阅读说明：本技术 抑制地震数据中的噪声 (Suppressing noise in seismic data ) 是由 秦福豪 康斯坦丁诺斯·钦戈斯 于 2018-05-15 设计创作，主要内容包括：本公开描述了用于抑制地震数据中的噪声的方法和系统,包括计算机实现的方法、计算机程序产品和计算机系统。一种计算机实现的方法包括：在数据处理装置处接收与地下区域相关联的地震数据集合；由数据处理装置根据识别的地震事件对地震数据集合进行平坦化；由数据处理装置将地震数据集合划分为多个空间窗口；由数据处理装置根据随机次序顺序对地震数据集合进行随机化；由数据处理装置对随机化后的地震数据进行滤波；以及由数据处理装置根据随机化前的顺序对滤波后的地震数据进行重新组织。(The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for suppressing noise in seismic data. A computer-implemented method comprising: receiving, at a data processing device, a set of seismic data associated with a subsurface region; flattening, by the data processing device, the seismic data set according to the identified seismic event; dividing, by a data processing device, a seismic data set into a plurality of spatial windows; randomizing, by the data processing device, the seismic data set according to a random order sequence; filtering the randomized seismic data by a data processing device; and reorganizing, by the data processing device, the filtered seismic data according to the order prior to randomization.)

1. A computer-implemented method for suppressing noise in seismic data, comprising:

receiving, at a data processing device, a set of seismic data associated with a subsurface region;

flattening, by the data processing device, the seismic data set according to the identified seismic event;

dividing, by the data processing device, the seismic data set into a plurality of spatial windows;

randomizing, by the data processing device, the seismic data set according to a random order sequence;

filtering, by the data processing device, the randomized seismic data; and

reorganizing, by the data processing device, the filtered seismic data according to the order prior to randomization.

2. The method of claim 1, wherein the seismic data is post-stack data.

3. The method of claim 2, wherein the seismic data comprises at least one of pre-stack time migration results or pre-stack depth migration results.

4. The method of claim 1, wherein the identified seismic event is identified from an interpretation of the subsurface region.

5. The method of claim 1, further comprising: generating, by the data processing device, an image of the subsurface region based on the reorganized data.

6. The method of claim 1, further comprising: subtracting, by the data processing device, the reorganized set of seismic data from the seismic data.

7. The method of claim 1, further comprising: filtering, by the data processing device, the reorganized seismic data.

8. A non-transitory computer-readable medium storing instructions that, when executed, cause a computing device to perform operations comprising:

receiving, at a data processing device, a set of seismic data associated with a subsurface region;

flattening, by the data processing device, the seismic data set according to the identified seismic event;

dividing, by the data processing device, the seismic data set into a plurality of spatial windows;

randomizing, by the data processing device, the seismic data set according to a random order sequence;

filtering, by the data processing device, the randomized seismic data; and

reorganizing, by the data processing device, the filtered seismic data according to the order prior to randomization.

9. The non-transitory computer-readable medium of claim 8, wherein the seismic data is post-stack data.

10. The non-transitory computer-readable medium of claim 9, wherein the seismic data includes at least one of pre-stack time migration results or pre-stack depth migration results.

11. The non-transitory computer-readable medium of claim 8, wherein the identified seismic event is identified from an interpretation of the subsurface region.

12. The non-transitory computer-readable medium of claim 8, the operations further comprising: generating, by the data processing device, an image of the subsurface region based on the reorganized data.

13. The non-transitory computer-readable medium of claim 8, the operations further comprising: subtracting, by the data processing device, the reorganized set of seismic data from the seismic data.

14. The non-transitory computer-readable medium of claim 8, the operations further comprising: filtering, by the data processing device, the reorganized seismic data.

15. An apparatus, comprising:

at least one hardware processor; and

a non-transitory computer-readable storage medium coupled to the at least one hardware processor and storing programming instructions for execution by the at least one hardware processor, wherein the programming instructions, when executed, cause the at least one hardware processor to perform operations comprising:

receiving, at the at least one hardware processor, a set of seismic data associated with a subsurface region;

flattening, by the at least one hardware processor, the seismic data set according to the identified seismic events;

dividing, by the at least one hardware processor, the seismic data set into a plurality of spatial windows;

randomizing, by the at least one hardware processor, the seismic data set according to a random order sequence;

filtering, by the at least one hardware processor, the randomized seismic data; and

reorganizing, by the at least one hardware processor, the filtered seismic data according to an order prior to randomization.

16. The apparatus of claim 15, wherein the seismic data is post-stack data.

17. The apparatus of claim 16, wherein the seismic data comprises at least one of pre-stack time migration results or pre-stack depth migration results.

18. The apparatus of claim 15, wherein the identified seismic event is identified from an interpretation of the subsurface region.

19. The device of claim 15, the operations further comprising: generating, by a data processing device, an image of the subsurface region based on the reorganized data.

20. The device of claim 15, the operations further comprising: subtracting, by the data processing device, the reorganized set of seismic data from the seismic data.

Technical Field

The present disclosure relates to suppressing noise in seismic data.

Background

In geophysical analysis, seismic data is collected and used to analyze subsurface geological structures and rock properties of a geographic region. These data, and the analysis based on them, are critical to exploration, production and drilling operations in the oil and gas industry.

Disclosure of Invention

The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for suppressing noise in seismic data. A computer-implemented method for suppressing noise in seismic data, comprising: receiving, at a data processing device, a set of seismic data associated with a subsurface region; flattening, by the data processing device, the seismic data set according to the identified seismic events; dividing, by a data processing device, a seismic data set into a plurality of spatial windows; randomizing, by the data processing device, the seismic data set according to a random order sequence; filtering the randomized seismic data by a data processing device; and reorganizing, by the data processing device, the filtered seismic data according to the order prior to randomization.

Other implementations of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods. A system of one or more computers may be configured to perform particular operations or actions by software, firmware, hardware, or a combination of software, firmware, or hardware installed on the system that in operation causes the system to perform the actions. One or more computer programs may be configured to perform particular operations or actions by including instructions that, when executed by a data processing apparatus, cause the apparatus to perform the actions.

The details of one or more implementations of the subject matter of this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims.

Drawings

FIG. 1 illustrates an example seismic data noise suppression process according to an implementation.

FIG. 2 illustrates an example effect of seismic data noise suppression processing according to an implementation.

FIG. 3 illustrates an example pre-stack time-shifted (migrated) seismic section according to an implementation.

FIG. 4 illustrates example results of filtered seismic data according to an implementation.

FIG. 5 illustrates an example comparison between an original seismic input and an extracted filtered seismic section according to an implementation.

FIG. 6 is a high-level architectural block diagram of a geophysical imaging system according to an implementation.

Like reference numbers and designations in the various drawings indicate like elements.

Detailed Description

The following description is presented to enable any person skilled in the art to make and use the disclosed subject matter, and is provided in the context of one or more particular implementations. Various modifications to the disclosed implementations will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other implementations and applications without departing from the scope of the present disclosure. Thus, the present disclosure is not intended to be limited to the implementations described and/or illustrated, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

The present disclosure generally describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for suppressing noise in seismic data. In some cases, seismic signals may be transmitted through a source device into the subsurface of the earth at a source location. Examples of seismic signals include acoustic signals. Seismic signals travel through the subsurface and may be received by receiver devices placed at receiver locations. In some cases, the source device, the receiver device, or a combination thereof may be placed on the earth's surface. The signal may travel downward until reaching the reflective structure and reflecting upward toward the surface. Because the signal is refracted and reflected by the subsurface structures, the characteristics of the received signal contain information about the subsurface structures. The received signals may be collected as seismic data. The seismic data may include traces of reflected waves (traces).

In marine surveys, air guns and hydrophones may be used as source and receiver devices, respectively. During acquisition, a seismic source is fired from an air gun array. Reflected and refracted signals are acquired by the streamers of the hydrophones. In land acquisition, explosives may be used as the source of the detonation and geophones employed as the receiver equipment. In another example, a vibrating truck may be used as the source device. Other devices for generating and receiving seismic signals may also be used.

In some implementations, migration operations are applied to the seismic data to image subsurface structures. Seismic migration (Seismic migration) is one such process: seismic events are geometrically repositioned in space or time to the location where the event occurred in the subsurface, rather than recording the event at the surface, thereby creating a more accurate subsurface image. Migration of seismic data is a correction to a flat geological layer assumption by numerically grid-based spatial convolution of the seismic data to compute dip (ripping) events in which the geological layer is not flat. In migration processing, echo information contained in the seismic data is converted from recording time to features in the subsurface depth. The migration process positions the seismic feature in its position in space in both the lateral and vertical directions.

Different offset algorithms may be used. For example, in the depth domain, ray-based and beam-based kirchhoff methods are popular in practice due to their computational efficiency and flexibility towards the target. Thereafter, one-way and two-way wave equation migration is widely used. In some cases, a Reverse Time Migration (RTM) may be used in analyzing the migration. The RTM algorithm includes calculation of the forward propagation of the source wavefield, calculation of the backward propagation of the receiver wavefield, and calculation of the correlation imaging conditions between the two calculated wavefields. In some cases, the source and receiver wavefields may be referred to as forward and backward wavefields, respectively. In the time domain, imaging is typically achieved by co-depth point (CDP) stacking, which assumes a flat subsurface structure, followed by post-stack (post-stack) migration, or pre-stack time migration similar to kirchhoff migration in the depth domain. However, the travel time of energy transfer from the source and receiver to the imaging point cannot be computed by ray tracing as in the depth domain. It is based on Normal Moveout (NMO) velocity and Dix formula, which assumes that the velocity model is formed of relatively flat and thick layers.

In some cases, it may be difficult to obtain a reliable seismic image generated based on the seismic data due to noise in the seismic data. For example, seismic data may be affected by interbed multiples (interlamutiplies) associated with subsurface structures. These interbed multiples may be caused by seismic energy bouncing off multiple reflecting interfaces in the subsurface of the geographic region. These interbed multiples may be referred to as noise in the seismic data. Some of these multiple noises may have apparent dip similar to that of the primary seismic event (apparent dip) and thus may not be suppressed or distinguished by stacking or migration operators. In some cases, such as in land seismic surveys, seismic noise may also include effects from imperfect static or near-surface models, irregular shot (shot) and receiver distributions, and near-surface diffraction and scattering.

In some implementations, seismic multiples noise may be suppressed based on a prediction of multiple arrivals (multiple arrivals) which are then subtracted from the acquired data. These prediction and subtraction techniques may be applicable to surface-related multiples in deep sea marine data due to good data quality and relatively large apparent dip discrimination between multiple events and major events. On the other hand, due to near-surface geological complexity in some geographic regions, the prediction and subtraction schemes may not work well with terrestrial seismic data. As a result, the final seismic image from the stack or migration typically includes a significant amount of coherent noise. In some cases, the noise may be so strong that the actual seismic reflections may be compromised, thereby making seismic interpretation difficult to understand.

Processing difficulties arise when the actual seismic event is very poorly inclined from the noise. In this case, frequency-wave (F-K) filtering or other noise removal methods (e.g., median filters) may not be able to separate the noise from the dominant events.

The noise suppression processing may be performed on pre-stack data or post-stack data. In pre-stack data applications, seismic events in shot gathers or CDP gathers may be lined up and flattened. Data randomization and noise suppression processing may be performed to suppress noise. However, the effect of randomizing the prestack data depends on the accuracy of the Normal Moveout (NMO) speed used in the process. If the NMO velocities are not accurate, the randomization may not align the coherent seismic events and the noise may not be effectively suppressed.

In post-stack data applications, operations are applied to the final image of the seismic data. For example, the data may be NMO stacking, pre-stack time migration, or pre-stack depth migration results. The interpreter identifies events on the data as primary reflections or noise (e.g., multiple reflections). The identification may be based on general geological knowledge, known regional geological properties, or existing well information. These events can also be trial interpretation scenes (trial interpretation scenes) of the interpreter, when the interpreter cannot unambiguously identify the events due to data quality or lack of other information.

Once seismic events are identified and mapped, the seismic data is vertically compressed and stretched to flatten the identified events, and then the flattened data is sequentially randomized. The coherence of seismic events parallel to the identified event is preserved while the coherence of other events is reduced. Coherent events can then be extracted while filtering can be used to suppress other events. These other events may also be obtained by performing a subtraction operation between the original data and the filtered data after randomization and noise suppression for further analysis.

FIG. 1 illustrates an example seismic data noise suppression process 100 according to an implementation. For clarity of presentation, the following description generally describes process 100 in the context of fig. 2-6. However, it should be understood that process 100 may be performed, for example, by any other suitable system, environment, software, and hardware, or combination of systems, environments, software, and hardware, as appropriate. In some cases, process 100 may be performed on a large-scale cluster of computers, a supercomputer, or any other computing device or collection of computing devices. In some implementations, the various steps of process 500 may be performed in parallel, combined, in a loop, and/or in any order.

At 102, a seismic data set associated with a subsurface region is received at a data processing device. The seismic data set may be a set of receiver signal data acquired for a subsurface region. In some cases, during seismic data acquisition, the source (explosive, vibrating truck, air gun array, etc.) is activated and reflections/refractions/transmissions from subsurface geological boundaries are recorded by receiver devices located on the earth's surface. This type of acquisition is repeated for each shot sequentially or simultaneously until all the seismic data has been acquired for the survey area. These acquired seismic data are included in the receiver signal data. In some cases, the acquired seismic data is collected on-site, transmitted to an office (stored and transmitted via a computer network, a physical network, or a combination thereof), and used as input to a computing device that performs the process 100.

In some cases, the seismic data set may be post-stack data. For example, the seismic data may be a final image of the seismic data. Examples of post-stack data include NMO stacking, pre-stack time migration results, and pre-stack depth migration results.

At 104, the seismic data set is flattened from the one or more identified seismic events. Seismic planarization is an explanatory technique for removing structures such as folds or faults to reveal the original subsurface structure as the geological layers are deposited. Flattening helps the interpreter to identify geologic features based on a horizon (horizon). In interpretation, one or more seismic events may be identified by an interpreter, an interpretation tool, or a combination thereof. The seismic data set may be flattened based on the identified events. Examples of identified events may include a major break, where events on both sides of the break may be matched explicitly. To flatten the data based on events, a trace location is selected as an anchor point. All other seismic traces will be adjusted according to the anchor trace. The seismic traces may be compressed or stretched so that all identified events are aligned horizontally. Compression and stretching are achieved by using the original unplanarized data to interpolate data between adjacent identified events.

At 106, the flattened data is divided into a plurality of overlapping spatial windows. If the size of each window is small, the coherent events in each window may be linear or nearly linear and the amplitude of the events does not vary significantly. The optimal window size varies from data to data. In some cases, a test may be performed to determine the window size.

At 108, the seismic data sets are rearranged according to a random order sequence. In one implementation, the data in each spatial window may be indexed based on the spatial distance corresponding to the spatial window. A random order may be generated. The data in each spatial window may be reallocated according to a randomized order. In one example, the randomization order is [4,7,3,1,2,6,5], and the seismic data set is randomized based on spatial distance in the horizontal direction. In this example, each spatial window is assigned an index from 1 to 7 based on the horizontal distance of the spatial window. During sequential randomization, data in a spatial window with spatial distance indexed 4 will be reassigned to the first position, followed by data in a spatial window with spatial distance indexed 7, followed by data in a spatial window with spatial distance indexed 3, and so on.

At 110, the rearranged seismic data is filtered. In some cases, a median filter may be applied to extract coherent horizon events. The median filter window size is typically much smaller than the data randomization window size. In some cases, a test may be performed to determine the median filter window size. FK filtering can also be used to extract horizon events by keeping only around the zero-K component. In some cases, a test may be performed to determine the FK filter window size. Other techniques for extracting linear events with known tilt may also be employed.

At 112, the filtered data is reorganized based on its pre-randomization order. In the example given at step 108, after filtering, the data in the spatial window with spatial distance indexed 4 is returned from the first location to the fourth location.

In some cases, an image of the subsurface region may be generated based on the reorganized data. The images may be output by the data processing apparatus, sent to a different data processing apparatus, or a combination thereof.

FIG. 2 illustrates an example effect of seismic data noise suppression processing according to an implementation. FIG. 2 includes composite images 202, 204, 206, and 208. Image 202 shows an example collection of synthetic seismic data that has been flattened for horizon events identified based on interpretation. As shown, image 202 illustrates a flattened horizon event 210 and two other events 212 and 214. Event 212 has a small tilt to the right and event 214 has a small tilt to the left. Since the difference between the tilts is small, direct filtering may not be effective in distinguishing these events.

Image 204 shows the effect of randomizing the seismic data. Here, the seismic data in image 202 are small windows randomly rearranged in the horizontal direction. The interpretation-based identified horizon events are not affected by randomization, but destroy the coherence of the tilt events. Image 206 shows the noise suppression effect after filtering the seismic data in image 204 using a value filter and reorganizing it according to its original horizontal position. Here, identified horizon events are not affected, but dip events are suppressed to a large extent. Image 208 shows the result of F-K filtering image 204 after the filtered traces have returned to their original positions. The noise residual in image 208 is different from the noise residual in image 206.

In some cases, as shown in images 206 and 208, after filtering and reorganization, there may be residual energy due to the tilt event. However, the residual energy is discontinuous (as shown in image 206) or weaker (as shown in image 208). Additional filtering processes may be used to further suppress residual energy. As shown in images 206 and 208, the noise suppression process successfully suppresses the tilt coherent noise while leaving the horizon events identified in the interpretation unaffected. In some cases, more than one interpretation may be performed, each interpretation may identify a different event. The noise suppression process discussed herein may be applied in an iterative process based on different interpretations. In each iteration, the seismic data is flattened according to a particular interpretation according to the identified event. These processes may generate clearer seismic images based on the selection of the interpreter and help the interpreter test different interpretation scenarios.

FIG. 3 illustrates an example pre-stack time-shifted (migrated) seismic section according to an implementation. As shown in fig. 3, coherent noise interferes with the primary reflections, which makes interpretation difficult.

In some cases, assumptions may be made based on geological and well information, assuming that there may be no such drastic formation changes and fault activity in the region. Thus, the rough appearance of the break-out in FIG. 3 may be generally due to the presence of coherent noise. In these cases, the seismic data may be flattened based on the primary seismic reflection events identified in the interpretation. Other reflection events parallel to the primary seismic reflection event may also be flattened. In the example shown, seismic events of 1.5 seconds or less are used to flatten the fracture because they are continuous and coherent. Then, a randomization and filtering process is applied to the flattened data and the flattening is removed. FIG. 4 illustrates example results of filtered seismic data according to an implementation. As shown in fig. 4, most horizons are continuous and conform to local geological conditions. The interpreter can easily track seismic events and derive geological structures across the fracture surface using new seismic images and well control techniques for the area.

In some cases, the suppressed events may be obtained by subtracting suppressed data (e.g., the data shown in FIG. 4) from unsuppressed data (e.g., the data shown in FIG. 3). FIG. 5 illustrates an example comparison between an original seismic input and an extracted filtered seismic section according to an implementation. As shown, the differences between the data primarily include piecewise events corresponding to noise on the original offset image. The differences may be analyzed, for example, by an interpreter, to determine whether the events reflect noise in the seismic data.

FIG. 6 is a high-level architectural block diagram of a geophysical imaging system according to an implementation. From a high level, the illustrated system 600 includes a geophysical image processing computer 602 coupled to a network 630. The depicted illustration is only one possible implementation of the described subject matter and is not intended to limit the disclosure to this single depicted implementation. Those of ordinary skill in the art will appreciate the fact that the described components may be connected, combined, or used in an alternative manner in light of the present disclosure.

Network 630 facilitates communication between computer 602 and other components (e.g., components that obtain observations of locations and send observations to computer 602). The network 630 may be a wireless or wired network. The network 630 may also be a memory pipe, a hardware connection, or any internal or external communication path between components.

The computer 602 comprises a computing system configured to perform a method as described herein. In some cases, the algorithms of the method may be implemented in executable computing code (e.g., C/C + + executable code). In some cases, computer 602 may include a stand-alone Linux system running a batch application. In some cases, computer 602 may comprise a mobile or personal computer having sufficient memory size to process each piece of geophysical data. The computer 602 may be used to implement the noise suppression process discussed in this disclosure.

The computer 602 may include a computer including input devices (e.g., a keypad, keyboard, touch screen, microphone, voice recognition device, other devices capable of accepting user information) and/or output devices that communicate information associated with the operation of the computer 602, including digital data, visual and/or audio information, or a GUI.

Computer 602 may serve as a client, a network component, a server, a database or other persistent device, and/or any other component of system 600. In some implementations, one or more components of the computer 602 may be configured to operate in a cloud-computing-based environment.

At a high level, the computer 602 is an electronic computing device operable to receive, transmit, process, store, or manage data and information associated with the system 600. According to some implementations, the computer 602 may also include or be communicatively coupled to application servers, email servers, Web servers, cache servers, streaming data servers, Business Intelligence (BI) servers, and/or other servers.

The computer 602 may receive requests from client applications (e.g., an application executing on another computer 602) over the network 630 and respond to the received requests by processing the requests in an appropriate software application. Further, requests may also be sent to the computer 602 from internal users (e.g., from a command console or by another suitable access method), external or third parties, other automation applications, and any other suitable entity, person, system, or computer.

Each component of the computer 602 may communicate using a system bus 603. In some implementations, any and/or all of the components (both hardware and/or software) of the computer 602 can interface with each other and/or with the interface 604 via the system bus 603 using an Application Programming Interface (API)612 and/or a services layer 613. The API 612 may include specifications for routines, data structures, and object classes. The API 612 may be independent or dependent on the computer language and involve a complete interface, a single function, or even a collection of APIs. Service layer 613 provides software services to computer 602 and/or system 600. The functionality of the computer 602 may be accessible to all service consumers using the service layer. Software services (e.g., provided by the services layer 613) provide reusable, defined business functions through defined interfaces. For example, the interface may be software written in JAVA, C + +, or other suitable language that provides data in an extensible markup language (XML) format or other suitable format. While shown as an integrated component of computer 602, alternative implementations may show API 612 and/or service layer 613 as separate components in relation to computer 602 and/or other components of system 600. Further, any or all portions of the API 612 and/or the service layer 613 may be implemented as sub-modules or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of this disclosure.

The computer 602 includes an interface 604. Although shown in fig. 6 as a single network interface 604, two or more interfaces 604 may be used according to particular needs, desires, or particular implementations of the computer 602 and/or system 600. Computer 602 uses interface 604 for communicating with other systems (including within system 600) in a distributed environment, connected to network 630 (whether shown or not). In general, the interface 604 comprises logic encoded in software and/or hardware in a suitable combination and operable to communicate with the network 630. More specifically, interface 604 may include software that supports one or more communication protocols associated with communications such that network 630 or the interface's hardware is operable to communicate physical signals both internal and external to system 600 as shown.

The computer 602 includes a processor 605. Although illustrated in fig. 6 as a single processor 605, two or more processors may be used depending on the particular needs, desires, or particular implementation of the computer 602 and/or system 600. In general, the processor 605 executes instructions and manipulates data to perform the operations of the computer 602. In particular, the processor 605 performs the functions required for processing geophysical data.

The computer 602 also includes a memory 606, the memory 606 holding data for the computer 602 and/or other components of the system 600. Although illustrated in fig. 6 as a single memory 606, two or more memories may be used depending on the particular needs, desires, or particular implementation of the computer 602 and/or system 600. While memory 606 is shown as an integrated component of computer 602, in alternative implementations, memory 606 may be external to computer 602 and/or system 600.

The application 607 is an algorithmic software engine that provides functionality, particularly with respect to functionality required to process geophysical data, according to particular needs, or particular implementations of the computer 602 and/or system 600. For example, the application 607 may serve as one or more of the components/applications described in fig. 1-5. Further, although shown as a single application 607, the application 607 may be implemented as multiple applications 607 on the computer 602. Further, although shown as being integrated with computer 602, in alternative implementations, application 607 may be external to computer 602 and/or system 600.

There may be any number of computers 602 associated with or external to the system 600 and communicating via the network 630. Moreover, the terms "client," "user," and other suitable terms may be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Further, the present disclosure contemplates that many users can use one computer 602, or that one user can use multiple computers 602.

Implementations of the described subject matter may include one or more features, either alone or in combination. For example, in a first implementation, a computer-implemented method for suppressing noise in seismic data includes: receiving, at a data processing device, a set of seismic data associated with a subsurface region; flattening, by the data processing device, the seismic data set according to the identified seismic event; dividing, by a data processing device, a seismic data set into a plurality of spatial windows; randomizing, by the data processing device, the seismic data set according to a random order sequence; filtering the randomized seismic data by a data processing device; and reorganizing, by the data processing device, the filtered seismic data according to the order prior to randomization.

The previously and other described implementations may each optionally include one or more of the following features:

the first feature may be combined with any feature wherein the seismic data is post-stack data.

The second feature may be combined with any previous or subsequent feature, wherein the seismic data includes at least one of a pre-stack time migration result or a pre-stack depth migration result.

Third features that may be combined with any previous or following features, wherein the identified seismic event is identified from an interpretation of the subsurface region.

The fourth feature, which may be combined with any of the previous or following features, further comprises generating, by the data processing apparatus, an image of the subsurface region based on the reorganized data.

The fifth feature, which may be combined with any of the previous or following features, further comprises subtracting the reorganized set of seismic data from the seismic data by the data processing device.

The sixth feature, which may be combined with any of the previous or following features, further comprises filtering the reorganized seismic data by the data processing device.

In a second implementation, a non-transitory computer-readable medium storing instructions that, when executed, cause a computing device to perform operations comprising: receiving, at a data processing device, a set of seismic data associated with a subsurface region; flattening, by the data processing device, the seismic data set according to the identified seismic event; dividing, by a data processing device, a seismic data set into a plurality of spatial windows; randomizing, by the data processing device, the seismic data set according to a random order sequence; filtering the randomized seismic data by a data processing device; and reorganizing, by the data processing device, the filtered seismic data according to the order prior to randomization.

The previously and other described implementations may each optionally include one or more of the following features:

the first feature may be combined with any feature wherein the seismic data is post-stack data.

The second feature may be combined with any previous or subsequent feature, wherein the seismic data includes at least one of a pre-stack time migration result or a pre-stack depth migration result.

Third features that may be combined with any previous or following features, wherein the identified seismic event is identified from an interpretation of the subsurface region.

A fourth feature which may be combined with any of the previous or following features, the operations further comprising generating, by the data processing apparatus, an image of the subsurface region based on the reorganized data.

A fifth feature which may be combined with any of the previous or following features, the operations further comprising subtracting the reorganized set of seismic data from the seismic data by the data processing device.

A sixth feature which may be combined with any preceding or subsequent feature, the operations further comprising: the reorganized seismic data is filtered by the data processing device.

In a third implementation, an apparatus includes: at least one hardware processor; and a non-transitory computer-readable storage medium coupled to the at least one hardware processor and storing programming instructions for execution by the at least one hardware processor, wherein the programming instructions, when executed, cause the at least one hardware processor to perform operations comprising: receiving, at least one hardware processor, a set of seismic data associated with a subsurface region; flattening, by at least one hardware processor, the seismic data set according to the identified seismic events; dividing, by at least one hardware processor, a seismic data set into a plurality of spatial windows; randomizing, by at least one hardware processor, the seismic data set according to a random order sequence; filtering, by at least one hardware processor, the randomized seismic data; and reorganizing, by the at least one hardware processor, the filtered seismic data according to the order prior to randomization.

The previously and other described implementations may each optionally include one or more of the following features:

the first feature may be combined with any feature wherein the seismic data is post-stack data.

The second feature may be combined with any previous or subsequent feature, wherein the seismic data includes at least one of a pre-stack time migration result or a pre-stack depth migration result.

Third features that may be combined with any previous or following features, wherein the identified seismic event is identified from an interpretation of the subsurface region.

A fourth feature which may be combined with any of the previous or following features, the operations further comprising generating, by the data processing apparatus, an image of the subsurface region based on the reorganized data.

A fifth feature which may be combined with any of the previous or following features, the operations further comprising subtracting the reorganized set of seismic data from the seismic data by the data processing device.

A sixth feature which may be combined with any preceding or subsequent feature, the operations further comprising filtering, by the data processing apparatus, the reorganized seismic data.

Implementations of the subject matter and the functional operations described in this specification can be realized in the form of: digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware, including the structures disclosed in this specification and their structural equivalents, or combinations of one or more of them. Implementations of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory computer-readable storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively or additionally, program instructions may be encoded in/on an artificially generated propagated signal (e.g., a machine-generated electrical, optical, or electromagnetic signal) that is generated to encode information for transmission to suitable receiver apparatus for execution by data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer storage media.

The terms "real-time," "real-time (rapid) (RFT)," near real-time (NRT), "near real-time," or similar terms (as understood by one of ordinary skill in the art) mean that the actions and responses are close in time such that the personally perceived actions and responses occur substantially simultaneously. For example, the time difference of response to the display of data (or for initiating the display) after the individual has made an action to access the data may be less than 1ms, less than 1 second, or less than 5 seconds. Although the requested data need not be displayed (or launched for display) on-the-fly, the data is displayed (or launched for display) without any intentional delay, taking into account the processing limitations of the described computing system and the time required to, for example, collect, accurately measure, analyze, process, store, or transmit the data.

The terms "data processing apparatus," "computer," or "electronic computer device" (or equivalents thereof as understood by those of ordinary skill in the art) refer to data processing hardware and include various apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. An apparatus may also be, or may also include, special purpose logic circuitry, e.g., a Central Processing Unit (CPU), an FPGA (field programmable gate array), or an ASIC (application-specific integrated circuit). In some implementations, the data processing apparatus or dedicated logic circuit (or a combination of the data processing apparatus or dedicated logic circuit) may be hardware-based or software-based (or a combination of hardware-based and software-based). Alternatively, the apparatus may comprise code that creates an execution environment for the computer program, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of execution environments. The present disclosure contemplates the use of data processing apparatus with or without a conventional operating system (e.g., LINUX, UNIX, WINDOWS, MAC OS, ANDROID, IOS, or any other suitable conventional operating system).

A computer program can be written in any form of programming language, which can also be referred to or described as a program, software application, module, software module, script, or code, including compiled or interpreted languages, or declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. The computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network. While the portions of the program shown in the figures are illustrated as separate modules implementing various features and functions through various objects, methods, or other processes, the program may instead include multiple sub-modules, third party services, components, libraries, and the like, as the case may be. Rather, the features and functionality of the various components may be combined into a single component, as the case may be. The threshold value for making the calculation determination may be determined statistically, dynamically, or both.

The methods, processes, or logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The methods, processes, or logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., a CPU, FPGA, or ASIC.

A computer suitable for executing a computer program may be based on a general purpose or special purpose microprocessor, both, or any other type of CPU. Generally, a CPU will receive instructions and data from a read-only memory (ROM) or a Random Access Memory (RAM) or both. The essential elements of a computer are a CPU for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, the computer need not have these devices. Further, a computer may be embedded in another device, e.g., a mobile telephone, a Personal Digital Assistant (PDA), a mobile audio or video player, a game player, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a Universal Serial Bus (USB) flash drive), to name a few.

Computer-readable media (whether transitory or non-transitory as appropriate) suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices (e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices); magnetic disks (e.g., internal hard disks or removable disks); magneto-optical disks; and CD ROM, DVD +/-R, DVD-RAM and DVD-ROM disks. The memory may store various objects or data, including: caches, classes (classes), frames, applications, backup data, jobs, web pages, web page templates, database tables, knowledge bases storing dynamic information, and any other suitable information including any parameters, variables, algorithms, instructions, rules, constraints, or references thereto. Further, the memory may include any other suitable data, such as logs, policies, security or access data, reporting files, and so forth. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, the subject matter described in this disclosure can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube), LCD (liquid crystal display), LED (light emitting diode), or plasma monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse, a trackball, or a trackpad) by which the user can provide input to the computer. Touch screens (such as tablet computer surfaces with pressure sensitivity, multi-touch screens using capacitive or electrical sensing, or other types of touch screens) can also be used to provide input to a computer. Other types of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including acoustic, speech, or tactile input. Further, the computer may interact with the user by sending and receiving documents to and from the device used by the user; for example, a user is interacted with by sending a web page to a web browser on a user client device in response to a request received from the web browser.

The term "graphical user interface" or GUI may be used in the singular or plural to describe one or more graphical user interfaces and each display of a particular graphical user interface. Thus, the GUI may represent any graphical user interface, including but not limited to a web browser, touch screen, or Command Line Interface (CLI) that processes information and effectively presents the results of the information to the user. In general, a GUI may include a number of User Interface (UI) elements, some or all of which are associated with a web browser, such as interactive fields, drop-down lists, and buttons. These and other UI elements may be related to or represent functionality of a web browser.

Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a client computer having a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described in this specification), or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of wired or wireless digital data communication (or combination of data communication), e.g., a communication network. Examples of communication networks include a Local Area Network (LAN), a Radio Access Network (RAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a network (WLAN) using, for example, 802.11a/b/g/n or 802.20 (or a combination of 802.11x and 802.20 or other protocols consistent with this disclosure), all or a portion of the internet, or any other communication system (or combination of communication networks) at one or more locations. The network may transport, for example, Internet Protocol (IP) packets, frame relay frames, Asynchronous Transfer Mode (ATM) cells, voice, video, data, or other suitable information (or combination of communication types) between network addresses.

The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as descriptions of features specific to particular implementations of particular inventions. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination. Furthermore, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

Particular implementations of the present subject matter have been described. Other implementations, changes, and substitutions to the described implementations are apparent to those of skill in the art and are within the scope of the claims that follow. Although the operations are described in the drawings and claims in a particular order, this should not be understood as: it may be desirable to perform the operations in the particular order shown, or in sequential order, or to perform all of the operations shown (some of which may be viewed as optional) in order to achieve the desired results. In some cases, multitasking or parallel processing (or a combination of multitasking and parallel processing) may be advantageous and performed as appropriate.

Moreover, the separation or integration of various system modules and components in the foregoing implementations should not be understood as requiring such separation or integration in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Accordingly, the foregoing example implementations do not define or limit the present disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of this disclosure.

Moreover, any claimed implementation is considered to be suitable for at least one computer-implemented method; a non-transitory computer-readable medium storing computer-readable instructions for performing a computer-implemented method; and a computer system comprising computer memory interoperably coupled with a hardware processor configured to perform a computer-implemented method or instructions stored on a non-transitory computer-readable medium.