EP4662515A1 - Methods and computing systems for attenuating shear noise - Google Patents

Abstract

A method for attenuating shear noise in seismic data including receiving seismic data. The seismic data includes multi-component seismic data including a pressure component and a vertical component. The method also includes mapping the pressure component and the vertical component to generate mapped seismic data. The method also includes determining a correlation between the pressure component and the vertical component based upon the mapped seismic data. The method also includes attenuating shear noise in the seismic data to generate attenuated seismic data, the shear noise is attenuated based upon the correlation.

Description

METHODS AND COMPUTING SYSTEMS FOR ATTENUATING SHEAR NOISE

Cross-Reference to Related Applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63/489,496, filed on March 10, 2023, which is incorporated by reference.

Background

[0002] Seismic surveying generally includes the process of recording reflected seismic waves from beneath the subsurface to model geological structures and physical properties of the earth. For instance, the aim of a seismic survey may be to depict the physical properties of a reservoir. Many techniques have been used for processing the collected survey data, yet the processing of such data to form reliable and accurate images of the subsurface is often difficult. Accordingly, there is a need for methods and computing systems that can employ more effective and accurate methods for identifying, isolating, transforming, and/or processing various aspects of seismic signals or other data that is collected from a subsurface region or other multi-dimensional space.

Summary

[0003] A method for attenuating shear noise in seismic data is disclosed. The method includes receiving seismic data. The seismic data includes multi-component seismic data including a pressure component and a vertical component. The method also includes mapping the pressure component and the vertical component to generate mapped seismic data. The method also includes determining a correlation between the pressure component and the vertical component based upon the mapped seismic data. The method also includes attenuating shear noise in the seismic data to generate attenuated seismic data, wherein the shear noise is attenuated based upon the correlation.

[0004] A computing system is also disclosed. The computing system includes one or more processors and a memory system. The memory system includes one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations include receiving seismic data. The seismic data includes multi-component seismic data including a pressure component and a vertical component. The vertical component includes a vertical particle displacement and its time derivatives. The operations also include interpolating the seismic data to generate interpolated seismic data. The interpolated seismic data includes an interpolated pressure component and an interpolated vertical component. The operations also include mapping the interpolated pressure component and the interpolated vertical component to generate mapped seismic data. The interpolated pressure component and the interpolated vertical component are mapped in a multi-dimensional sparsity-promoting transform domain. The interpolated pressure component and the interpolated vertical component are mapped to identify different frequency -related data and different dip-related data. The operations also include determining a correlation between the interpolated pressure component and the interpolated vertical component based upon the mapped seismic data. The correlation is determined in the multi-dimensional sparsity-promoting transform domain. The correlation is based upon the different frequency-related data and the different dip-related data. The operations also include attenuating shear noise in the seismic data to generate attenuated seismic data. The shear noise is attenuated based upon the correlation.

[0005] A computer-readable medium is also disclosed including, but not limited to, a non- transitory computer-readable medium. The medium stores instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations include receiving seismic data. The seismic data includes multicomponent seismic data including a pressure component and a vertical component. The vertical component includes a vertical particle displacement and its time derivatives. The seismic data is received from an ocean-bottom node. The seismic data is acquired by the ocean-bottom node using sequential or simultaneous shooting. The operations also include interpolating the seismic data to generate interpolated seismic data. The interpolated seismic data includes an interpolated pressure component and an interpolated vertical component. The seismic data is interpolated at a regular grid. The operations also include mapping the interpolated pressure component and the interpolated vertical component to generate mapped seismic data. The interpolated pressure component and the interpolated vertical component are mapped in a multidimensional sparsity-promoting transform domain. The interpolated pressure component and the interpolated vertical component are mapped to identify different frequency-related data and different dip-related data. The operations also include determining a correlation between the interpolated pressure component and the interpolated vertical component based upon the mapped seismic data. The correlation is determined in the multi-dimensional sparsitypromoting transform domain. The correlation is based upon the different frequency-related data and the different dip-related data. The operations also include attenuating shear noise in the seismic data to generate attenuated seismic data. The shear noise is attenuated based upon the correlation. The shear noise contaminates the vertical particle displacement and/or its time derivatives of the vertical component in the seismic data. Attenuating the shear noise includes matching a first amplitude of the vertical component to a second amplitude of the pressure component. The first amplitude includes a pixel-wise amplitude of complex coefficients of the vertical component. The second amplitude includes a pixel-wise amplitude of complex coefficients of the pressure component. A phase of the vertical component is preserved during the matching. Attenuating the shear noise also includes determining a root-mean-square (RMS) value of the matched first and second amplitudes. The RMS value is determined using a localized sliding window in a temporal-spatial domain. The RMS value is determined based upon a temporal-spatial continuity of the shear noise in the multi-dimensional sparsitypromoting transform domain. Attenuating the shear noise also includes estimating the shear noise in the vertical component based upon the RMS value. The shear noise is estimated in the vertical particle displacement and/or its time derivatives of the vertical component. Attenuating the shear noise also includes determining a first order noise and signal statistic and a second order noise and signal statistic based upon the estimated shear noise. The first order noise and signal statistic includes a mean, and the second order noise and signal statistic includes a variance. Attenuating the shear noise also includes generating or updating an optimal shear noise model based upon the pressure component, the vertical component, the estimated shear noise, and the first and second order noise and signal statistics. The optimal shear noise model is an optimal Bayesian weighted noise model. Attenuating the shear noise also includes attenuating the shear noise in the multi-component seismic data to generate the attenuated

seismic data using the optimal shear noise model. The operations also include displaying the attenuated seismic data.

[0006] It will be appreciated that this summary is intended merely to introduce some aspects of the present methods, systems, and media, which are more fully described and/or claimed below. Accordingly, this summary is not intended to be limiting.

Brief Description of the Drawings

[0007] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:

[0008] Figure 1 depicts an example computing system, in accordance with some embodiments.

[0009] Figure 2 illustrates a survey operation being performed by a survey tool, such as a seismic truck, to measure properties of the subterranean formation, in accordance with some embodiments.

[0010] Figure 3 illustrates a drilling operation being performed by drilling tools suspended by rig and advanced into subterranean formations to form wellbore, in accordance with some embodiments.

[0011] Figure 4 illustrates a wireline operation being performed by a wireline tool suspended by the rig and into the wellbore of Figure 3, in accordance with some embodiments.

[0012] Figure 5 illustrates a production operation being performed by a production tool deployed from a production unit or a Christmas tree and into the completed wellbore for

drawing fluid from the downhole reservoirs into surface facilities, in accordance with some embodiments.

[0013] Figure 6 illustrates a schematic view, partially in cross section, of an oilfield having data acquisition tools positioned at various locations along the oilfield for collecting data of the subterranean formation, in accordance with some embodiments.

[0014] Figure 7 illustrates an oilfield for performing production operations, in accordance with some embodiments.

[0015] Figure 8 illustrates a side view of a marine-based survey of a subterranean subsurface, in accordance with some embodiments.

[0016] Figure 9 illustrates a marine electromagnetic survey system, in accordance with some embodiments.

[0017] Figure 10A illustrates a 2D subsection extracted from a common receiver gather for the pressure component, and Figure 10B illustrates a 2D subsection extracted from a common receiver gather for the vertical (e.g., vertical particle velocity) component, according to an embodiment.

[0018] Figures 11A-11C illustrate images showing the impact of adaptive localized filtering on preserving the weak coherent signal buried beneath the shear noise when performing thresholding based on envelope ratio scaling, according to an embodiment.

[0019] Figure 12 illustrates a flowchart of a method for attenuating shear noise in seismic data (e.g., using Bayesian summation), according to an embodiment.

Detailed Description

[0020] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the disclosure. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0021] It will also be understood that, although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the disclosure. The first object or step, and the second object or step, are both objects or steps, respectively, but they are not to be considered the same object or step.

[0022] The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting of the invention. As used in the description of the invention and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and/or" as used herein refers to and encompasses any possible combination of one or more of the associated listed items. It will be further understood that the terms "includes," "including," "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

[0023] As used herein, the term "if¹ may be construed to mean "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context.

[0024] Computing Systems

[0025] Figure 1 depicts an example computing system 100 in accordance with some embodiments. The computing system 100 can be an individual computer system 101 A or an arrangement of distributed computer systems. The computer system 101 A includes one or more geosciences analysis modules 102 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the geosciences analysis module 102 executes independently, or in coordination with, one or more processors 104, which is (or are) connected to one or more storage media 106. The processor(s) 104 is (or are) also connected to a network interface 108 to allow the computer system 101 A to communicate over a data network 110 with one or more additional computer systems and/or computing systems, such as 10 IB, 101C, and/or 10 ID (note that computer systems 101B, 101C and/or 101D may or may not share the same architecture as computer system 101A, and may be located in different physical locations, e.g., computer systems 101A and 10 IB may be on a ship underway on the ocean, while in communication with one or more computer systems such as 101 C and/or 101D that are located in one or more data centers on shore, other ships, and/or located in varying countries on different continents). Note that data network 110 may be a private network, it may use portions of public networks, it may include remote storage and/or applications processing capabilities (e.g., cloud computing).

[0026] A processor can include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0027] The storage media 106 can be implemented as one or more computer-readable or machine-readable storage media. Note that, while in the example embodiment of Figure 1, storage media 106 is depicted as within computer system 101A, in some embodiments, storage media 106 may be distributed within and/or across multiple internal and/or external enclosures of computing system 101A and/or additional computing systems. Storage media 106 may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable readonly memories (EEPROMs) and flash memories; magnetic disks such as fixed, floppy and removable disks; other magnetic media including tape; optical media such as compact disks (CDs) or digital video disks (DVDs), BluRays or any other type of optical media; or other types of storage devices. Note that the instructions discussed above can be provided on one computer-readable or machine-readable storage medium, or alternatively, can be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes and/or non-transitory storage means. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture can refer to any manufactured single component or multiple components. The storage medium or media can be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions can be downloaded over a network for execution.

[0028] It should be appreciated that computer system 101A is one example of a computing system, and that computer system 101 A may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of Figure 1, and/or computer system 101 A may have a different configuration or arrangement of the components depicted in Figure 1. The various components shown in Figure 1 may be implemented in hardware, software, or a combination of both, hardware and software, including one or more signal processing and/or application specific integrated circuits.

[0029] It should also be appreciated that while no user input/output peripherals are illustrated with respect to computer systems 101A, 101B, 101C, and 101D, many embodiments of computing system 100 include computer systems with keyboards, mice, touch screens, displays, etc. Some computer systems in use in computing system 100 may be desktop workstations, laptops, tablet computers, smartphones, server computers, etc.

[0030] Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and/or their combination with general hardware are included within the scope of protection.

[0031] Figures 2-5 illustrate simplified, schematic views of an oilfield having a subterranean formation containing a reservoir therein in accordance with implementations of various technologies and techniques described herein. More particularly, Figure 2 illustrates a survey operation being performed by a survey tool, such as seismic truck 206.1, to measure properties of the subterranean formation. The survey operation is a seismic survey operation for producing sound vibrations. In Figure 2, one such sound vibration (e.g., sound vibration 212 generated by source 210) reflects off horizons 214 in earth formation 216. A set of sound vibrations is received by sensors, such as geophone-receivers 218, situated on the earth's surface. The data received 220 is provided as input data to a computer 222.1 of a seismic truck 206.1, and responsive to the input data, the computer 222.1 generates seismic data output 224. This seismic data output may be stored, transmitted or further processed as desired, for example, by data reduction.

[0032] Figure 3 illustrates a drilling operation being performed by drilling tools 206.2 suspended by rig 228 and advanced into a subterranean formations 202 to form a wellbore 236. A mud pit 230 is used to draw drilling mud into the drilling tools via flow line 232 for circulating drilling mud down through the drilling tools, then up wellbore 236 and back to the surface. The drilling mud is typically fdtered and returned to the mud pit. A circulating system may be used for storing, controlling, or filtering the flowing drilling mud. The drilling tools are advanced into the subterranean formations 202 to reach the reservoir 204. Each well may target one or more reservoirs. The drilling tools are adapted for measuring downhole properties using logging while drilling tools. The logging while drilling tools may also be adapted for taking core sample 233 as shown.

[0033] Computer facilities may be positioned at various locations about the oilfield 200 (e.g., the surface unit 234) and/or at remote locations. The surface unit 234 may be used to communicate with the drilling tools and/or offsite operations, as well as with other surface or downhole sensors. The surface unit 234 is capable of communicating with the drilling tools to send commands to the drilling tools, and to receive data therefrom. The surface unit 234 may also collect data generated during the drilling operation and produce data output 235, which may then be stored or transmitted.

[0034] Sensors (S), such as gauges, may be positioned about the oilfield 200 to collect data relating to various oilfield operations as described previously. As shown, the sensor (S) is positioned in one or more locations in the drilling tools and/or at the rig 228 to measure drilling parameters, such as weight on bit, torque on bit, pressures, temperatures, flow rates, compositions, rotary speed, and/or other parameters of the field operation. The sensors (S) may also be positioned in one or more locations in the circulating system.

[0035] Drilling tools 206.2 may include a bottom hole assembly (BHA) (not shown), generally referenced, near the drill bit (e.g., within several drill collar lengths from the drill bit). The bottom hole assembly includes capabilities for measuring, processing, and storing information, as well as communicating with surface unit 234. The bottom hole assembly further includes drill collars for performing various other measurement functions.

[0036] The bottom hole assembly may include a communication subassembly that communicates with surface unit 234. The communication subassembly is adapted to send signals to and receive signals from the surface using a communications channel such as mud pulse telemetry, electro-magnetic telemetry, or wired drill pipe communications. The communication subassembly may include, for example, a transmitter that generates a signal, such as an acoustic or electromagnetic signal, which is representative of the measured drilling parameters. It will be appreciated by one of skill in the art that a variety of telemetry systems may be employed, such as wired drill pipe, electromagnetic or other known telemetry systems. [0037] The wellbore may be drilled according to a drilling plan that is established prior to drilling. The drilling plan sets forth equipment, pressures, trajectories and/or other parameters that define the drilling process for the wellsite. The drilling operation may then be performed according to the drilling plan. However, as information is gathered, the drilling operation may need to deviate from the drilling plan. Additionally, as drilling or other operations are performed, the subsurface conditions may change. The earth model may also need adjustment as new information is collected.

[0038] The data gathered by the sensors (S) may be collected by the surface unit 234 and/or other data collection sources for analysis or other processing. The data collected by the sensors (S) may be used alone or in combination with other data. The data may be collected in one or more databases and/or transmitted on or offsite. The data may be historical data, real time data, or combinations thereof. The real time data may be used in real time, or stored for later use. The data may also be combined with historical data or other inputs for further analysis. The data may be stored in separate databases, or combined into a single database.

[0039] The surface unit 234 may include a transceiver 237 to allow communications between the surface unit 334 and various portions of the oilfield 200 or other locations. The surface unit 234 may also be provided with or functionally connected to one or more controllers (not shown) for actuating mechanisms at the oilfield 200. The surface unit 234 may then send command signals to the oilfield 200 in response to data received. The surface unit 234 may receive commands via the transceiver 237 or may itself execute commands to the controller. A processor may be provided to analyze the data (locally or remotely), make the decisions and/or actuate the controller. In this manner, the oilfield 200 may be selectively adjusted based on the data collected. This technique may be used to optimize (or improve) portions of the field operation, such as controlling drilling, weight on bit, pump rates, or other parameters. These adjustments may be made automatically based on computer protocol, and/or manually by an operator. In some cases, well plans may be adjusted to select optimum (or improved) operating conditions, or to avoid problems.

[0040] Figure 4 illustrates a wireline operation being performed by a wireline tool 206.3 suspended by the rig 228 and into the wellbore 236 of Figure 3. The wireline tool 206.3 is adapted for deployment into the wellbore 236 for generating well logs, performing downhole tests and/or collecting samples. The wireline tool 206.3 may be used to provide another method and apparatus for performing a seismic survey operation. The wireline tool 206.3 may, for example, have an explosive, radioactive, electrical, or acoustic energy source 244 that sends and/or receives electrical signals to surrounding subterranean formations 202 and fluids therein. [0041] The wireline tool 206.3 may be operatively connected to, for example, geophones 218 and a computer 222.1 of a seismic truck 206.1 of Figure 2. The wireline tool 206.3 may also provide data to the surface unit 234. The surface unit 234 may collect data generated during the wireline operation and may produce data output 235 that may be stored or transmitted. The wireline tool 106.3 may be positioned at various depths in the wellbore 236 to provide a survey or other information relating to the subterranean formation 202.

[0042] The sensors (S), such as gauges, may be positioned about the oilfield 200 to collect data relating to various field operations as described previously. As shown, the sensor S is positioned in the wireline tool 206.3 to measure downhole parameters which relate to, for example porosity, permeability, fluid composition and/or other parameters of the field operation.

[0043] Figure 5 illustrates a production operation being performed by a production tool 206.4 deployed from a production unit or a Christmas tree 229 and into the completed wellbore 236 for drawing fluid from the downhole reservoirs into surface facilities 242. The fluid flows from the reservoir 204 through perforations in the casing (not shown) and into the production tool 206.4 in the wellbore 236 and to the surface facilities 242 via a gathering network 246.

[0044] The sensors (S), such as gauges, may be positioned about the oilfield 200 to collect data relating to various field operations as described previously. As shown, the sensor (S) may be positioned in the production tool 206.4 or associated equipment, such as the Christmas tree 229, the gathering network 246, the surface facility 242, and/or the production facility, to measure fluid parameters, such as fluid composition, flow rates, pressures, temperatures, and/or other parameters of the production operation. Production may also include injection wells for added recovery. One or more gathering facilities may be operatively connected to one or more of the wellsites for selectively collecting downhole fluids from the wellsite(s).

[0045] While Figures 3-5 illustrate tools used to measure properties of an oilfield, it will be appreciated that the tools may be used in connection with non-oilfield operations, such as gas fields, mines, aquifers, storage or other subterranean facilities. Also, while certain data acquisition tools are depicted, it will be appreciated that various measurement tools capable of sensing parameters, such as seismic two-way travel time, density, resistivity, production rate, etc., of the subterranean formation and/or its geological formations may be used. Various sensors (S) may be located at various positions along the wellbore and/or the monitoring tools to collect and/or monitor the desired data. Other sources of data may also be provided from offsite locations.

[0046] The field configurations of Figures 2-5 are intended to provide a brief description of an example of a field usable with oilfield application frameworks. Part of, or the entirety of, the oilfield 200 may be on land, water, and/or sea. Also, while a single field measured at a single location is depicted, oilfield applications may be utilized with any combination of one or more oilfields, one or more processing facilities and one or more wellsites.

[0047] Figure 6 illustrates a schematic view, partially in cross section of an oilfield 600 having data acquisition tools 602.1, 602.2, 602.3 and 602.4 positioned at various locations along the oilfield 600 for collecting data of the subterranean formation 604 in accordance with implementations of various technologies and techniques described herein. The data acquisition tools 602.1-602.4 may be the same as the data acquisition tools 206.1-206.4 of Figures 2-5, respectively, or others not depicted. As shown, the data acquisition tools 602.1-602.4 generate data plots or measurements 608.1-608.4, respectively. These data plots are depicted along the oilfield 600 to demonstrate the data generated by the various operations.

[0048] The data plots 608.1-608.3 are examples of static data plots that may be generated by the data acquisition tools 602.1-602.3, respectively; however, it should be understood that the data plots 608.1-608.3 may also be data plots that are updated in real time. These measurements may be analyzed to better define the properties of the formation(s) and/or determine the accuracy of the measurements and/or for checking for errors. The plots of each of the respective measurements may be aligned and scaled for comparison and verification of the properties.

[0049] The static data plot 608.1 is a seismic two-way response over a period of time. The static plot 608.2 is core sample data measured from a core sample of the formation 604. The core sample may be used to provide data, such as a graph of the density, porosity, permeability, or some other physical property of the core sample over the length of the core. Tests for density and viscosity may be performed on the fluids in the core at varying pressures and temperatures. The static data plot 608.3 is a logging trace that provides a resistivity or other measurement of the formation at various depths.

[0050] A production decline curve or graph 608.4 is a dynamic data plot of the fluid flow rate over time. The production decline curve provides the production rate as a function of time. As the fluid flows through the wellbore, measurements are taken of fluid properties, such as flow rates, pressures, composition, etc.

[0051] Other data may also be collected, such as historical data, user inputs, economic information, and/or other measurement data and other parameters of interest. As described below, the static and dynamic measurements may be analyzed and used to generate models of the subterranean formation to determine characteristics thereof. Similar measurements may also be used to measure changes in formation aspects over time.

[0052] The subterranean structure 604 has a plurality of geological formations 606.1-606.4. As shown, this structure has several formations or layers, including a shale layer 606.1, a carbonate layer 606.2, a shale layer 606.3 and a sand layer 606.4. A fault 607 extends through the shale layer 206.1 and the carbonate layer 606.2. The static data acquisition tools are adapted to take measurements and detect characteristics of the formations.

[0053] While a specific subterranean formation with specific geological structures is depicted, it will be appreciated that the oilfield 600 may contain a variety of geological structures and/or formations, sometimes having extreme complexity. In some locations (e.g., below the water line) fluid may occupy pore spaces of the formations. Each of the measurement devices may be used to measure properties of the formations and/or its geological features. While each acquisition tool is shown as being in specific locations in the oilfield 600, it will be appreciated that one or more types of measurement may be taken at one or more locations across one or more fields or other locations for comparison and/or analysis.

[0054] The data collected from various sources, such as the data acquisition tools of Figure 6, may then be processed and/or evaluated. Seismic data displayed in the static data plot 608.1 from the data acquisition tool 602.1 is used by a geophysicist to determine characteristics of the subterranean formations and features. The core data shown in the static plot 608.2 and/or log data from the well log 608.3 are used by a geologist to determine various characteristics of the subterranean formation. The production data from the graph 608.4 is used by the reservoir engineer to determine fluid flow reservoir characteristics. The data analyzed by the geologist, geophysicist and the reservoir engineer may be analyzed using modeling techniques.

[0055] Figure 7 illustrates an oilfield 700 for performing production operations in accordance with implementations of various technologies and techniques described herein. As shown, the oilfield has a plurality of wellsites 702 operatively connected to a central processing facility 754. The oilfield configuration of Figure 7 is not intended to limit the scope of the oilfield application system. At least some of the oilfield may be on land and/or sea. Also, while a single oilfield with a single processing facility and a plurality of wellsites is depicted, any combination of one or more oilfields, one or more processing facilities and one or more wellsites may be present.

[0056] Each wellsite 702 has equipment that forms a wellbore 736 into the earth. The wellbores extend through subterranean formations 706 including reservoirs 704. These reservoirs 704 contain fluids, such as hydrocarbons. The wellsites draw fluid from the reservoirs and pass them to the processing facilities via surface networks 744. The surface networks 744 have tubing and control mechanisms for controlling the flow of fluids from the wellsite to a processing facility 754.

[0057] Attention is now directed to Figure 8, which illustrates a side view of a marine-based survey 760 of a subterranean subsurface 762 in accordance with one or more implementations of various techniques described herein. The subsurface 762 includes a seafloor surface 764. Seismic sources 766 may include marine sources such as vibroseis or airguns, which may propagate seismic waves 768 (e.g., energy signals) into the Earth over an extended period of time or at a nearly instantaneous energy provided by impulsive sources. The seismic waves may be propagated by marine sources as a frequency sweep signal. For example, marine sources of the vibroseis type may initially emit a seismic wave at a low frequency (e.g., 5 Hz) and increase the seismic wave to a high frequency (e.g., 80-90Hz) over time.

[0058] The component(s) of the seismic waves 768 may be reflected and converted by the seafloor surface 764 (i.e., reflector), and seismic wave reflections 770 may be received by a plurality of seismic receivers 772. The seismic receivers 772 may be disposed on a plurality of streamers (i.e., streamer array 774). The seismic receivers 772 may generate electrical signals representative of the received seismic wave reflections 770. The electrical signals may be embedded with information regarding the subsurface 762 and captured as a record of seismic data.

[0059] In one implementation, each streamer may include streamer steering devices such as a bird, a deflector, a tail buoy and the like, which are not illustrated in this application. The streamer steering devices may be used to control the position of the streamers in accordance with the techniques described herein.

[0060] In one implementation, the seismic wave reflections 770 may travel upward and reach the water/air interface at the water surface 776, a portion of reflections 770 may then reflect downward again (i.e., sea-surface ghost waves 778) and be received by the plurality of seismic receivers 772. The sea-surface ghost waves 778 may be referred to as surface multiples. The point on the water surface 776 at which the wave is reflected downward is generally referred to as the downward reflection point.

[0061] The electrical signals may be transmitted to a vessel 780 via transmission cables, wireless communication or the like. The vessel 780 may then transmit the electrical signals to a data processing center. Alternatively, the vessel 780 may include an onboard computer capable of processing the electrical signals (i.e., seismic data). Those skilled in the art having the benefit of this disclosure will appreciate that this illustration is highly idealized. For instance, surveys may be of formations deep beneath the surface. The formations may include multiple reflectors, some of which may include dipping events, and may generate multiple reflections (including wave conversion) for receipt by the seismic receivers 772. In one implementation, the seismic data may be processed to generate a seismic image of the subsurface 762.

[0062] In one embodiment, marine seismic acquisition systems tow each streamer in streamer array 774 at the same depth (e.g., 5- 10m). However, marine based survey 760 may tow each streamer in streamer array 774 at different depths such that seismic data may be acquired and processed in a manner that avoids the effects of destructive interference due to sea-surface ghost waves. For instance, the marine-based survey 760 of Figure 8 illustrates eight streamers towed by vessel 780 at eight different depths. The depth of each streamer may be controlled and maintained using the birds disposed on each streamer.

[0063] Attention is now directed to Figure 9 that depicts a marine electromagnetic survey system 782 in accordance with implementations of various technologies described herein. The electromagnetic survey system 782 may use control! ed-source electromagnetic (CSEM) survey techniques, but other electromagnetic survey techniques may also be used. Marine electromagnetic surveying may be performed by a survey vessel 784 that moves in a predetermined pattern along the surface 785 of a body of water such as a lake or the ocean. The survey vessel 784 is configured to pull a towfish (e g., an electric source) 786, which is connected to a pair of electrodes 788. During the survey, the vessel may stop and remain stationary for a period of time while obtaining measurements, while in some circumstances, the vessel may remain underway while obtaining measurements.

[0064] Attention is now directed to methods, techniques, and workflows for processing and/or transforming collected data that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and/or the order of some operations may be changed. Those with skill in the art will recognize that in the geosciences and/or other multi-dimensional data processing disciplines, various interpretations, sets of assumptions, and/or domain models such as velocity models, may be refined in an iterative fashion. This concept is applicable to the procedures, methods, techniques, and workflows as discussed herein. This iterative refinement can include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 100, Figure 1), and/or through manual control by a user who may make determinations regarding whether a given step, action, template, or model has become sufficiently accurate.

[0065] Attenuating Shear Noise

[0066] Ocean bottom node (OBN) technology is becoming state-of-the-art seismic data acquisition scheme in a marine scenario. In OBN acquisition, data is acquired using hydrophones and geophones to record complete elastic wavefields propagated in the subsurface. Although OBN is mentioned above, the system and method described herein may also or instead use distributed acoustic sensing using fiber-optics. During the pre-processing stage, one or more hydrophones may be configured to measure a pressure component (P), and one or more vertical geophones may be configured to measure a vertical component (V_z), such as a vertical velocity measurement and/or vertical particle displacement and its time derivatives. These measurements may be used to perform de-ghosting, de-multiple etc. using either up- down deconvolution or multi-dimensional deconvolution. The assumption of up-down deconvolution (UDD) or multi-dimensional deconvolution (MDD) is to have better signal-to- noise (SNR) ratio across both P and V_z. However, V_z is often contaminated by strong converted shear waves due to the scattering across the ocean-bottom subsurface.

[0067] Various methodologies are proposed in the seismic community. These methodologies hinge on the fact that the P and V_z components exhibit similar wavefield characteristics, except for the polarity difference between a scalar measurement (e.g., P) and a vector measurement (e.g., V_z). Also, the pressure component (P) may be noise-free (e.g., does not contain the shear noise). Thus, the pressure component P may be used as a guide to differentiate between the shear noise and coherent signal in the vertical component V_z by computing envelope ratio scaling using P and V_z. The choice of the domain where this ratio is computed varies from Tau- p domain, dual -true complex wavelet domain, or 2D, 3D curvelet domain. Examples described herein are related to the shear noise attenuation in the curvelet domain.

[0068] According to some examples, to estimate the envelope ratio scaling (ERS) in the curvelet domain, the pressure component P and the vertical component V_z may first be transformed into the curvelet domain. ERS may then be determined using the following equation:

„ , , P(s,d,t,x,real)²+P(s,d,t,x,imag)²

C (s, d, t, x = , (i) z(s,d,t,x,real)²+Z(s,d,t,x,imag)² where s, d, t, x represents scale, dip, time and space (i.e., the domain representing the curvelet transform).

[0069] A direct application of a scalar vector C results in calibrated P and V_z components; however, due to the presence of shear noise, this calibration may be inaccurate. Therefore, a filtering approach is described in the curvelet domain where the scaling ratio is applied to the coefficient locations at which shear noise is not present. Moreover, the coefficients containing the shear noise are muted to get the clean vertical component V_z. The muting criteria may be used, either manually or adaptively, to estimate C and then applied to the geophones as follows: c ₍ A — 1 ⁰

Chard (S, d t, X) _± where p_{s d} is inversely proportional to the estimated signal-to-noise ratio (SNR) (i.e., high SNR results in a low threshold value or vice-versa). [0070] While this simple thresholding defined in equation (2) removes shear noise, it results in coherent signal leakage in the vertical component V_z. Thus, extra processing may be employed after the shear noise attenuation to perform signal protection to preserve weak and/or strong signal leakage dues to thresholding. This post-attenuation workflow may be cumbersome and varies from one survey to another, shallow-to-deep scenario. Accordingly, the present disclosure enables a shear-noise attenuation workflow with automatic signal protection using adaptive localized filtering and optimum Bayesian weighting to obtain the optimal shear-noise-free signal model without any laborious post-attenuation workflow. Experimental results on real data from the Gulf of Mexico demonstrate the robustness of examples of techniques described herein in estimating signal-preserving shear noise models. [0071] Adaptive localized filtering

[0072] To understand the root cause of the signal leakage, the envelope ratio scaling vector may be analyzed across one or more scales and angles in the curvelet domain. The simultaneous-source acquisition survey used as an example here is from a deep-water (500 - 1700 m) ultra-long-offset (~40 - 60 km) OBN survey. The nodes are deployed in a staggered grid of 1000 m by 1000 m, whereas the sources are acquired with 50 m by 100 m sampling steps. The survey is a two-vessel acquisition, with three sources (i.e., triple source) on each vessel, where each source on the same vessel fires every 20 s, while the other two sources on the same vessel fire every 6.67 s thereafter, sequentially. A 2 km distance was maintained between vessels and a time-dither of ± 1 s was applied. Figure 10A illustrates a 2D subsection extracted from a common receiver gather for the pressure component P, and Figure 10B illustrates a 2D subsection extracted from a common receiver gather for the vertical (e.g., vertical particle velocity) component V_z, according to an embodiment. The arrows in Figure 10B indicate shear noise.

[0073] For some examples, the P and V_zcomponents may be transformed into the curvelet domain. Then, the envelope ratio scaling may be determined across one or more scales and angles. The wedges may then be put together at different scale and angles next to each other to understand the characteristic of ERS.

[0074] Figures 11A-11C illustrate images showing the impact of adaptive localized filtering on preserving the weak coherent signal buried beneath the shear noise when performing thresholding based on envelope ratio scaling, according to an embodiment. More particularly, Figure 1 1 A illustrates an image before shear noise attenuation, Figure 1 IB illustrates an image modified using a thresholding equation, and Figure 11C illustrates an image after applying adaptive localized filtering on C_hard(s, d, t, x).

[0075] As evident from Figure 11 A, C_hard(s, d, t, x) exhibits a low-amplitude salt-pepper characteristic apart from the coefficients where the scaling ratio is high. The salt paper coefficient’ s location represents signal leakage caused by an un-calibrated V_z component, which is quite common when calibration is performed on raw V_z components before shear-noise attenuation, whereas high scaling ratio coefficients represent the location contaminated with the shear noise. Thus, the high scaling ratio coefficient’s location may be maintained while removing the low-amplitude salt-paper signal leakage.

[0076] To achieve this, according to some examples, a root-mean-square (e.g., quadratic mean) may be determined in a sliding window where a multi-dimensional window of specified length moves over each scale and angle wedge in the curvelet domain and computes the rootmean-square (RMS) in the window for each pixel value. Here, RMS is defined as the square root of the arithmetic mean of the squares of a set of pixel values. The RMS value may then be assigned to the central pixel of the multi-dimensional window before moving to the next pixel value. Moreover, the size of the sliding window changes from scale to scale, as the sampling of each curvelet scale and wedge is a function of frequency and wavenumber content represented by the corresponding wedge. The window size of scales and angles representing lower frequency and wavenumbers are small, whereas bigger window sizes are used for scale and angle at a higher frequency and wavenumber wedges. As shown in Figure, 1 IB, this results in an adaptive localized filtering approach to preserve signal leakage caused by un-calibrated P and V_z components.

[0077] Figure 11C demonstrates the benefit of this approach on signal protection in the timespace domain. A level of low-amplitude signal protection may be seen when the envelope scaling ratio is estimated using a localized filtering approach rather than pixel-wise computation.

[0078] Optimum Bayesian weighting

Although adaptive localized filtering mitigates signal leakage at locations not contaminated with shear noise in the curvelet domain, the locations of the coefficients in the curvelet domain contaminated with noise may be muted. In some examples, this may be undesirable, as it may also remove the coherent signal mapped to those locations in the curvelet domain.

To find out the optimal thresholding at the locations of shear noise in the curvelet domain, the second order statistics may be used to estimate the vertical particle velocity measurement optimally (Z) in the minimum mean square error sense. The estimated vertical particle velocity can be obtained as a weighted linear combination of the recorded pressure and the particle velocity as follows:

Z = w_p ^HP_n + w_z ^HZ_n, (3) where w_P and wz correspond to the weights leading to the optimal vertical particle velocity data. [0079] Denoting the vectors of weights and measurements by w and D in equation (3), the weights can be obtained by formulating the problem as minimum mean square error problem as follows: w = argmin E [|Z — Z|²| w J

- r 2-> (⁴)

= argmin E ] |w^HD — Z| , w I ¹ J where E{-} is the expectation operator taken over the pressure, velocity measurements and noise distributions.

[0080] The minimizer of the cost function is called the linear minimum mean square error estimator. It is a function of the second order statistics of the unknown noise-free vertical particle velocity Z. The minimizer of equation Error! Reference source not found, can be expressed as the solution of the following set of normal equation:

RDDW = TDZ (5) where R_DD is defined as the measurement covariance matrix, and r_DZ is defined as correlation vector between the measurements and the desired noise-free vertical particle velocity wavefield.

R where OXY is the correlation between X and Y. Note that the covariance matrix in equation Error! Reference source not found, can be estimated directly from the measured data.

[0081] For some embodiments, the windowed frequency wavenumber domain (FK domain) may be used as the domain where these statistics are computed. The statistics may be computed in different domains and the choice of the FK domain was done because it is efficient computationally to do so. If the noise variance of the pressure and the vertical particle velocity is denoted by o^and respectively, and assuming that the noise in the pressure is uncorrelated with the noise in the vertical particle velocity, then the correlation vector r_DZ can be obtained as follows:

[0082] The correlation vector r_DZ can be determined in addition to the correlation matrix

R_DD from the data in different domains. The solution in equation Error! Reference source not found, can be extended to include the inline and crossline velocity measurements. In this extended multi-measurement formulation, the estimated vertical particle velocity wavefield can be provided as a weighted sum of the measurement vector D as follows:

[0083] From the previous equations, the vertical particle velocity can be estimated from:

[0084] If it is assumed that the noise in pressure is zero (i.e., = 0), the cross-correlation coefficient is p = _and the signal-to-noise ratio (SNR) is this may lead to:

^CTZ^CTP ^CTn_z

[0085] If <^_z is high, then Z~Z. If (J_z is low, and the correlation coefficient is high (i.e.,p ~ 1), then Z « p — P_n. It is clear from this, that in areas where the SNR is low at the vertical particle Cp velocity measurement, then Z ~ p — P_n .

CTp

[0086] Exemplary Method to Estimate Optimal Shear Noise

[0087] The pressure component P and the vertical component V_z may be transformed into the curvelet domain. Then, an envelope scaling ratio C_hard(s, d, t, x) may be determined for each scale and angle wedge. Then adaptive localized filtering may be applied to each scale and/or angle wedge to remove salt-pepper signal leakage. Then, using Equation (2), the sub-optimal shear noise model N_vz may be estimated, and it may be transformed back into the time-space domain. Then, in localized multi-dimensional temporal spatial windows, the pressure component P, the vertical component V_z, and the sub-optimal shear noise model N_vz may be transformed into the Fourier- wav enumber domain.

[0088] Figure 12 illustrates a flowchart of a method 1200 for attenuating shear noise in seismic data (e.g., using Bayesian summation), according to an embodiment. An illustrative order of the method 1200 is provided below; however, one or more portions of the method 1200 may be performed in a different order, simultaneously, repeated, or omitted.

[0089] The method 1200 may include receiving seismic data, as at 1210. The seismic data may be or include multi-component seismic data including a pressure component and/or a vertical component. The vertical component may be or include a vertical velocity measurement and/or vertical particle displacement and its time derivatives. The seismic data may be received from an ocean-bottom node. The seismic data may be acquired by the ocean-bottom node using sequential or simultaneous shooting.

[0090] The method 1200 may also include interpolating the seismic data to generate interpolated seismic data, as at 1220. The interpolated seismic data may be or include an interpolated pressure component and/or an interpolated vertical component. The seismic data may be interpolated at a regular grid.

[0091] The method 1200 may also include mapping the (e.g., interpolated) pressure component and the (e.g., interpolated) vertical component to generate mapped seismic data, as at 1230. The (e.g., interpolated) pressure component and the (e.g., interpolated) vertical component may be mapped in a multi-dimensional sparsity-promoting transform domain. The (e.g., interpolated) pressure component and the (e g., interpolated) vertical component may be mapped to identify different frequency-related data and/or different dip-related data. The frequency-related data may be or include a subset of different frequency bands. The dip-related data may be or include a subset of different dip formations.

[0092] The method 1200 may also include determining a correlation between the (e.g., interpolated) pressure component and the (e.g., interpolated) vertical component based upon the mapped seismic data, as at 1240. The correlation may be determined in the multi- dimensional sparsity-promoting transform domain. The correlation may be based upon the different frequency -related data and/or the different dip-related data.

[0093] The method 1200 may also include attenuating shear noise in the seismic data to generate attenuated seismic data, as at 1250. The shear noise may be attenuated based upon the correlation. The shear noise contaminates the vertical particle displacement and/or its time derivatives of the vertical component in the seismic data.

[0094] Attenuating the shear noise may include matching a first amplitude of the vertical component to a second amplitude of the pressure component, as at 1251. The first amplitude may be or include a pixel-wise amplitude of complex coefficients of the vertical component. The second amplitude may be or include a pixel-wise amplitude of complex coefficients of the pressure component. A phase of the vertical component may be preserved (e.g., remain constant) during the matching.

[0095] Attenuating the shear noise may also include determining a root-mean-square (RMS) value of the matched first and second amplitudes, as at 1252. The RMS value may be determined using a localized sliding window in a temporal-spatial domain. The RMS value may be determined based upon a temporal-spatial continuity of the shear noise in the multidimensional sparsity-promoting transform domain.

[0096] Attenuating the shear noise may also include estimating the shear noise in the vertical component based upon the RMS value, as at 1253. The shear noise may be estimated in the vertical particle displacement and/or its time derivatives of the vertical component.

[0097] Attenuating the shear noise may also include determining a first order noise and signal statistic and a second order noise and signal statistic based upon the estimated shear noise, as at 1254. The first order noise and signal statistic may be or include a mean (e.g., average), and the second order noise and signal statistic may be or include a variance.

[0098] Attenuating the shear noise may also include generating an optimal shear noise model, as at 1255. The model may be based upon the pressure component, the vertical component, the estimated shear noise, the first order noise and signal statistics, the second order noise and signal statistics, or a combination thereof. The optimal shear noise model may be or include an optimal Bayesian weighted noise model.

[0099] Attenuating the shear noise may also include attenuating the shear noise in the multicomponent seismic data to generate the attenuated seismic data, as at 1256. The shear noise may be attenuated using the optimal shear noise model.

[0100] The method 400 may also include displaying the attenuated seismic data, as at 1260. The attenuated seismic data may be displayed as one or more images, one or more tracks, one or more graphs, or a combination thereof.

[0101] The method 400 may also include performing a wellsite action, as at 1270. The wellsite action may be based upon the attenuated seismic data. The wellsite action may be or include generating and/or transmitting a signal (e.g., using a computing system). The signal may instruct or cause a physical action to occur at a wellsite. The wellsite action may also or instead include performing the physical action at the wellsite. The physical action may include selecting where to drill a wellbore, drilling the wellbore, varying a weight and/or torque on a drill bit that is drilling the wellbore, varying a drilling trajectory of the wellbore, varying a concentration and/or flow rate of a fluid pumped into the wellbore, or the like.

[0102] In some embodiments, the multi-dimensional region of interest is selected from the group consisting of a subterranean region, human tissue, plant tissue, animal tissue, solid volumes, substantially solid volumes, volumes of liquid, volumes of gas, volumes of plasma, and volumes of space near and/or outside the atmosphere of a planet, asteroid, comet, moon, or other body.

[0103] The steps in the processing methods described above may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and/or their combination with general hardware are included within the scope of protection.

[0104] Of course, many processing techniques for collected data, including one or more of the techniques and methods disclosed herein, may also be used successfully with collected data types other than seismic data. While certain implementations have been disclosed in the context of seismic data collection and processing, those with skill in the art will recognize that one or more of the methods, techniques, and computing systems disclosed herein can be applied in many fields and contexts where data involving structures arrayed in a multi-dimensional space and/or subsurface region of interest may be collected and processed (e.g., medical imaging techniques such as tomography, ultrasound, MRI and the like for human tissue; radar, sonar, and LIDAR imaging techniques; mining area surveying and monitoring, oceanographic surveying and monitoring, and other appropriate multi-dimensional imaging problems).

[0105] Many examples of equations and mathematical expressions have been provided in this disclosure. But those with skill in the art will appreciate that variations of these expressions and equations, alternative forms of these expressions and equations, and related expressions and equations that can be derived from the example equations and expressions provided herein may also be successfully used to perform the methods, techniques, and workflows related to the embodiments disclosed herein.

[0106] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit disclosure to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

Claims What is claimed is:

1. A method for attenuating shear noise in seismic data, the method comprising: receiving seismic data, the seismic data includes multi-component seismic data including a pressure component and a vertical component; mapping the pressure component and the vertical component to generate mapped seismic data; determining a correlation between the pressure component and the vertical component based upon the mapped seismic data; and attenuating shear noise in the seismic data to generate attenuated seismic data, the shear noise is attenuated based upon the correlation.

2. The method of claim 1 , wherein the vertical component includes a vertical particle displacement and its time derivatives, the seismic data is received from an ocean-bottom node, and the seismic data is acquired by the ocean-bottom node using sequential or simultaneous shooting.

3. The method of claim 1, comprising interpolating the seismic data to generate interpolated seismic data, the interpolated seismic data includes an interpolated pressure component and an interpolated vertical component, and the interpolated pressure component and the interpolated vertical component are mapped to generate the mapped seismic data.

4. The method of claim 1, wherein the pressure component and the vertical component are mapped in a multi-dimensional sparsity-promoting transform domain, the pressure component and the vertical component are mapped to identify frequency-related data and dip-related data.

5. The method of claim 4, wherein the correlation is determined in the multi-dimensional sparsity-promoting transform domain, and the correlation is based upon the frequency-related data and the dip-related data.

6. The method of claim 1, wherein attenuating the shear noise comprises: matching a first amplitude of the vertical component to a second amplitude of the pressure component; and determining a root-mean-square (RMS) value of the matched first and second amplitudes.

7. The method of claim 6, wherein attenuating the shear noise comprises: estimating the shear noise in the vertical component based upon the RMS value; and determining a first order noise and signal statistic and/or a second order noise and signal statistic based upon the estimated shear noise.

8. The method of claim 7, wherein attenuating the shear noise comprises: generating or updating a shear noise model based upon the pressure component, the vertical component, the estimated shear noise, and the first and/or second order noise and signal statistics; and attenuating the shear noise in the seismic data to generate the attenuated seismic data using the shear noise model.

9. The method of claim 1, comprising displaying the attenuated seismic data.

10. The method of claim 1, comprising performing a physical wellsite action in response to the attenuated seismic data.

11. A computing system, comprising: one or more processors; and a memory system including one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations including: receiving seismic data, the seismic data includes multi-component seismic data including a pressure component and a vertical component, and the vertical component includes a vertical particle displacement and its time derivatives; interpolating the seismic data to generate interpolated seismic data, the interpolated seismic data includes an interpolated pressure component and an interpolated vertical component; mapping the interpolated pressure component and the interpolated vertical component to generate mapped seismic data, the interpolated pressure component and the interpolated vertical component are mapped in a multi-dimensional sparsitypromoting transform domain, the interpolated pressure component and the interpolated vertical component are mapped to identify different frequency-related data and different dip-related data; determining a correlation between the interpolated pressure component and the interpolated vertical component based upon the mapped seismic data, the correlation is determined in the multi-dimensional sparsity-promoting transform domain, and the correlation is based upon the different frequency-related data and the different dip- related data; and attenuating shear noise in the seismic data to generate attenuated seismic data, the shear noise is attenuated based upon the correlation.

12. The computing system of claim 11, wherein attenuating the shear noise comprises: matching a first amplitude of the vertical component to a second amplitude of the pressure component; determining a root-mean-square (RMS) value of the matched first and second amplitudes; estimating the shear noise in the vertical component based upon the RMS value; determining a first order noise and signal statistic and a second order noise and signal statistic based upon the estimated shear noise; generating or updating an optimal shear noise model based upon the pressure component, the vertical component, the estimated shear noise, and the first and second order noise and signal statistics; and attenuating the shear noise in the multi-component seismic data to generate the attenuated seismic data using the optimal shear noise model.

13. The computing system of claim 12, wherein the first amplitude includes a pixelwise amplitude of complex coefficients of the vertical component, the second amplitude includes a pixel-wise amplitude of complex coefficients of the pressure component, and a phase of the vertical component is preserved during the matching.

14. The computing system of claim 12, wherein the RMS value is determined using a localized sliding window in a temporal-spatial domain, and the RMS value is determined based upon a temporal-spatial continuity of the shear noise in the multi-dimensional sparsitypromoting transform domain.

15. The computing system of claim 12, wherein the first order noise and signal statistic includes a mean, and the second order noise and signal statistic includes a variance and the correlation.

16. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising: receiving seismic data, the seismic data includes multi-component seismic data including a pressure component and a vertical component, the vertical component includes a vertical particle displacement and its time derivatives, the seismic data is received from an ocean-bottom node, and the seismic data is acquired by the ocean-bottom node using sequential or simultaneous shooting; interpolating the seismic data to generate interpolated seismic data, the interpolated seismic data includes an interpolated pressure component and an interpolated vertical component, and the seismic data is interpolated at a regular grid; mapping the interpolated pressure component and the interpolated vertical component to generate mapped seismic data, the interpolated pressure component and the interpolated vertical component are mapped in a multi-dimensional sparsity-promoting transform domain, the interpolated pressure component and the interpolated vertical component are mapped to identify different frequency-related data and different dip-related data; determining a correlation between the interpolated pressure component and the interpolated vertical component based upon the mapped seismic data, the correlation is determined in the multi-dimensional sparsity-promoting transform domain, and the correlation is based upon the different frequency-related data and the different dip-related data; attenuating shear noise in the seismic data to generate attenuated seismic data, the shear noise is attenuated based upon the correlation, the shear noise contaminates the vertical particle displacement and/or its time derivatives of the vertical component in the seismic data, and attenuating the shear noise includes: i) matching a first amplitude of the vertical component to a second amplitude of the pressure component, the first amplitude includes a pixel-wise amplitude of complex coefficients of the vertical component, the second amplitude includes a pixel-wise amplitude of complex coefficients of the pressure component, and a phase of the vertical component is preserved during the matching; ii) determining a root-mean-square (RMS) value of the matched first and second amplitudes, the RMS value is determined using a localized sliding window in a temporal-spatial domain, and the RMS value is determined based upon a temporal- spatial continuity of the shear noise in the multi-dimensional sparsity-promoting transform domain; iii) estimating the shear noise in the vertical component based upon the RMS value, the shear noise is estimated in the vertical particle displacement and/or its time derivatives of the vertical component; iv) determining a first order noise and signal statistic and a second order noise and signal statistic based upon the estimated shear noise, the first order noise and signal statistic includes a mean, and the second order noise and signal statistic includes a variance and the correlation; v) generating or updating an optimal shear noise model based upon the pressure component, the vertical component, the estimated shear noise, and the first and second order noise and signal statistics, the optimal shear noise model is an optimal Bayesian weighted noise model; and vi) attenuating the shear noise in the multi-component seismic data to generate the attenuated seismic data using the optimal shear noise model; and displaying the attenuated seismic data.

17. The non-transitory computer-readable medium of claim 16, wherein the operations comprise performing an action based upon the attenuated seismic data.

18. The non-transitory computer-readable medium of claim 17, wherein the action comprises generating and transmitting a signal.

19. The non-transitory computer-readable medium of claim 18, wherein the signal instructs or causes a physical action to occur at a wellsite.

20. The non-transitory computer-readable medium of claim 19, wherein the physical action includes varying a weight or torque on a drill bit that is drilling a wellbore, varying a trajectory of the wellbore, or both.