and voids using directional elastic wave packets

Geophysics ◽

10.1190/geo2021-0027.1 ◽

2021 ◽

Vol 86 (6) ◽

pp. S405-S416

Author(s):

Yinshuai Ding ◽

Hao Hu ◽

Adel Malallah ◽

Michael C. Fehler ◽

Lianjie Huang ◽

...

Keyword(s):

Wave Packet ◽

Elastic Wave ◽

Wave Packets ◽

Random Noise ◽

Velocity Model ◽

Imaging Method ◽

Reverse Time ◽

Reverse Time Migration ◽

Time Migration ◽

The Difference

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.

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Reverse time migration (RTM) imaging of iron oxide deposits in the Ludvika mining area, Sweden

Solid Earth ◽

10.5194/se-12-1707-2021 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1707-1718

Author(s):

Yinshuai Ding ◽

Alireza Malehmir

Keyword(s):

Iron Oxide ◽

Large Scale ◽

Random Noise ◽

Mining Area ◽

Mineral Deposits ◽

Reverse Time ◽

Reverse Time Migration ◽

Near Surface ◽

Time Migration ◽

Seismic Methods

Abstract. To discover or delineate mineral deposits and other geological features such as faults and lithological boundaries in their host rocks, seismic methods are preferred for imaging the targets at great depth. One major goal for seismic methods is to produce a reliable image of the reflectors underground given the typical discontinuous geology in crystalline environments with low signal-to-noise ratios. In this study, we investigate the usefulness of the reverse time migration (RTM) imaging algorithm in hardrock environments by applying it to a 2D dataset, which was acquired in the Ludvika mining area of central Sweden. We provide a how-to solution for applications of RTM in future and similar datasets. When using the RTM imaging technique properly, it is possible to obtain high-fidelity seismic images of the subsurface. Due to good amplitude preservation in the RTM image, the imaged reflectors provide indications to infer their geological origin. In order to obtain a reliable RTM image, we performed a detailed data pre-processing flow to deal with random noise, near-surface effects, and irregular receiver and source spacing, which can downgrade the final image if ignored. Exemplified with the Ludvika data, the resultant RTM image not only delineates the iron oxide deposits down to 1200 m depth as shown from previous studies, but also provides a better inferred ending of sheet-like mineralization. Additionally, the RTM image provides much-improved reflection of the dike and crosscutting features relative to the mineralized sheets when compared to the images produced by Kirchhoff migration in the previous studies. Two of the imaged crosscutting features are considered to be crucial when interpreting large-scale geological structures at the site and the likely disappearance of mineralization at depth. Using a field dataset acquired in hardrock environment, we demonstrate the usefulness of RTM imaging workflows for deep targeting mineral deposits.

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Least-squares extended reverse time migration with randomly sampled space shifts

Geophysics ◽

10.1190/geo2019-0536.1 ◽

2020 ◽

Vol 85 (6) ◽

pp. S357-S369 ◽

Cited By ~ 1

Author(s):

Jizhong Yang ◽

Yunyue Elita Li ◽

Yuzhu Liu ◽

Jingjing Zong

Keyword(s):

Least Squares ◽

Random Noise ◽

Computational Cost ◽

Extended Model ◽

Memory Requirement ◽

Reverse Time ◽

Reverse Time Migration ◽

Time Migration ◽

Implicit Model ◽

Geologic Interpretation

Because the velocity errors are inevitable in field data applications, direct implementation of conventional least-squares reverse time migration (LSRTM) would generate defocused migration images. Extending the model domain has the potential to preserve the data information, and reducing the extended model could provide a final image with more continuous subsurface structures for geologic interpretation. However, the computational cost and the memory requirement would be increased significantly compared to conventional LSRTM. To obtain an inversion image with better quality than conventional LSRTM, while maintaining the same computational cost and memory requirement, we have introduced random space shifts in LSRTM. The key point is to perform implicit model extension and immediate model reduction within each iteration of the inversion procedure. To be robust against the random noise during the random sampling process, we formulate the inverse problem based on a correlation objective function. Numerical examples on a simple layered model, the Marmousi model, and the SEAM model demonstrate that even when the bulk velocity errors are up to 10%, we still obtain reasonable results for subsurface geologic interpretation.

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Seismic wavefield reconstruction inversion using a plane-wave encoding strategy

Journal of Geophysics and Engineering ◽

10.1093/jge/gxaa062 ◽

2020 ◽

Vol 17 (6) ◽

pp. 1037-1048

Author(s):

Sumin Kim ◽

Wookeen Chung ◽

Young Seo Kim ◽

Changsoo Shin

Keyword(s):

Plane Wave ◽

Initial Velocity ◽

Random Noise ◽

Homogeneous Model ◽

Waveform Inversion ◽

Velocity Model ◽

Reverse Time ◽

Computationally Efficient ◽

Reverse Time Migration ◽

Time Migration

Abstract Wavefield reconstruction inversion (WRI) mitigates cycle skipping by using an inaccurate initial velocity. This attractive technique is usually implemented with shot records. However, if large numbers of shot records are used, WRI can become computationally burdensome due to the many over-determined linear systems that need to be solved. To alleviate this computational issue, we propose an efficient WRI scheme involving plane-wave encoding (WRI-PW) in the frequency domain. Plane-wave encoding can dramatically reduce the number of relevant datasets by transforming shot records into common ray-parameter gathers with time shifting. Therefore, plane-wave encoding is widely used in many aspects of seismic data processing (e.g. waveform inversion, reverse time migration, etc.). Initially, we performed a simple numerical experiment using a velocity model with a box-shaped anomaly. WRI-PW also could generate scattering wavefields in a homogeneous model. Next, computational efficiency was checked with a modified Marmousi-2 model. The results show that the usage of a sufficient plane-wave angle can achieve satisfactory inversion results. It indicates that WRI-PW requires small datasets compared to WRI. Thus, the computational costs for solving the augmented system can be reduced. Further experiments were conducted to evaluate the robustness of WRI-PW to random noise and to compare WRI-PW and conventional full waveform inversion (FWI) with a modified SEG/EAGE salt velocity model. We verify that WRI-PW is more robust to random noise than WRI, it exhibited less dependency on the accuracy of the initial velocity model than conventional FWI and it is computationally efficient.

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