WO2025002236A1 - Système de tomographie par adjoint différentiel à bruit ambiant - Google Patents

Système de tomographie par adjoint différentiel à bruit ambiant Download PDF

Info

Publication number
WO2025002236A1
WO2025002236A1 PCT/CN2024/101927 CN2024101927W WO2025002236A1 WO 2025002236 A1 WO2025002236 A1 WO 2025002236A1 CN 2024101927 W CN2024101927 W CN 2024101927W WO 2025002236 A1 WO2025002236 A1 WO 2025002236A1
Authority
WO
WIPO (PCT)
Prior art keywords
source
processor
seismic
differential
tomography
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2024/101927
Other languages
English (en)
Inventor
Xin Liu
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Hong Kong HKU
Original Assignee
University of Hong Kong HKU
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University of Hong Kong HKU filed Critical University of Hong Kong HKU
Priority to CN202480042239.7A priority Critical patent/CN121399502A/zh
Publication of WO2025002236A1 publication Critical patent/WO2025002236A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/303—Analysis for determining velocity profiles or travel times
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/282—Application of seismic models, synthetic seismograms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/284—Application of the shear wave component and/or several components of the seismic signal
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/10—Aspects of acoustic signal generation or detection
    • G01V2210/12—Signal generation
    • G01V2210/123—Passive source, e.g. microseismics
    • G01V2210/1236—Acoustic daylight, e.g. cultural noise
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/62—Physical property of subsurface
    • G01V2210/622—Velocity, density or impedance
    • G01V2210/6222—Velocity; travel time
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/67—Wave propagation modeling
    • G01V2210/679—Reverse-time modeling or coalescence modelling, i.e. starting from receivers

Definitions

  • the present invention generally relates to a tomography system. More specifically the present invention relates to an ambient noise differential adjoint tomography system.
  • an ambient noise differential adjoint tomography system comprising a plurality of seismic wave recording devices and a processor.
  • the seismic wave recording devices are disposed on a region.
  • the processor connects the seismic wave recording devices.
  • the processor indicates three of the seismic wave recording devices as a source, a first receiver, and a second receiver, and the first and second receivers are disposed nearby while each of them have different distance measuring from the source, and the source is disposed far away from the receivers, and the source and the receivers are linearly disposed.
  • the source and the receivers record ambient noise and generate ambient noise data.
  • the the processor compute a seismic noise interferometry by cross-correlating the ambient noise data from the source and the receivers to generate virtual earthquake signals.
  • FIG. 5 depicts a flow diagram of ambient noise differential adjoint tomography according to another embodiment of the present disclosure
  • FIG. 7 depicts a schematic view of a source, a plurality of noise sources, and a plurality of seismicwave recording devices, depicting geometry for the forward simulation: one virtual source and a subarray;
  • FIG. 8 depicts noise source intensity distribution versus azimuth for the synthetic noise interferometry data
  • FIG. 14 depicts total sensitivity for all 36 virtual sources at the 13th evaluation of the kernel, which uses the optimal velocity model that minimize the validation set;
  • FIG. 15 depicts training and validation misfit (loss) functions versus iteration; the optimal velocity parameter is reached at the 13th iteration, where the validation misfit is minimum;
  • FIG. 16A depicts topography along the LASSIE array with labels of fault locations (top) .
  • NIF Newport-Inglewood Fault
  • LAF Los Alamitos Fault
  • NF Norwalk Fault
  • LEPTF Lower Elysian Park Thrust Fault.
  • FIG. 16B depicts shear wave velocity based on differential adjoint tomography and low frequency data (top) . Velocity update for the top panel compared with initial ray theory model (bottom) ;
  • FIG. 19 depicts the lower bound of porosity converted from Vp/Vs ratio using BGTL method. At 0.8 km depth, the porosity is exact. For depth below 0.8 km, the displayed porosity value is a lower bound;
  • FIG. 20 is the Map of a linear ocean-bottom seismic station array off Kumano and shallow slow earthquakes (red &green dots) .
  • the southern Kumano Basin is between OBS74 and OBS79;
  • FIG. 21 is the S wave tomography result based on this invention.
  • the red color denotes slower S wave velocity, which corresponds to fluid-rich rocks below the sea floor.
  • the tomography is overlaid by seismic reflection profile from active-source data, which shows agreements between our invention method and the active-source method highlighting Kumano sedimentary basin.
  • noise interferometry results depend on both the noise sources and the structural properties they aim to investigate. Given the complex nature of the Earth's heterogeneous ambient seismic field and its temporal variations, the results are inevitably biased by the distribution and characteristics of the noise sources.
  • an ambient noise differential adjoint tomography system comprises a plurality of seismic wave recording devices and a processor.
  • the processor connects the seismic wave recording devices. While the seismic wave recording devices record the noise in a region, the processor indicates three of the seismic wave recording devices as a source and two receivers.
  • the source and the receivers are arranged as a linear array, and the receivers are positioned nearby each other.
  • the processor generates seismic noise at the sources and records at the receivers, and obtaining a noise interferometry, and generating synthetic dispersive surface wave signals, i.e. virtual earthquake signals.
  • the relationship between virtual earthquake signals and noise interferometry is based on the concept of Green's function retrieval.
  • cross-correlating the ambient noise recordings between two seismic stations can yield the Green's function, which represents the impulse response of the Earth between these two stations.
  • the resulting cross-correlation function can be interpreted as a virtual earthquake signal, as if one of the stations acts as a virtual source and the other as a receiver.
  • This virtual earthquake signal contains information about the seismic wave propagation between the two stations, including the effects of the Earth's structure along the path.
  • researchers can extract information about the subsurface structure and construct tomographic images, such as those obtained through ambient noise differential adjoint tomography.
  • the processor generates a differential adjoint tomography based on observed dispersive surface wave from the noise interferometry.
  • the preocessore estimates a shear wave velocity from the differential adjoint tomography, and the processor use the estimated tomography to estimate the characteristic of the region, providing a diagram with these information through a display or printer.
  • FIG. 1 is a schematic view of the ambient noise differential adjoint tomography system according to the embodiment.
  • the system 1 uses differential time measurements of a pair of stations within a linear triplet to obtain results that are more sensitive to the structural properties of the medium of the region.
  • the system 1 comprises seismic wave recording devices including the seismic wave recording devices 101, 102, 103, which form the linear triplet, and these devices 101, 102, 103 and a processor 100 is connected.
  • the processor 100 indicates the device 101 as a source; the device 102 as a receiver; and the device 103 as another receiver.
  • the processor 100 by cross correlating the ambient noise recordings, which can be generated from any of the noise sources 50, between the source 101 and the stations 102, 103, the processor 100 creates two virtual earthquake signals, as if the source 101 is the earthquake source while the two stations 102, 103, are the receivers of the earthquakes.
  • FIG. 1 diagrams of the two virtual earthquake signals are shown.
  • ⁇ T time delay
  • FIG. 2 is another schematic view of the ambient noise differential adjoint tomography system on the land according to another embodiment.
  • the land acquisition geometry is shown. Any station (triangle) can be picked as the virtual source. These stations are connected by a processor as well, which is not shown in this figure, and the processor indicates the source 101, the receiver 102, and the receiver 103.
  • the system cross correlate the ambient noise recordings on the source 101 with that of the receivers 102, 103, generating virtual earthquake signals V1, V2 from the source 101 to the receivers 102, 103.
  • the system can perform unbiased ambient noise differential adjoint tomography and derived the S wave tomography image for the subsurface structure underneath the seismic wave recording devices.
  • Swave indicates the secondary waves, which is the shear wave mocing particles perpendicular to the direction of wave travel.
  • the seismic wave recorded by the system of the embodiments of the present invention comprises at least the S wave and the P wave, i.e. primary wave.
  • S waves also known as shear waves or secondary waves
  • S waves are a type of elastic wave that propagates through the Earth's interior.
  • P waves primary waves
  • S waves are transverse waves that can only travel through solid materials.
  • the particle motion of S waves is perpendicular to the direction of wave propagation.
  • S waves travel slower than P waves and cannot pass through liquid or gas because these materials do not support shear stress.
  • seismology the velocity of S waves provides valuable information about the Earth's interior structure, particularly in identifying and characterizing fluid-bearing rocks, as fluids significantly slow down S wave propagation.
  • FIG. 3 is another schematic view of the ambient noise differential adjoint tomography system on the marine region according to another embodiment.
  • the marine acquisition geometry is shown.
  • the stations, or ocean-bottom seismometers (OBS) or ocean-bottom cables (OBC) are deployed on the sea floor recording seismic noise. Any ocean-bottom seismic station can be picked as the source 101.
  • Any ocean-bottom seismic station can be picked as the source 101.
  • the system comprises a linear ocean-bottom seismometer (OBS) array across the Nankai Trough off the Kii Peninsula, Japan.
  • OBS ocean-bottom seismometer
  • FIG. 5 is another flowchart showing detailed workflow of ambient noise differential adjoint tomography according to another embodiment.
  • the processor of the system of this embodiment measure a observed differential time with dispersion from the noise interferometry.
  • the observed differential time data from noise interfereometry is compared with the synthetic differential time data generated by simulated earthquake waveform data using a computer software by numerically solving the elastic seismic wave equation.
  • the loop in FIG. 5 means iterative updates of the S wave velocity model below the seismic array. After the discrepancy between the observed and simulated differential time data is minimized, the tomography result is the final S wave velocity model.
  • the distance between stations 101 and 103 is slightly longer than the distance between stations 101 and 102, ensuring that the paths from the virtual source to the two receivers have significant overlap.
  • the sensitivity to the noise source distribution within the overlapping region is effectively canceled out. This allows the creation of a misfit function that is more sensitive to the structural properties of the Earth between receivers 102 and 103.
  • Differential time sensitivity kernels of noise interferometry functions are effective when the heterogeneous noise source distribution is unknown.
  • the point source kernel is equivalent to the noise interferometry kernel for uniform noise sources, allowing simplifications by replacing the virtual source with a point source while still using differential time measurements. This extends ambient noise differential time kernels to linear arrays lacking 2D seismic wave recording devices coverage.
  • differential time sensitivity kernels describe how sensitive these differential time measurements are to changes in the Earth's properties.
  • the differential time sensitivity kernel for noise interferometry (where virtual earthquake sources are created by cross-correlating ambient noise recorded at different stations) is similar to the sensitivity kernel for a case where there is a single point source generating seismic waves, if the noise source distribution is uniform. This means that even if the actual noise source distribution is not uniform, we can still use the simpler point source kernel in our calculations, as long as we use differential time measurements.
  • FIG. 7 depicts a schematic view of a source, a plurality of noise sources, and a plurality of stations, i.e. seismic wave recording devices.
  • the setup for the forward simulation includes a virtual source and a subarray of 8x8 stations, surrounded by a ring of noise sources. Within the subarray, there is a lamda-shaped low-velocity structure.
  • the velocity anomaly structure is estimated with two approaches:
  • the processor computes the gradient of the misfit function using the adjoint method, which involves simulating the forward and backward propagation of seismic waves. We would like to explain the detail of the approach of the embodiment.
  • the following equations (3) and (4) define how to compute inner product between the forward and backward (adjoint) wavefields in order to estimate sensitivity kernel, which is the gradient of data misfit function with respect to the specific elastic modulus (e.g. shear modulus in the examples) .
  • the forward wavefield is just created by a point source with a simple source wavelet covering the frequency range of interest from a seismic survey.
  • SH waves e.g., Love waves
  • u (x) For SH waves propagating in a 2D cross-section along a line, the above vector wavefield u (x) reduces to displacement in crossline direction, uy.
  • the medium is isotropic, the differential sensitivity kernel is considered for shear modulus ⁇ ,
  • ⁇ t ij ( ⁇ ) are, respectively, observed and synthetic differential time measurements between station pairs 1-2 and 1-3 (i ⁇ station 2 ; j ⁇ station 3) .
  • W ( ⁇ ) is a weighting function and ⁇ is angular frequency.
  • Eq. (S8) contains the complex conjugate phase factor of the synthetic waveform. Therefore, multi-taper analysis does not be applied for the synthetic waveform.
  • An appropriate bandpass filter is used to remove the large amplitude in the transition band.
  • T-t means time reversal of the right-hand side.
  • Eq. (S9) The backward (adjoint) wavefield is created by Eq. (S9) , which defines the differential adjoint sources for differential time measurements on dispersive surface waves.
  • Eq.(S8) defines some terms in Eq. (S9) .
  • the differential time structure kernel for one triplet is primarily sensitive to the velocity structure between the two nearby receivers.
  • the station triplet containing the receiver pair shows strong sensitivity between the two nearby receivers where differential time data are measured (FIG. 11A) .
  • the combined sensitivity kernel for the virtual source N101 shows positive shear velocity sensitivity from near surface to ⁇ 3.5 km depth, suggesting that the actual shear velocity is slower than in the starting model. Moreover, the side lobes are significantly reduced due to the overlapping of multiple station triplets.
  • Ambient noise differential adjoint tomography is introduced to a linear array across the Los Angeles Basin (FIG. 12) .
  • the inverted shear-wave velocity model based on differential time measurements of Love wave phases reveals low-velocity zones (up to 50%reduction compared with the ray theory velocity model) corresponding to groundwater reservoirs from 200 m to 2 km depth that some deeper ones were not previously identified due to the limitation of borehole depth.
  • the inverted shear-wave velocity model in the top 100 m shows systematic agreements with the Vs 30 information from the independently derived geotechnical layer. Together, these results demonstrate the resolving power of the new differential adjoint tomography system compared with traditional ray theory tomography.
  • Ambient noise differential time adjoint tomography is applied to two different frequency bands: 1) low-frequency band 0.15-0.35 Hz, and 2) low+high-frequency band 0.15-1.5 Hz.
  • the sensitivity kernel of the 36 virtual sources shows strong positive sensitivity to low-velocity structures at shallow depths: up to 3.5 km SW of the Newport-Inglewood Fault and 2 km depth between the Newport-Inglewood Fault and Lower Elysian Park Thrust Fault.
  • the sensitivity values are much smaller, and the positive and negative values may relate to residual data misfit near fault traces.
  • the total misfit decreases (FIG. 15) , with optimization converging around the 14th iteration.
  • validation using the 5 additional stations suggests that the minimum validation misfit occurs at the 13th iteration, and the 14th iteration increases the validation misfit, indicating overfitting.
  • the inverted shear velocity model at the 13th iteration is chosen as the final result for the long period data.
  • the low-frequency result shows significant velocity reduction, with up to 50%velocity decrease at 300m depth and 23%decrease at 2km depth southwest of the Newport-Inglewood Fault.
  • the peak velocity reduction values for different shallow zones appear at ⁇ 300 m depth, suggesting sensitivity to layered structures at this depth.
  • the final shear velocity model is obtained with an enhanced image at shallow depths of 0-800 m.
  • the final velocity model shows only a marginal difference from the velocity model based on low-frequency Love wave data, suggesting that the low-frequency data are also sensitive to the shallow structure in the top 800 m in addition to the deeper structure between 800-3000 m depth.
  • the Vp/Vs ratio is firstly estimated based on the adjoint shear velocity tomography result.
  • the P wave velocity model is derived from the Long Beach nodal array using ambient seismic noise interferometry.
  • the Vp/Vs ratios are computed for the top 2 km of the sedimentary basin structure centered on the Newport-Inglewood Fault (FIG. 17 top) .
  • Such large values are not unheard of, for example, similar and higher Vp/Vs ratios are found at Groningen, Netherlands in the top 0.2 km depth based on borehole seismic arrays.
  • Previous surface wave tomography with long-period data using the regional broadband seismic network did not resolve the shallow layers in Los Angeles Basin where a high Vp/Vs ratio is found.
  • the estimated porosity at 0.8 km depth shows strong fluctuation between 0.25-0.38 (FIG. 17 bottom) .
  • the porosity is 0.31 at 0.8 km depth, which is a local minimum.
  • the Vp/Vs ratio peaks at the same horizontal location between 0.2-0.4 km depth, indicating greater water saturation compared with other horizontal locations.
  • the two blocks separated by the Newport Inglewood Fault show significant contrast in Vp/Vs ratio and porosity (FIG. 17) , suggesting different materials for sedimentary layers across the fault interface.
  • the sediments above 0.6 km depth in the NE block show lower Vp/Vs ratios than those from the SW block at the same depth range, indicating more pore fluids in the SW block at these shallow depths.
  • the NE block shows higher Vp/Vs ratios within 2 km from the fault interface than the SW block, suggesting higher porosity for the NE block at greater depths near the fault interface.
  • This observation is clarified by converting the Vp/Vs ratio beneath 0.8 km depth to a lower-bound on porosity (FIG. 19) , which shows the deep reservoirs have porosity around 0.33.
  • the porosity information is directly related to Vp/Vs ratios, but the P-wave velocity model is only available in the top 2 km and spans 10 km away from the Newport-Inglewood Fault due to the limitation of the Long Beach array 15 . Therefore, our study of porosity is limited to the smaller volume where both Vp and Vs models overlap. Another limitation is that the BGTL method only works for a depth range where the differential pressure is ⁇ 15 MPa.
  • the P-wave velocity beneath Long Beach contains much less lateral variation than the S-wave velocity. Therefore, most of the observed lateral variations in Vp/Vs ratio arise from the S-wave velocity model, which is more sensitive to pore fluids in sedimentary rocks than the P-wave model.
  • the first limitation can be alleviated by comparing the lateral variation of shear velocity update after adjoint tomography (e.g. FIG. 16B &C) , as the shear velocity reduction indicates larger Vp/Vs ratio and therefore higher porosity.
  • the second limitation can be reduced by estimating the lower bound of porosity for deeper depths with higher differential pressure (e.g. FIG. 19) .
  • the estimated shallow shear-wave velocity structure is compared with the Vs 30 data from the geotechnical layer beneath the LASSIE array (FIG. 18) .
  • the inverted shear-wave velocity is derived from the top 100 m due to the limitation of the grid size in adjoint tomography.
  • the geotechnical layer was sparsely sampled (by strong motion stations) and Vs 30 values are grouped by different geological units.
  • Vs 100 The inverted shear wave velocity in top 100 m (Vs 100) from adjoint tomography is considerably lower than that from ray theory tomography (FIG. 18) .
  • the trend of Vs 100 generally increases as the distance from the coast increases.
  • the Vs 100 SW of the Newport-Inglewood Fault is the lowest, which is close the Vs 30 values from the geotechnical layer.
  • Vs 100 is higher while the Vs 30 shows decreasing velocity.
  • Vs 100 For the geological unit between the Los Alamitos Fault and the Lower Elysian Park Thrust Fault, both the inverted Vs 100 (adjoint tomography) and the Vs 30 decrease at the edges of the unit. Vs 30 remains constant within the geological unit –perhaps due to sparse sampling, while Vs 100 exhibits lateral variations within the unit and is almost twice the value of Vs 30.
  • the high topography above the Whittier Fault corresponds to higher Vs 100 and Vs 30, while the inverted Vs 100 is ⁇ 0.15 km/sfaster than Vs 30.
  • the inverted Vs 100 from adjoint tomography matches the trend of the ray theory Vs 100, but is slightly lower.
  • Ambient noise differential adjoint tomography shows great potential for resolving detailed geological/hydrological structures in urban sedimentary basins. It reduces the bias caused by uneven and temporally variable noise source distribution.
  • 5 stations are used as virtual sources for a validation dataset, which is independent from the training dataset consisting of 36 virtual sources. By minimizing the validation error, the optimal velocity model is chosen that do not overfit to the training dataset. This approach is widely used in machine learning and could be useful for adjoint tomography community. It is similar to cross validation, but the multiple different combinations of training and validation sets required for true cross validation are not feasible due to the computational demands of adjoint tomography.
  • the Vp and Vs models are derived from different seismic arrays with different approaches.
  • the Vp model is based on P-wave phases extracted from the noise interferometry data of the 2D Long Beach nodal array through ray tracing of refracted P wave ray paths.
  • the Vs model derived here is from the ambient noise differential adjoint tomography based on wave equations and a linear array. Therefore, it’s possible that some unresolved P-wave velocity variations may be missing in the Vp/Vs ratio (FIG. 19) . Due to the density of Long Beach nodal array and the proximity to the coast, however, the unresolved low-Vp anomaly at the shallow depths (top 1 km) is unlikely to be as significant as the Vs anomaly.
  • system of an embodiment of the present invention method is applied to a marine acquisition for a linear OBS array off Kumano, Kii peninsula, Japan (FIG. 20) .
  • the resulting S-wave velocity model (FIG. 21) reveals significantly low S-wave velocity in the southeastern part of the Kumano Basin and the incoming sediments above the subducting oceanic crust of the Philippine Sea Plate. Specifically, in the southeastern portion of the Kumano Basin beneath the linear array (for X between 65-85 km) , the S-wave velocities are low, ranging between 0.2-0.5 km/sin the top 3 km depth, forming a distinctive bowl-shaped structure. For comparison, this bowl-shaped structure corresponds to approximately 40-60%velocity reduction with respect to the surrounding rocks.
  • the southern Kumano Basin is interpreted as a fluid-rich sedimentary basin compared with the northern Kumano Basin, which agrees with the spatial distribution of slow earthquakes between OBS74 and OBS79 (FIG. 20) that requires abundant fluids at the subducting plate interface.
  • the fluids at plate interface are believed to be the source of fluids in southern Kumano Basin.
  • the main function includes:
  • underground geophysical/hydrological properties e.g. underground fluid reservoirs can be located.
  • the spacing between receivers should be small, i.e. the distance between source and the receiver positioned farther should be less than 125%of the distance between source and receiver positioned closer.
  • the angle between straight lines connecting the source and every receiver should be less than 5 degrees.
  • the processer use the differential time measurements for the noise interferometric function to create a misfit function for dispersive surface wave signals.
  • the processor use elastic wave equation to compute gradient of differential-time misfit function.
  • the differential-time kernels are similar to those before replacement.
  • the processor of the system greatly simplifies the calculations through this approach, and the linear array formed by the seismic wave recording devices can provide proper ambient noise differential adjoint tomography.
  • the processor of the system uses differential travel time to compute kernels, and the processor reduces sensitivity to noise sources with this approach.
  • the processor of the system defines the differential-time adjoint sources for ambient noise differential adjoint tomography.
  • the processor of the system uses differential time misfit function.
  • the processor of the system executive the following steps:
  • a) Compute seismic noise interferometry for two station pairs: 1-2.1-3, where 1 denotes virtual source. 2 and 3 are receivers.
  • the noise interferometries includes noise interferometric functions.
  • the differential adjoint sources of dispersive surface wave signals are defined (Eq. S7, S8, S9) .
  • some technique for spectral density estimation e.g. Welch's method, Multitaper
  • Eq. S9 Spectral density estimation
  • Gradient is the spatial derivative of the misfit function with respect to the parameter (e.g. shear wave velocity) .
  • the functional units and modules of an ambient noise differential adjoint tomography system in accordance with the embodiments disclosed herein may be implemented using computing devices, computer processors, or electronic circuitries including but not limited to application specific integrated circuits (ASIC) , field programmable gate arrays (FPGA) , microcontrollers, and other programmable logic devices configured or programmed according to the teachings of the present disclosure.
  • ASIC application specific integrated circuits
  • FPGA field programmable gate arrays
  • microcontrollers and other programmable logic devices configured or programmed according to the teachings of the present disclosure.
  • Computer instructions or software codes running in the computing devices, computer processors, or programmable logic devices can readily be prepared by practitioners skilled in the software or electronic art based on the teachings of the present disclosure.
  • All or portions of the methods in accordance to the embodiments may be executed in one or more computing devices including server computers, personal computers, laptop computers, mobile computing devices such as smartphones and tablet computers.
  • the embodiments may include computer storage media, transient and non-transient memory devices having computer instructions or software codes stored therein, which can be used to program or configure the computing devices, computer processors, or electronic circuitries to perform any of the processes of the present invention.
  • the storage media, transient and non-transient memory devices can include, but are not limited to, floppy disks, optical discs, Blu-ray Disc, DVD, CD-ROMs, and magneto-optical disks, ROMs, RAMs, flash memory devices, or any type of media or devices suitable for storing instructions, codes, and/or data.
  • Each of the functional units and modules in accordance with various embodiments also may be implemented in distributed computing environments and/or Cloud computing environments, wherein the whole or portions of machine instructions are executed in distributed fashion by one or more processing devices interconnected by a communication network, such as an intranet, Wide Area Network (WAN) , Local Area Network (LAN) , the Internet, and other forms of data transmission medium.
  • a communication network such as an intranet, Wide Area Network (WAN) , Local Area Network (LAN) , the Internet, and other forms of data transmission medium.

Landscapes

  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Acoustics & Sound (AREA)
  • Environmental & Geological Engineering (AREA)
  • Geology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Geophysics (AREA)
  • Geophysics And Detection Of Objects (AREA)

Abstract

L'invention concerne un système de tomographie par adjoint différentiel à bruit ambiant (l). Le système (l) comprend des dispositifs d'enregistrement d'ondes sismiques (101, 102, 103) et un processeur (100). Le processeur (100) indique trois des dispositifs d'enregistrement d'ondes sismiques (101, 102, 103) en tant que source (lOl), un premier récepteur (102), et un second récepteur (103), et la source (lOl) et les premier et second récepteurs (102, 103) sont disposés sous la forme d'un réseau linéaire, et les premier et second récepteurs (102, 103) sont positionnés à proximité l'un de l'autre. Le processeur (100) reçoit un bruit sismique et obtient une interférométrie de bruit, calcule des signaux d'onde de surface dispersive synthétique, génère une tomographie par adjoint différentiel sur la base d'une onde de surface dispersive observée à partir de l'interférométrie de bruit. Le processeur (100) estime une vitesse d'onde de cisaillement à partir de la tomographie par adjoint différentiel, à l'aide de la tomographie estimée pour estimer la caractéristique de la région, et le processeur (100) fournit un diagramme avec une caractéristique de la région par l'intermédiaire d'un dispositif d'affichage ou d'une imprimante.
PCT/CN2024/101927 2023-06-27 2024-06-27 Système de tomographie par adjoint différentiel à bruit ambiant Ceased WO2025002236A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202480042239.7A CN121399502A (zh) 2023-06-27 2024-06-27 背景噪声差分伴随层析成像系统

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202363510624P 2023-06-27 2023-06-27
US63/510,624 2023-06-27

Publications (1)

Publication Number Publication Date
WO2025002236A1 true WO2025002236A1 (fr) 2025-01-02

Family

ID=93937702

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2024/101927 Ceased WO2025002236A1 (fr) 2023-06-27 2024-06-27 Système de tomographie par adjoint différentiel à bruit ambiant

Country Status (2)

Country Link
CN (1) CN121399502A (fr)
WO (1) WO2025002236A1 (fr)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6424920B1 (en) * 1999-09-17 2002-07-23 Konstantin Sergeevich Osypov Differential delay-time refraction tomography
US20100054083A1 (en) * 2008-09-03 2010-03-04 Christof Stork Measuring and modifying directionality of seismic interferometry data
US20170307772A1 (en) * 2016-04-20 2017-10-26 Magnitude Microseismic JV Formation measurements using downhole noise sources
CN110579795A (zh) * 2018-06-08 2019-12-17 中国海洋大学 基于被动源地震波形及其逆时成像的联合速度反演方法
CN113504566A (zh) * 2021-06-01 2021-10-15 南方海洋科学与工程广东省实验室(湛江) 基于波动方程的地震反演方法、系统、装置及介质
CN115184986A (zh) * 2022-06-28 2022-10-14 吉林大学 不依赖震源的全局包络互相关全波形反演方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6424920B1 (en) * 1999-09-17 2002-07-23 Konstantin Sergeevich Osypov Differential delay-time refraction tomography
US20100054083A1 (en) * 2008-09-03 2010-03-04 Christof Stork Measuring and modifying directionality of seismic interferometry data
US20170307772A1 (en) * 2016-04-20 2017-10-26 Magnitude Microseismic JV Formation measurements using downhole noise sources
CN110579795A (zh) * 2018-06-08 2019-12-17 中国海洋大学 基于被动源地震波形及其逆时成像的联合速度反演方法
CN113504566A (zh) * 2021-06-01 2021-10-15 南方海洋科学与工程广东省实验室(湛江) 基于波动方程的地震反演方法、系统、装置及介质
CN115184986A (zh) * 2022-06-28 2022-10-14 吉林大学 不依赖震源的全局包络互相关全波形反演方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LIU XIN, BEROZA GREGORY C., LI HONGYI: "Ambient noise differential adjoint tomography reveals fluid-bearing rocks near active faults in Los Angeles", NATURE COMMUNICATIONS, NATURE PUBLISHING GROUP, UK, vol. 14, no. 1, UK, XP093253415, ISSN: 2041-1723, DOI: 10.1038/s41467-023-42536-4 *

Also Published As

Publication number Publication date
CN121399502A (zh) 2026-01-23

Similar Documents

Publication Publication Date Title
Zelt Modelling strategies and model assessment for wide-angle seismic traveltime data
Süss et al. P wave seismic velocity structure derived from sonic logs and industry reflection data in the Los Angeles basin, California
Minson et al. Bayesian inversion for finite fault earthquake source models–II: the 2011 great Tohoku-oki, Japan earthquake
Robinson et al. Geophysical signal analysis
EP3710867B1 (fr) Atténuation du bruit de données sismiques à sources multiples
Steiner et al. Time reverse modeling of low‐frequency microtremors: Application to hydrocarbon reservoir localization
Özalaybey et al. Investigation of 3-D basin structures in the Izmit Bay area (Turkey) by single-station microtremor and gravimetric methods
Morgan et al. Next-generation seismic experiments: wide-angle, multi-azimuth, three-dimensional, full-waveform inversion
Nakanishi et al. Detailed structural image around splay‐fault branching in the Nankai subduction seismogenic zone: Results from a high‐density ocean bottom seismic survey
US7952960B2 (en) Seismic imaging with natural Green's functions derived from VSP data
CA2940406C (fr) Caracterisation d'une structure physique a l'aide d'un modele de bruit multidimensionnel pour attenuer des donnees bruitees
Garambois et al. Analysis of ballistic waves in seismic noise monitoring of water table variations in a water field site: added value from numerical modelling to data understanding
EP2548052B1 (fr) Système et procédé d'imagerie vsp de flancs de sel 3d avec des ondes transmises
CN101663597A (zh) 根据2-d地震数据进行3-d散射体成像
Wang et al. Near-surface site characterization based on joint iterative analysis of first-arrival and surface-wave data
Provenzano et al. Pre-stack full waveform inversion of ultra-high-frequency marine seismic reflection data
Nakata et al. Imaging a crustal low‐velocity layer using reflected seismic waves from the 2014 earthquake swarm at Long Valley Caldera, California: The magmatic system roof?
Ford et al. Seismic diffraction imaging to characterize Mass‐transport complexes: Examples from the Gulf of Cadiz, South west Iberian Margin
Aminzadeh et al. Fundamentals of petroleum geophysics
Talukdar et al. Sub-basalt imaging of hydrocarbon-bearing Mesozoic sediments using ray-trace inversion of first-arrival seismic data and elastic finite-difference full-wave modeling along Sinor–Valod profile of Deccan Syneclise, India
Lin et al. Source‐independent passive seismic reverse‐time structure imaging with grouping imaging condition: Method and application to microseismic events induced by hydraulic fracturing
Zhao et al. Source‐receiver interferometric redatuming using sparse buried receivers to address complex near‐surface environments: A case study of seismic imaging quality and time‐lapse repeatability
Sadler et al. Modeling crustal structure in the Permian basin by waveform‐matching p receiver functions and autocorrelograms with particle swarm optimization
Jimenez-Tejero et al. Downward continuation of wide-angle seismic data: implications for traveltime tomography uncertainty
WO2025002236A1 (fr) Système de tomographie par adjoint différentiel à bruit ambiant

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 24830869

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE