Seismic data compression method and device, electronic equipment, storage medium and product

Abstract

The application discloses a compression method and device of seismic data, electronic equipment, storage medium and product. The method comprises the following steps: extracting pre-stack seismic data into common offset data; performing discrete wavelet transform on the common offset data to obtain transformed data; performing multi-level coding on the transformed data based on a multi-level tree set splitting coding algorithm to obtain compressed data, wherein the coding level number of the multi-level coding includes an integer from a high coding level number to a maximum coding level number, the maximum coding level number is the total number of thresholds that can be used to determine the importance of wavelet coefficients, and the high coding level number is an integer greater than 1 and less than the maximum coding level number. The above scheme realizes seismic data compression by performing multi-level coding on the wavelet domain data of the seismic data through the multi-level tree set splitting coding algorithm, can guarantee the compression quality of the seismic data, and starts positioning and coding of the data from the high coding level number, so that the total coding level number is reduced, and the compression efficiency of the seismic data is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, storage medium, and product for compressing seismic data. Background Technology

[0002] With the rapid development of petroleum geophysical exploration technology and the increasing demands for exploration accuracy, seismic exploration instruments are also moving towards multi-dimensional, multi-component, multi-parameter, and high-resolution capabilities. Consequently, the amount of seismic exploration data is growing exponentially. Faced with the current situation of real-time data recovery from massive amounts of data and limitations in channel bandwidth transmission, improving the real-time data transmission capability of seismic exploration systems is a major bottleneck restricting production efficiency.

[0003] Seismic data compression and reconstruction techniques offer an effective solution to this problem. Seismic data compression is a key technology for handling the transmission and storage of massive amounts of seismic data. Data compression can reduce the amount of data at the source of transmission, improve real-time data processing speed, and save storage space.

[0004] Seismic exploration data compression methods can be divided into two main categories based on whether or not information is lost: lossless compression and lossy compression. Lossless compression can ensure that the decompressed data is exactly the same as the original data, and is particularly suitable for compressing seismic data that still needs further processing after decompression. However, its compression efficiency is low, and the compression ratio is generally less than 2. Lossy compression can achieve high compression ratios while allowing a small amount of information loss, but it has problems such as information loss. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, storage medium, and product for compressing seismic data to improve the compression quality and efficiency of seismic data.

[0006] In a first aspect, embodiments of this application provide a method for compressing seismic data, including:

[0007] Pre-stack seismic data is extracted into common offset data;

[0008] Perform discrete wavelet transform on the common offset data to obtain the transformed data;

[0009] The transformed data is encoded using a multi-level tree set split coding algorithm to obtain compressed data. The number of coding levels in the multi-level coding includes integers from the highest coding level to the maximum coding level. The maximum coding level is the total number of thresholds that can be used to determine the importance of wavelet coefficients. The highest coding level is an integer greater than 1 and less than the maximum coding level.

[0010] Secondly, embodiments of this application also provide a seismic data compression apparatus, comprising:

[0011] The extraction module is used to extract pre-stack seismic data into common offset data;

[0012] The transformation module is used to perform discrete wavelet transform on the common offset data to obtain the transformed data.

[0013] The encoding module is used to perform multi-level encoding on the transformed data based on a multi-level tree set split encoding algorithm to obtain compressed data. The number of encoding levels in the multi-level encoding includes integers from the high-order encoding level to the maximum encoding level. The maximum encoding level is the total number of thresholds that can be used to determine the importance of wavelet coefficients. The high-order encoding level is an integer greater than 1 and less than the maximum encoding level.

[0014] Thirdly, embodiments of this application provide an electronic device, including:

[0015] One or more processors;

[0016] Storage device for storing one or more programs;

[0017] When the one or more programs are executed by the one or more processors, the one or more processors implement the seismic data compression method as described in the first aspect.

[0018] Fourthly, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method for compressing seismic data as described in the first aspect.

[0019] Fifthly, embodiments of this application also provide a computer program product, including a computer program and / or instructions, which, when executed by a processor, implement the seismic data compression method as described in any of the above embodiments.

[0020] This application provides a method, apparatus, electronic device, storage medium, and product for compressing seismic data. The method includes: extracting pre-stack seismic data into common offset data; performing discrete wavelet transform on the common offset data to obtain transformed data; and performing multi-level encoding on the transformed data based on a multi-level tree ensemble split coding algorithm to obtain compressed data. The number of encoding levels in the multi-level encoding includes integers from the highest encoding level to the maximum encoding level, where the maximum encoding level is the total number of thresholds that can be used to determine the importance of wavelet coefficients, and the highest encoding level is an integer greater than 1 and less than the maximum encoding level. This technical solution uses a multi-level tree ensemble split coding algorithm to perform multi-level encoding on the wavelet domain data of seismic data, achieving seismic data compression. This ensures the compression quality of the seismic data, and by starting data location and encoding from the highest encoding level, the total number of encoding levels is reduced, improving the compression efficiency of the seismic data. Attached Figure Description

[0021] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0022] Figure 1 A flowchart illustrating a method for compressing seismic data provided in this application embodiment;

[0023] Figure 2 This is a schematic diagram of a single cross-section with common offset provided for one embodiment.

[0024] Figure 3 This is a schematic diagram of a single profile after compressing and decompressing co-offset data, as provided in one embodiment.

[0025] Figure 4 This is a schematic diagram illustrating the difference between a single profile after compression and decompression and a single profile with co-offset distance, provided as an embodiment.

[0026] Figure 5 This is a schematic diagram comparing the compression performance of a conventional SPIHT algorithm and an improved SPIHT algorithm according to one embodiment.

[0027] Figure 6 A schematic diagram of a seismic data compression device provided in an embodiment of this application;

[0028] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0029] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present application, not the entire structure.

[0030] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of these steps can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the steps can be rearranged. The process can be terminated when its operation is complete, but may also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0031] It should be noted that the concepts of "first" and "second" mentioned in the embodiments of this application are only used to distinguish different devices, modules, units or other objects, and are not used to limit the order of functions performed by these devices, modules, units or other objects or their interdependencies.

[0032] Furthermore, the embodiments and features described in this application may be combined with each other, unless otherwise specified.

[0033] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0034] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the relevant content of the solution.

[0035] Figure 1 This is a flowchart illustrating a seismic data compression method provided in an embodiment of this application. This embodiment is applicable to situations involving the compression of seismic data. Specifically, the seismic data compression method can be executed by a seismic data compression device, which can be implemented through software and / or hardware and integrated into an electronic device. The electronic device includes, but is not limited to, devices with data processing capabilities such as computers, smartphones, or servers.

[0036] like Figure 1 As shown, the method specifically includes the following steps:

[0037] S110. Extract pre-stack seismic data into common offset data.

[0038] In this embodiment, pre-stack seismic data mainly refers to the raw data of seismic signals recorded during seismic exploration before stacking processing. This data contains rich seismic information but also has a high noise level. Common offset data mainly refers to seismic wave signals received from the same reflection point during seismic exploration, where the offset (i.e., the distance from the source to the reflection point) is the same. This type of data is mainly used to improve the signal-to-noise ratio and resolution of seismic data.

[0039] S120. Perform discrete wavelet transform on the common offset data to obtain the transformed data;

[0040] Specifically, the principle of Discrete Wavelet Transform (DWT) is mainly based on wavelet analysis, which decomposes a signal into multiple sub-signals. Each sub-signal represents information in a different frequency range of the original signal. These sub-signals are called wavelet coefficients, which are the transformed data. Wavelet coefficients can be represented as a matrix.

[0041] S130. The transformed data is encoded in multiple levels based on a multi-level tree set splitting coding algorithm to obtain compressed data.

[0042] The coding levels of the multi-level coding include integers from the high-order coding level to the maximum coding level, where the maximum coding level is the total number of thresholds that can be used to determine the importance of wavelet coefficients, and the high-order coding level is an integer greater than 1 and less than the maximum coding level.

[0043] The Set Partitioning in Hierarchical Trees (SPIHT) algorithm, also known as a hierarchical tree-based set partitioning algorithm, is an improved version of the Embedded Zerotree Wavelets (EZW) algorithm. Based on wavelet transform of co-offset data, it effectively utilizes the similarity between importance coefficients of sub-bands at different scales. SPIHT constructs wavelets in the spatial domain without relying on Fourier transform. Employing SPIHT during compression, its high peak signal-to-noise ratio (PSNR) ensures the high quality of seismic data reproduction.

[0044] The main idea of ​​the SPIHT algorithm is to compare each wavelet coefficient with some thresholds. If the value is greater than the current threshold, a binary number is output as a flag indicating the importance of the wavelet coefficient. This binary flag is the bitstream generated after encoding the wavelet coefficient. After traversing all the wavelet transform coefficient values, the first-level encoding is completed. The current threshold can be halved, the wavelet coefficients can be scanned again and compared with the updated threshold, and then the corresponding compressed bitstream can be output. This process continues until the threshold becomes 1.

[0045] It should be noted that in the traditional (or conventional) SPIHT algorithm, each bit of the quantized wavelet coefficients needs to be encoded. If there are N thresholds (T1, T2, ..., T...) that can be used to determine the importance of the wavelet coefficients... N If the encoding process is performed in a way that requires N levels (or N times) of coding to obtain the final compressed data, and each level of coding requires wavelet coefficient scanning and quantization coding, then this bit-by-bit coding method requires a large amount of positioning information, resulting in information redundancy and affecting compression efficiency. However, the seismic data compression method in this embodiment can be understood as an improvement on the conventional SPIHT algorithm. By setting the high-bit coding level (denoted as N0), the high-bit coding can be uniformly scanned and positioned in one operation. That is, starting from the high-bit coding level, the N0 level coding, N0+1 level coding, N0+2 level coding, ..., up to the Nth level coding, are performed sequentially, for a total of N-N0+1 levels (or N-N0+1 times) of coding, to obtain the final compressed data, effectively reducing the amount of scanning and positioning information, thereby improving compression efficiency. The high-bit coding level can be determined according to actual needs or through experiments. This embodiment does not impose a specific limitation on it. For example, N0 can be taken as large a value as possible to ensure that the coding result meets the signal-to-noise ratio requirements.

[0046] The seismic data compression method provided in this application extracts pre-stack seismic data into common offset data and performs DWT transformation to improve its sparsity; it adopts a one-step positioning method for multi-level coding based on the high-bit coding level, which effectively reduces the amount of information in the positioning coding process, thereby improving coding efficiency and seismic data compression ratio while maintaining the decompression signal-to-noise ratio and ensuring compression quality.

[0047] In one embodiment, the multi-level tree set split coding algorithm is used to encode the transformed data at multiple levels to obtain compressed data, including: during each level of encoding:

[0048] 1) Determine the threshold corresponding to the current level;

[0049] 2) Determine the importance of wavelet coefficients based on the threshold corresponding to the current level;

[0050] 3) Sort and scan the transformed data according to the unimportant pixel list and the unimportant set list to obtain the important pixel list;

[0051] 4) Quantize and encode the wavelet coefficients corresponding to the important pixel list to obtain the compressed data corresponding to the current level.

[0052] The SPIHT algorithm uses three linked lists to sort and quantize the importance coefficients. Specifically, it uses the List of Insignificant Pixels (LIP) and the List of Insignificant Sets (LIS) to determine importance, and the List of Significant Pixels (LSP) to quantize the importance coefficients. The LIP records the positions of important wavelet coefficients, facilitating binary quantization encoding of the wavelet coefficients at the corresponding positions. The LSP has two types, A and B. For a node (i,j), type A can be transformed into type B by splitting all descendants (D(i,j)) of node (i,j) into all indirect descendants (L(i,j)) and all direct descendants (O(i,j)). Type B can be transformed into type A by splitting all indirect descendants of node (i,j) into all descendants (D(i,j)) of the four nodes (i,j).

[0053] For example, the maximum number of codes is N, and the thresholds that can be used to determine the importance of wavelet coefficients include T1, T2, ..., T. N The high-order coding level is N0, where 1 < N0 < N.

[0054] The process of i-level encoding is as follows:

[0055] 1) Determine the threshold T corresponding to level i encoding. i Where i has an initial value of N0 and a maximum value of N;

[0056] 2) Assume the set to be scanned is represented as p, and the maximum amplitude value in set p corresponds to the wavelet coefficient C of node (i,j). ij Then C ij The importance can be determined by the following formula: That is, when the maximum amplitude value in set p is greater than or equal to the threshold Ti, S n (p) is 1; conversely, S n (p) is 0;

[0057] 3) Sort and scan (locate) the transformed data according to the unimportant pixel list and the unimportant set list. The process is as follows:

[0058] First, encode each node (i,j) in the LIP. If S n If (i,j) = 1, output c. ij If the sign is positive, output 1; if the sign is negative, output 0. Move node (i,j) to the end of the LSP and remove node (i,j) from the LIP; if S n If (i,j) = 0, then no processing is done on node (i,j), and it remains in LIP;

[0059] Then, each node (i,j) in the LIS is encoded. If node (i,j) belongs to type A, then output S. n (D(i,j)); if S n If (D(i,j)) = 1, then output S for all nodes (k,l) in O(i,j). n (k,l); further, if S n If (k,l) = 1, then copy node (k,l) to the end of the LSP and output c. kl The symbol; if S n If (k,l) = 0, then copy node (k,l) to LIP; if L(i,j) is not empty, mark node (i,j) as type B and store it at the end of LIS; otherwise, delete node (i,j) from LIS.

[0060] If node (i,j) belongs to type B, then output S. n (L(i,j)); if S n If (L(i,j)) = 1, then each node (k,l) in O(i,j) is added to LIS with type A, and the node (i,j) in LIS is removed.

[0061] 4) Perform fine-grained quantization encoding on the wavelet coefficients in the LSP. Specifically, for each node (i,j) in the LSP, perform fine-grained quantization encoding on the wavelet coefficient c. ij Fine quantization is performed based on the SPIHT algorithm. The leading zeros and the first one of the binary numbers of important coefficients are not encoded, thus reducing the quantization encoding of each important coefficient by 1 bit, while maintaining high quantization accuracy.

[0062] Based on the compressed data corresponding to the current level obtained by quantization encoding, let i = i + 1, return to step 2), and enter the next level of encoding until the Nth level of encoding is completed, and obtain the compressed data of the seismic data.

[0063] In one embodiment, determining the threshold corresponding to the current level includes:

[0064] The reference threshold is determined based on the maximum absolute value of the wavelet coefficients in the transformed data.

[0065] The threshold corresponding to the current level is determined based on the reference threshold.

[0066] Specifically, the reference threshold can be understood as the threshold when the coding level is 1, assuming the maximum absolute value of the wavelet coefficients is |c ij |, then the reference threshold is If the encoding level is i, then the corresponding threshold is T. i =T1 / 2 i-1 .in, This indicates rounding down to the nearest integer.

[0067] In one embodiment, the method further includes:

[0068] S140. Decode the compressed data to obtain decoded data;

[0069] S150. Perform inverse discrete wavelet transform on the decoded data to obtain decompressed seismic data.

[0070] Specifically, the core of the SPIHT decoding process lies in restoring the ordering (location) information of seismic data and reconstructing the image. The decoding process follows a similar execution path to the encoding process, but in the opposite direction. For example, the decoding process includes:

[0071] 1) Initialization: Based on the initial input parameters, initialize the data structures required for decoding, such as linked lists, queues, etc.

[0072] 2) Recovering the sorting information of the data: The decoder uses the same sorting algorithm as the encoder to recover the sorting information of the data by executing the same path, which mainly depends on the input sorted scan bitstream and fine scan bitstream.

[0073] 3) Image Reconstruction: While restoring the data sorting information, the decoder is responsible for image reconstruction. For the important coefficients that are confirmed to be recovered, the quantization values ​​of the wavelet coefficients are updated through two steps: sorting scan and fine scan, gradually improving the approximation accuracy and the quality of the reconstructed image.

[0074] Based on this, accurate seismic data can be obtained through decompression, ensuring the quality of decompression and improving decompression efficiency.

[0075] Figures 2 to 5 The compression effect of seismic data is shown. (By...) Figure 4 It can be seen that the difference between the single profile of the common offset of a certain actual data before compression and the single profile after compression and decompression based on the improved SPIHT algorithm is very small, and the difference is almost imperceptible when displayed at the same scale. Figure 5 It can be seen that compression using the improved SPIHT algorithm achieves better compression ratio and signal-to-noise ratio than the conventional SPIHT algorithm.

[0076] The seismic data compression method in this embodiment is based on pre-stack common offset seismic data. After performing discrete wavelet transform, the maximum number of encoding levels and the number of high-order encoding levels are set according to data quality and production requirements. The high-order encoding levels are encoded using an improved SPIHT algorithm (one-step localization and quantization of the high-order bits), while the low-order encoding levels are encoded using a conventional SPIHT algorithm (localization and quantization of each bit). This results in compressed data, which can guarantee the compression quality of seismic data and improve the compression efficiency. In the decoding stage, the high-order bits are first fully decoded using the SPIHT algorithm, and then the low-order bits are fully or partially decoded using the SPIHT algorithm. Finally, an inverse discrete wavelet transform is performed to obtain decompressed seismic data, which can guarantee the decompression quality of seismic data and improve decompression efficiency.

[0077] Figure 6 A schematic diagram of a seismic data compression device provided in an embodiment of this application. The seismic data compression device provided in this embodiment includes:

[0078] Extraction module 210 is used to extract pre-stack seismic data into common offset data;

[0079] Transformation module 220 is used to perform discrete wavelet transform on the common offset data to obtain the transformed data;

[0080] The encoding module 230 is used to perform multi-level encoding on the transformed data based on a multi-level tree set split encoding algorithm to obtain compressed data. The number of encoding levels in the multi-level encoding includes integers from the high-order encoding level to the maximum encoding level. The maximum encoding level is the total number of thresholds that can be used to determine the importance of wavelet coefficients. The high-order encoding level is an integer greater than 1 and less than the maximum encoding level.

[0081] This device performs multi-level encoding on wavelet domain data of seismic data using a multi-level tree set split coding algorithm, thereby achieving seismic data compression. It can ensure the compression quality of seismic data, and the data can be located and encoded starting from the highest coding level, reducing the total number of coding levels and improving the compression efficiency of seismic data.

[0082] In one embodiment, the encoding module 230 includes:

[0083] The threshold determination unit is used to determine the threshold corresponding to the current level during each level of encoding.

[0084] The importance determination unit is used to determine the importance of wavelet coefficients based on the threshold corresponding to the current level.

[0085] The sorting and scanning unit is used to sort and scan the transformed data according to the unimportant pixel list and the unimportant set list to obtain the important pixel list.

[0086] The quantization encoding unit is used to quantize and encode the wavelet coefficients corresponding to the important pixel list to obtain the compressed data corresponding to the current level.

[0087] In one embodiment, the quantization encoding unit is specifically used for:

[0088] The reference threshold is determined based on the maximum absolute value of the wavelet coefficients in the transformed data.

[0089] The threshold corresponding to the current level is determined based on the reference threshold.

[0090] The seismic data compression apparatus provided in this application embodiment can be used to execute the seismic data compression method provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0091] Figure 7 A schematic diagram of an electronic device 10, which can be used to implement embodiments of this application, is shown. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 10 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, user equipment, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0092] like Figure 7 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0093] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks and wireless networks.

[0094] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above.

[0095] In some embodiments, the methods described above can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the methods of any of the embodiments described above by any other suitable means (e.g., by means of firmware).

[0096] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0097] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0098] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0099] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 10, which includes: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device 10. Other types of devices can also be used to provide interaction with the 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 can be received in any form (including sound input, voice input, or tactile input).

[0100] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0101] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0102] This application also provides a computer program product, including a computer program and / or instructions, which, when executed by a processor, implement the seismic data compression method as described in any of the above embodiments.

[0103] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0104] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for compressing seismic data, characterized in that, include: Pre-stack seismic data is extracted into common offset data; Perform discrete wavelet transform on the common offset data to obtain the transformed data; The transformed data is encoded using a multi-level tree set split coding algorithm to obtain compressed data. The number of coding levels in the multi-level coding includes integers from the highest coding level to the maximum coding level. The maximum coding level is the total number of thresholds that can be used to determine the importance of wavelet coefficients. The highest coding level is an integer greater than 1 and less than the maximum coding level.

2. The method according to claim 1, characterized in that, The multi-level tree set splitting encoding algorithm performs multi-level encoding on the transformed data to obtain compressed data, including: During each level of encoding process Determine the threshold corresponding to the current level; The importance of wavelet coefficients is determined based on the threshold corresponding to the current level. The transformed data is sorted and scanned according to the unimportant pixel list and the unimportant set list to obtain the important pixel list; The wavelet coefficients corresponding to the important pixel list are quantized and encoded to obtain the compressed data corresponding to the current level.

3. The method according to claim 2, characterized in that, Determine the threshold corresponding to the current level, including: The reference threshold is determined based on the maximum absolute value of the wavelet coefficients in the transformed data. The threshold corresponding to the current level is determined based on the reference threshold.

4. The method according to claim 1, characterized in that, Also includes: The compressed data is decoded to obtain decoded data; The decoded data is subjected to inverse discrete wavelet transform to obtain the decompressed seismic data.

5. A seismic data compression device, characterized in that, include: The extraction module is used to extract pre-stack seismic data into common offset data; The transformation module is used to perform discrete wavelet transform on the common offset data to obtain the transformed data. The encoding module is used to perform multi-level encoding on the transformed data based on a multi-level tree set split encoding algorithm to obtain compressed data. The number of encoding levels in the multi-level encoding includes integers from the high-order encoding level to the maximum encoding level. The maximum encoding level is the total number of thresholds that can be used to determine the importance of wavelet coefficients. The high-order encoding level is an integer greater than 1 and less than the maximum encoding level.

6. The apparatus according to claim 5, characterized in that, The encoding module includes: The threshold determination unit is used to determine the threshold corresponding to the current level during each level of encoding. The importance determination unit is used to determine the importance of wavelet coefficients based on the threshold corresponding to the current level. The sorting and scanning unit is used to sort and scan the transformed data according to the unimportant pixel list and the unimportant set list to obtain the important pixel list. The quantization encoding unit is used to quantize and encode the wavelet coefficients corresponding to the important pixel list to obtain the compressed data corresponding to the current level.

7. The apparatus according to claim 6, characterized in that, The quantization encoding unit is specifically used for: The reference threshold is determined based on the maximum absolute value of the wavelet coefficients in the transformed data. The threshold corresponding to the current level is determined based on the reference threshold.

8. An electronic device, characterized in that, include: At least one processor; A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the seismic data compression method as described in any one of claims 1-4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the seismic data compression method as described in any one of claims 1-4.

10. A computer program product comprising a computer program and / or instructions, characterized in that, When the computer program and / or instructions are executed by the processor, they implement the seismic data compression method as described in any one of claims 1-4.

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