A joint first arrival travel time tomography method and system based on streamer and OBN data

Abstract

The present invention relates to a combined first-arrival traveltime tomography method and system based on streamer and OBN data, belonging to the technical field of seismic exploration modeling. The method addresses the problems of low accuracy, insufficient data utilization, and inapplicability of existing models to complex geological structures in the prior art. The method comprises: obtaining raw streamer seismic data and raw OBN seismic data from a target work area; after preprocessing, calculating the streamer traveltime and OBN traveltime of each detector point; constructing an initial seismic velocity model for the target work area and iteratively updating it; in each iteration, obtaining streamer deviations based on the initial seismic velocity model and streamer traveltimes, and obtaining OBN deviations based on the initial seismic velocity model and OBN traveltimes; calculating a slowness correction based on the streamer deviations and OBN deviations; and updating the slowness of the initial seismic velocity model based on the slowness corrections to obtain an updated initial seismic velocity model, which serves as the initial seismic velocity model for the next iteration.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic exploration modeling, and in particular to a combined first-arrival travel time tomography method and system based on streamer and OBN data. Background Art

[0002] With the growing demand for marine resource exploration and the advancement of exploration technology, accurate information on the marine subsurface geological structure is crucial for the exploration and development of resources such as oil and natural gas. In the field of marine seismic exploration, streamer and ocean bottom node (OBN) technologies are two important data acquisition methods. However, the data collected by either method has limitations when used alone, and existing methods have many problems when processing the combined streamer and OBN data.

[0003] On the one hand, when constructing seismic velocity models, due to the failure to fully integrate the respective strengths of streamer and OBN data during model construction, they have poor adaptability to complex geological structures and are often inaccurate. Under complex geological conditions, velocity models cannot accurately depict changes in subsurface velocity, resulting in significant deviations in subsequent analysis results based on the model and affecting an accurate understanding of the geological structure. On the other hand, during the iterative updating of velocity models, complex geological structures can severely impact imaging performance. Due to velocity model accuracy issues, accuracy and efficiency are difficult to guarantee when updating velocity models to accommodate complex geological conditions. For example, in areas with complex geological structures, velocity models may not accurately reflect the heterogeneity and anisotropy of the strata, resulting in calculated deviations that do not truly reflect the actual situation and ultimately poor imaging results. Furthermore, streamer and OBN data suffer from insufficient data utilization, failing to fully exploit the geological information hidden within the data. This results in the constructed velocity models and imaging results failing to meet the requirements of high-precision exploration. Summary of the Invention

[0004] In view of the above analysis, the embodiments of the present invention aim to provide a joint first arrival travel time tomography method and system based on streamer and OBN data to solve the problems of low accuracy, insufficient data utilization and inapplicability of existing models to complex geological structures.

[0005] In a first aspect, an embodiment of the present invention provides a joint first arrival travel time tomography method based on streamer and OBN data, the method comprising the following steps:

[0006] Obtain the original streamer seismic data of the target work area, and after preprocessing, pick the first arrival time of the streamer seismic data of each detection point from the original streamer seismic data;

[0007] Obtain the original OBN seismic data of the target work area, and after preprocessing, pick out the first arrival time of the OBN seismic data of each receiver point from the original OBN seismic data;

[0008] The streamer travel time of each receiver point is calculated based on the first arrival time of the streamer seismic data and the recorded shot firing time of each receiver point, and the OBN travel time of each receiver point is calculated based on the first arrival time of the OBN seismic data and the recorded shot firing time of each receiver point;

[0009] An initial seismic velocity model of the target work area is constructed, and the initial seismic velocity model is iteratively updated until the iterative convergence conditions are met to obtain the final seismic velocity model. First-arrival travel time tomography of the target work area is performed based on the final seismic velocity model. In each iteration, the streamer deviation is obtained based on the initial seismic velocity model and the streamer travel time, and the OBN deviation is obtained based on the initial seismic velocity model and the OBN travel time. The slowness correction is calculated based on the streamer deviation and the OBN deviation, and the slowness of the initial seismic velocity model is updated based on the slowness correction to obtain the updated initial seismic velocity model as the initial seismic velocity model for the next iteration.

[0010] As a further improvement of the present application, obtaining the streamer deviation based on the initial seismic velocity model and the streamer travel time, and obtaining the OBN deviation based on the initial seismic velocity model and the OBN travel time include:

[0011] Perform path ray tracing on the target work area to obtain the shortest streamer seismic wave propagation path and the shortest OBN seismic wave propagation path corresponding to each detection point;

[0012] Based on the initial seismic velocity model and the shortest streamer seismic wave propagation path and the shortest OBN seismic wave propagation path corresponding to each detection point, the predicted streamer travel time and the predicted OBN travel time corresponding to each detection point are output;

[0013] The difference between the predicted streamer travel time and the streamer travel time at each detection point is calculated to obtain the streamer deviation, and the difference between the predicted OBN travel time and the OBN travel time at each detection point is calculated to obtain the OBN deviation.

[0014] As a further improvement of the present application, path ray tracing is performed on the target work area to obtain the shortest streamer seismic wave propagation path and the shortest OBN seismic wave propagation path corresponding to each detection point, including:

[0015] Gridding the strata of the target work area based on the geological structure information of the target work area to obtain multiple grid cells;

[0016] Based on the initial seismic velocity model, a corresponding velocity value is assigned to each grid cell;

[0017] Calculate the travel time from each grid cell to the adjacent grid cells based on the velocity value assigned to each grid cell;

[0018] Each grid cell is regarded as a node, the connection between adjacent grid cells is regarded as an edge, and the travel time between adjacent grid cells is regarded as the weight of the edge to generate a strip diagram of the target work area;

[0019] The strip diagram of the target work area is processed based on the Dijkstra algorithm to obtain the shortest streamer seismic wave propagation path from the shot point to the streamer detection point and the shortest OBN seismic wave propagation path from the shot point to each OBN detection point.

[0020] As a further improvement of this application, the final seismic velocity model is:

[0021]

[0022] Among them, s min To detect the minimum value of slowness data, s max To detect the maximum value of slowness data, z i is the depth of the geological layer, z min To detect the minimum value of depth data, Δs n is the slowness correction for the nth iteration.

[0023] As a further improvement of the present application, the calculation of the slowness correction based on the streamer deviation and the OBN deviation includes:

[0024] Perform path ray tracing on the initial seismic velocity model to generate the shortest streamer seismic wave propagation path and the shortest OBN seismic wave propagation path;

[0025] Construct the seismic tomography equation, as shown in formula (2);

[0026]

[0027] Where ΔS is the slowness correction, ΔT TS is the towline deviation; ΔT OBN is the OBN deviation, L TS is the seismic wave propagation path of the end streamer, L OBN is the shortest OBN seismic wave propagation path, is the streamer bias weight, is the OBN bias weight, n is the number of iterations;

[0028] The slowness correction is solved based on the seismic tomography equation.

[0029] As a further improvement of the present application, the towline deviation weight is calculated as shown in formula (3);

[0030]

[0031] The OBN deviation weight is calculated as shown in formula (4);

[0032]

[0033] in, is the streamer chi-square value, is the OBN chi-square value.

[0034] As a further improvement of this application, the streamer chi-square value is calculated as shown in formula (5);

[0035]

[0036] Among them, N TS is the number of streamer detection points, σ TS is the streamer standard deviation, k is the streamer detection point number;

[0037] The OBN chi-square value is shown in formula (6);

[0038]

[0039] Among them, N OBN is the number of OBN detection points, σ OBN is the OBN standard deviation, and l is the OBN detection point number.

[0040] As a further improvement of this application, the iterative convergence condition is:

[0041] The streamer chi-square value and the OBN chi-square value are less than or equal to 1 or the preset upper limit of the number of iterations is reached.

[0042] As a further improvement of the present application, the target work area strip diagram is processed based on the Dijkstra algorithm to obtain the shortest streamer seismic wave propagation path from the shot point to the streamer detection point and the shortest OBN seismic wave propagation path from the shot point to each OBN detection point, including:

[0043] For any streamer or OBN receiver, the shortest propagation path from the shot point to the receiver is obtained by the following steps:

[0044] Create a visited node set and an unvisited node set. The initial visited node set includes the gun points, and the initial unvisited node set includes all nodes except the gun points.

[0045] Search the unvisited node set for the node closest to the shot point, remove it from the unvisited node set, and add it to the visited node set; for each node added to the visited node set, continue searching for the node closest to it; repeat this operation until the detection point is added to the visited node set;

[0046] Generate the shortest propagation path from the shot point to the receiver point based on the set of visited nodes.

[0047] In a second aspect, an embodiment of the present invention provides a combined first arrival travel time tomography system based on streamer and OBN data, the system comprising:

[0048] The streamer seismic data acquisition module is used to obtain the original streamer seismic data of the target work area, and after preprocessing, pick up the first arrival time of the streamer seismic data of each detection point from the original streamer seismic data;

[0049] The OBN seismic data acquisition module is used to obtain the original OBN seismic data of the target work area, and after preprocessing, pick up the first arrival time of the OBN seismic data of each detection point from the original OBN seismic data;

[0050] A travel time calculation module for calculating the streamer travel time of each detection point based on the first arrival time of the streamer seismic data and the recorded shot firing time of each detection point, and for calculating the OBN travel time of each detection point based on the first arrival time of the OBN seismic data and the recorded shot firing time of each detection point;

[0051] The model training module is used to construct an initial seismic velocity model for the target work area, iteratively update the initial seismic velocity model until the iterative convergence conditions are met, obtain the final seismic velocity model, and perform first-arrival travel time tomography on the target work area based on the final seismic velocity model; in each iteration, the towline deviation is obtained based on the initial seismic velocity model and the towline travel time, and the OBN deviation is obtained based on the initial seismic velocity model and the OBN travel time; the slowness correction is calculated based on the towline deviation and the OBN deviation, and the slowness of the initial seismic velocity model is updated based on the slowness correction to obtain the updated initial seismic velocity model as the initial seismic velocity model for the next iteration.

[0052] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0053] 1. The present invention obtains the original streamer seismic data and original OBN seismic data of the target work area, and picks the first arrival time of the streamer and OBN seismic data respectively after preprocessing. Then, the travel time is calculated in combination with the shot point firing time. The characteristics of the streamer and OBN data are used to construct an initial seismic velocity model. During the iterative update process, the deviation and slowness correction of the streamer and OBN are calculated to further optimize the velocity model and improve the model accuracy.

[0054] 2. In the process of constructing and iterating the velocity model, the present invention integrates the streamer and OBN data, jointly processes the two types of data, and uses geological structure information when calculating the streamer and OBN deviations and slowness corrections, thereby improving data utilization.

[0055] 3. The present invention constructs a model based on the slowness and depth data of the target work area, and uses the towline and OBN data for iterative updates. It can effectively adapt to complex geological structures, including complex stratigraphic layers, faults, and areas with drastic velocity changes, overcoming the problem that existing technologies are not applicable to complex geological structures.

[0056] In the present invention, the above-mentioned technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of the present invention will be described in the following description, and some advantages will become apparent from the description or be learned through practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. Throughout the drawings, the same reference symbols denote the same components.

[0058] Figure 1 A schematic flow chart of a combined first arrival travel time tomography method based on streamer and OBN data provided in one embodiment of the present invention;

[0059] Figure 2 A target work area model diagram provided by an embodiment of the present invention;

[0060] Figure 3 A schematic diagram of towline-only tomography results provided in one embodiment of the present invention;

[0061] Figure 4 A schematic diagram of the results of a joint first-arrival travel time tomography method based on streamer and OBN data provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0062] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0063] A specific embodiment of the present invention discloses a combined first arrival travel time tomography method based on streamer and OBN data, such as Figure 1 Shown, including:

[0064] S1. Obtain the original streamer seismic data of the target work area, and after preprocessing, pick up the first arrival time of the streamer seismic data of each detection point from the original streamer seismic data;

[0065] Streamer seismic data is seismic wave information collected during marine seismic exploration using streamer equipment. A streamer is a long cable with multiple equally spaced detectors arranged on it. During marine exploration, an exploration vessel tows the streamer across the ocean surface. As seismic waves propagate toward the seabed and underground, they are reflected and refracted by different geological layers. The reflected and refracted waves return to the surface and are received by the detectors on the streamer. These signals are the raw streamer seismic data. The first arrival time of streamer seismic data is the time when a seismic wave is first detected by a detector on the streamer.

[0066] Preprocessing involves denoising the raw streamer seismic data. For example, bandpass filtering is used to remove high- and low-frequency noise from the raw streamer seismic data. High-frequency noise is caused by factors such as electronic interference from the instrument itself, while low-frequency noise comes from environmental factors such as ocean currents and waves.

[0067] S2. Obtain the original OBN seismic data of the target work area, and after preprocessing, pick out the first arrival time of the OBN seismic data of each detection point from the original OBN seismic data;

[0068] OBN seismic data is collected by OBN seismic data acquisition devices deployed at ocean bottom nodes (OBNs). When seismic waves are generated by an earthquake source and propagate to the seafloor, the OBN seismic data acquisition devices receive reflected and refracted waves from various directions underground. Because OBN seismic data acquisition devices are located on the seafloor, closer to the shallower subsurface layers, they are more capable of capturing detailed information about shallow geological structures. The first arrival time of OBN seismic data is the time when the OBN seismic data acquisition device first detects the seismic wave.

[0069] The preprocessing of the original OBN seismic data is the same as the preprocessing of the acquired original streamer seismic data, and will not be described in detail here.

[0070] When acquiring streamer and observable borehole (OBN) seismic data, shotpoints are typically located underwater. The specific depth depends on the exploration objectives and equipment capabilities. In marine seismic exploration, shotpoints are positioned from a few to tens of meters below the water surface to ensure that seismic waves effectively propagate to the target strata. When a shotpoint is activated, it generates strong seismic waves that propagate through the seawater and reach the seafloor. There, the waves encounter strata with varying densities and elastic properties, penetrating these layers and refracting into the subsurface.

[0071] When arranging shot points, the firing direction should be perpendicular or oblique to the stratigraphic direction to generate effective refracted waves. In areas with simpler geology, the shot point spacing can be increased appropriately; in areas with complex geology, the shot point spacing should be reduced to ensure accurate acquisition of refracted wave information. Receiver points should be arranged in a linear or grid pattern, covering the target area. Receiver point spacing is determined based on the expected stratigraphic thickness and velocity variations. In shallow layers and areas with dramatic velocity variations, receiver point spacing is reduced to accurately capture refracted wave signals.

[0072] For example, Figure 2 The target work area is 10 km long horizontally and 5 km deep vertically, with a seawater depth of 0.5 km. When acquiring raw streamer seismic data for the target work area, the distance between shot points is 25 m, the distance between receivers is 12.5 m, and the streamer length is 5000 m. When acquiring raw OBN seismic data for the target work area, the receivers are located at the seabed, the distance between receivers is 200 m, and 51 OBN seismic data acquisition devices are deployed.

[0073] S3, calculating the streamer travel time of each detection point based on the first arrival time of the streamer seismic data and the recorded shot firing time of each detection point, and calculating the OBN travel time of each detection point based on the first arrival time of the OBN seismic data and the recorded shot firing time of each detection point;

[0074] Travel time is the length of time it takes for a seismic wave to travel from a shot point, through various geological media, and reach a detector. Specifically, the streamer travel time is the duration from the time a shot point generates a seismic wave to the time a detector on the streamer receives the first valid seismic wave signal. This is the streamer travel time at that detector. The OBN travel time is the duration from the time a shot point generates a seismic wave to the time an OBN detector receives the first valid seismic wave signal. This is the OBN travel time at that detector.

[0075] S4. Construct an initial seismic velocity model of the target work area based on the geological structure information of the target work area obtained from exploration, iteratively update the initial seismic velocity model until the iterative convergence condition is met to obtain a final seismic velocity model, and perform first-arrival travel time tomography of the target work area based on the final seismic velocity model; in each iteration, obtain the towline deviation based on the initial seismic velocity model and the towline travel time, and obtain the OBN deviation based on the initial seismic velocity model and the OBN travel time; calculate the slowness correction based on the towline deviation and the OBN deviation, and update the slowness of the initial seismic velocity model based on the slowness correction to obtain the updated initial seismic velocity model as the initial seismic velocity model for the next iteration.

[0076] An initial seismic velocity model for the target area is constructed based on the geological structure information of the target area obtained through exploration. This information can be obtained from geological exploration reports or drill hole data. Geological exploration reports include information on stratigraphic distribution, lithologic characteristics, and geological structures (such as faults and folds) within the target area. Drill hole data can reveal lithologic and stratigraphic characteristics at different depths.

[0077] Different geological strata can be identified based on their distribution, lithologic characteristics, and geological structure. Each stratum has a specific seismic wave propagation velocity, and slowness data can be derived based on these different seismic wave propagation velocities. Seismic wave propagation velocities for different rock types can be obtained from geological literature or pre-measured from laboratory tests. Borehole data provides information on the depth of underground rock formations. Analysis of drill core samples can determine the depth distribution of different geological strata.

[0078] The initial seismic velocity model is:

[0079]

[0080] Among them, s min To detect the minimum value of slowness data, s max To detect the maximum value of slowness data, z i is the depth of the geological layer, z min The minimum value of the detected depth data.

[0081] Furthermore, streamer deviations are obtained based on the initial seismic velocity model and the streamer travel time. Obtaining OBN deviations based on the initial seismic velocity model and the OBN travel time includes:

[0082] S401: Perform path ray tracing on the target work area to obtain the shortest streamer seismic wave propagation path and the shortest OBN seismic wave propagation path corresponding to each detection point.

[0083] Ray tracing is a method used in seismic exploration to determine the propagation paths of seismic waves and calculate travel times. First, an initial seismic velocity model is constructed based on the geological structure information of the target area obtained through exploration.

[0084] After the initial model is established, the locations of the source and receiver points for ray tracing can be determined using a seismic monitoring network or a specific survey layout.

[0085] Furthermore, path ray tracing is performed on the target work area to obtain the shortest streamer seismic wave propagation path and the shortest OBN seismic wave propagation path corresponding to each detection point, including:

[0086] Gridding the strata of the target work area based on the geological structure information of the target work area to obtain multiple grid cells;

[0087] Based on the initial seismic velocity model, a corresponding velocity value is assigned to each grid cell;

[0088] The shortest streamer seismic wave propagation path and the shortest OBN seismic wave propagation path corresponding to each detection point are generated based on the Dijkstra algorithm.

[0089] Specifically, the shortest streamer seismic wave propagation path and the shortest OBN seismic wave propagation path corresponding to each detection point are generated based on the Dijkstra algorithm, including:

[0090] By performing path ray tracing on the target work area, the shortest streamer seismic wave propagation path and the shortest OBN seismic wave propagation path corresponding to each detection point are obtained, including:

[0091] Gridding the strata of the target work area based on the geological structure information of the target work area to obtain multiple grid cells;

[0092] Based on the initial seismic velocity model, a corresponding velocity value is assigned to each grid cell;

[0093] Calculate the travel time from each grid cell to the adjacent grid cells based on the velocity value assigned to each grid cell;

[0094] Each grid cell is regarded as a node, the connection between adjacent grid cells is regarded as an edge, and the travel time between adjacent grid cells is regarded as the weight of the edge to generate a strip diagram of the target work area;

[0095] The strip diagram of the target work area is processed based on the Dijkstra algorithm to obtain the shortest streamer seismic wave propagation path from the shot point to the streamer detection point and the shortest OBN seismic wave propagation path from the shot point to each OBN detection point.

[0096] When gridding the target work area, the size and shape of the grid cells are determined based on the target area's scope and the required accuracy. Each grid cell is assigned a corresponding seismic wave propagation velocity value based on the initial seismic velocity model. A grid cell can have four or eight adjacent grid cells, and multiple paths exist from the shot point to the receiver point.

[0097] Furthermore, for any streamer detection point or OBN detection point, the shortest propagation path from the shot point to the detection point is obtained by the following steps:

[0098] Create a visited node set and an unvisited node set. The initial visited node set includes the gun points, and the initial unvisited node set includes all nodes except the gun points.

[0099] Search the unvisited node set for the node closest to the shot point, remove it from the unvisited node set, and add it to the visited node set; for each node added to the visited node set, continue searching for the node closest to it; repeat this operation until the detection point is added to the visited node set;

[0100] Generate the shortest propagation path from the shot point to the receiver point based on the set of visited nodes.

[0101] The Dijkstra algorithm is a method for finding the shortest path between nodes in a weighted graph. When generating the shortest path from a shot point to each receiver point using the Dijkstra algorithm, each grid cell is considered a node in the graph, and the connections between adjacent grid cells are considered edges. The weight of an edge is the travel time between adjacent grid cells. This transforms the grid model of the entire work area into a weighted graph. Each node is assigned two attributes: the distance from the node to the shot point, and the predecessor node information, which indicates the node preceding the current node on the path from the shot point to the current node.

[0102] The target work area strip map is processed using the Dijkstra algorithm to obtain the shortest streamer seismic wave propagation paths from the shot point to the streamer receivers and the shortest OBN seismic wave propagation paths from the shot point to each OBN receiver. Two sets are first created: a visited node set and an unvisited node set. Initially, the visited node set includes only the shot point, while the unvisited node set includes the nodes corresponding to all grid cells other than the shot point. The unvisited node set is searched for the node closest to the shot point, removed from the unvisited node set, and added to the visited node set. Simultaneously, for each neighboring node, the distance from the shot point through this node to the neighboring node is calculated. This distance is then added to the weight of the edge between the node and the neighboring node. If the calculated distance is less than the neighboring node's previously recorded distance to the shot point, the distance from the neighboring node to the shot point is updated to the newly calculated value, and the predecessor node of the neighboring node is marked as the current node. This process of searching the unvisited node set, moving nodes, and updating neighboring node information is repeated until all receivers have been added to the visited node set. Starting from the detection point, trace back to the shot point based on the predecessor node information. The node sequence passed through is the shortest path from the shot point to the detection point.

[0103] S402: Output the predicted streamer travel time and the predicted OBN travel time corresponding to each detection point based on the initial seismic velocity model and the shortest streamer seismic wave propagation path and the shortest OBN seismic wave propagation path corresponding to each detection point;

[0104] For the shortest streamer seismic wave propagation path and the shortest OBN seismic wave propagation path corresponding to each detection point, the propagation time of the seismic wave on the path is calculated by dividing the length of each grid cell on the path by the velocity of the cell, and the velocity of each grid cell is output through the initial seismic velocity model.

[0105] S403: Calculate the difference between the predicted streamer travel time and the streamer travel time at each detection point to obtain the streamer deviation, and calculate the difference between the predicted OBN travel time and the OBN travel time at each detection point to obtain the OBN deviation.

[0106] The streamer bias is the difference between the predicted streamer travel time and the streamer travel time at each detection point, and the OBN bias is the difference between the predicted OBN travel time and the OBN travel time at each detection point.

[0107] Furthermore, the initial seismic velocity model is iteratively updated. In each iteration, the slowness correction is calculated based on the streamer deviation and the OBN deviation. The slowness of the initial seismic velocity model is updated based on the slowness correction to obtain the updated initial seismic velocity model as the initial seismic velocity model for the next iteration.

[0108] In seismology, slowness describes the propagation characteristics of seismic waves in a medium. It is the reciprocal of velocity and is measured in seconds per meter (s / m). The slowness correction is a parameter used to iteratively adjust the initial seismic velocity model. During each iteration, the slowness correction is calculated based on the streamer deviation and the OBN deviation. The slowness of the initial seismic velocity model is updated based on the slowness correction, resulting in an updated initial seismic velocity model that serves as the initial seismic velocity model for the next iteration.

[0109] The calculation of slowness correction based on streamer deviation and OBN deviation includes:

[0110] Construct the seismic tomography equation, as shown in formula (2);

[0111]

[0112] Where ΔS is the slowness correction, ΔT TS is the towline deviation; ΔT OBN is the OBN deviation, L TS is the shortest streamer seismic wave propagation path, L OBN is the shortest OBN seismic wave propagation path, is the streamer bias weight, is the OBN bias weight, n is the number of iterations;

[0113] The slowness correction can be obtained by transforming and solving the seismic tomography equation.

[0114] Furthermore, the streamer deviation weight is calculated as shown in formula (3);

[0115]

[0116] The OBN deviation weight is calculated as shown in formula (4);

[0117]

[0118] in, is the streamer chi-square value, is the OBN chi-square value.

[0119] The chi-square value of the streamer is shown in formula (5);

[0120]

[0121] Among them, N TS is the number of streamer detection points, σ TS is the streamer standard deviation, k is the streamer detection point number;

[0122] The OBN chi-square value is shown in formula (6);

[0123]

[0124] Among them, N OBN is the number of OBN detection points, σ OBN is the OBN standard deviation, and l is the OBN detection point number.

[0125] The iterative convergence condition of the initial seismic velocity model is that the streamer chi-square value and the OBN chi-square value are less than or equal to 1 or the preset upper limit of the number of iterations is reached.

[0126] The streamer chi-square value is used to measure the degree of difference between the observed value of the streamer travel time and the theoretical value predicted based on the current initial seismic velocity model, and the OBN chi-square value is used to measure the degree of difference between the observed value of the OBN travel time and the theoretical value predicted based on the current initial seismic velocity model.

[0127] When the streamer chi-square value and the OBN chi-square value are less than or equal to 1, it means that the streamer travel time and OBN travel time predicted by the current initial seismic velocity model are very close to the actual observed travel time, and the model has achieved a high degree of accuracy. The purpose of setting a preset upper limit on the number of iterations is to prevent the iteration process from falling into an infinite loop or excessive iterations. During implementation, due to data noise, complex geological structures, etc., the streamer chi-square value and the OBN chi-square value may not converge to the ideal state of less than or equal to 1. When the number of iterations reaches the preset upper limit, the iteration is stopped even if the streamer chi-square value and the OBN chi-square value do not meet the condition of less than or equal to 1. The preset upper limit of the number of iterations can be set to 20-50 times.

[0128] When the iterative convergence conditions are met, the final seismic velocity model is obtained:

[0129]

[0130] Among them, s min To detect the minimum value of slowness data, s max To detect the maximum value of slowness data, z i is the depth of the geological layer, z min To detect the minimum value of depth data, Δs n is the slowness correction for the nth iteration.

[0131] In order to verify the effectiveness of this embodiment, Figure 3 This is a schematic diagram of the first arrival wave tomography results obtained when only the streamer data is used. Figure 4 Schematic diagram of first-arrival tomography results from the combined first-arrival traveltime tomography method based on streamer and OBN data, provided in an embodiment of the present invention. Comparing the two figures, we can see that at depths greater than 2.5 km, streamer data alone provides an inaccurate representation of the stratigraphic structure, with poor results at depth, and a significant presence of gray gradients, indicating weak resolution of deep subsurface structures. However, the tomographic imaging results from the combined first-arrival traveltime tomography method based on streamer and OBN data, provided in an embodiment of the present invention, can display a clearer velocity structure and stratigraphic structure.

[0132] Compared with the existing technology, the joint first-arrival traveltime tomography method based on streamer and OBN data provided in this embodiment improves data utilization by fusing streamer and OBN data and jointly processing the two types of data. It also uses geological structure information when calculating streamer and OBN deviations and slowness corrections, and can effectively adapt to complex geological structures, including complex stratigraphic layers, faults, and areas with drastic velocity changes, overcoming the problem that the existing technology is not applicable to complex geological structures. It also uses streamer and OBN data for iterative updates to improve the accuracy of the model.

[0133] Another embodiment of the present invention discloses a combined first arrival travel time tomography system based on streamer and OBN data, comprising:

[0134] The streamer seismic data acquisition module is used to obtain the original streamer seismic data of the target work area, and after preprocessing, pick up the first arrival time of the streamer seismic data of each detection point from the original streamer seismic data;

[0135] The OBN seismic data acquisition module is used to obtain the original OBN seismic data of the target work area, and after preprocessing, pick up the first arrival time of the OBN seismic data of each detection point from the original OBN seismic data;

[0136] A travel time calculation module for calculating the streamer travel time of each detection point based on the first arrival time of the streamer seismic data and the recorded shot firing time of each detection point, and for calculating the OBN travel time of each detection point based on the first arrival time of the OBN seismic data and the recorded shot firing time of each detection point;

[0137] The model training module is used to construct an initial seismic velocity model of the target work area based on the geological structure information of the target work area obtained from exploration, iteratively update the initial seismic velocity model until the iterative convergence conditions are met, obtain the final seismic velocity model, and perform first-arrival travel time tomography on the target work area based on the final seismic velocity model; in each iteration, the streamer deviation is obtained based on the initial seismic velocity model and the streamer travel time, and the OBN deviation is obtained based on the initial seismic velocity model and the OBN travel time; the slowness correction is calculated based on the streamer deviation and the OBN deviation, and the slowness of the initial seismic velocity model is updated based on the slowness correction to obtain the updated initial seismic velocity model as the initial seismic velocity model for the next iteration.

[0138] It is understandable that the combined first-arrival travel time tomography system based on streamer and OBN data corresponds to each step in the combined first-arrival travel time tomography method based on streamer and OBN data, and will not be described in detail here.

[0139] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0140] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A combined first arrival travel time tomography method based on streamer and OBN data, characterized in that: The method comprises the following steps: Obtain the original streamer seismic data of the target work area, and after preprocessing, pick the first arrival time of the streamer seismic data of each detection point from the original streamer seismic data; Obtain the original OBN seismic data of the target work area, and after preprocessing, pick out the first arrival time of the OBN seismic data of each receiver point from the original OBN seismic data; The streamer travel time of each receiver point is calculated based on the first arrival time of the streamer seismic data and the recorded shot firing time of each receiver point, and the OBN travel time of each receiver point is calculated based on the first arrival time of the OBN seismic data and the recorded shot firing time of each receiver point; An initial seismic velocity model of the target work area is constructed based on geological structure information of the target work area obtained through exploration. The initial seismic velocity model is iteratively updated until an iterative convergence condition is met to obtain a final seismic velocity model. First arrival travel time tomography of the target work area is performed based on the final seismic velocity model. During each iteration, a streamer deviation is obtained based on the initial seismic velocity model and the streamer travel time, and an OBN deviation is obtained based on the initial seismic velocity model and the OBN travel time. A slowness correction is calculated based on the streamer deviation and the OBN deviation, and the slowness of the initial seismic velocity model is updated based on the slowness correction to obtain an updated initial seismic velocity model as the initial seismic velocity model for the next iteration. Calculating the slowness correction based on the streamer deviation and the OBN deviation includes: Construct the seismic tomography equation, as shown in formula (2); Where ΔS is the slowness correction, ΔT TS is the towline deviation; ΔT OBN is the OBN deviation, L TS is the shortest streamer seismic wave propagation path, L OBN is the shortest OBN seismic wave propagation path, is the streamer bias weight, is the OBN bias weight, n is the number of iterations; The slowness correction is solved based on the seismic tomography equation.

2. The method according to claim 1, characterized in that The streamer deviation is obtained based on the initial seismic velocity model and the streamer travel time. The OBN deviation is obtained based on the initial seismic velocity model and the OBN travel time, including: Perform path ray tracing on the target work area to obtain the shortest streamer seismic wave propagation path and the shortest OBN seismic wave propagation path corresponding to each detection point; Based on the initial seismic velocity model and the shortest streamer seismic wave propagation path and the shortest OBN seismic wave propagation path corresponding to each detection point, the predicted streamer travel time and the predicted OBN travel time corresponding to each detection point are output; The difference between the predicted streamer travel time and the streamer travel time at each detection point is calculated to obtain the streamer deviation, and the difference between the predicted OBN travel time and the OBN travel time at each detection point is calculated to obtain the OBN deviation.

3. The method according to claim 2, characterized in that By performing path ray tracing on the target work area, the shortest streamer seismic wave propagation path and the shortest OBN seismic wave propagation path corresponding to each detection point are obtained, including: Gridding the strata of the target work area based on the geological structure information of the target work area to obtain multiple grid cells; Based on the initial seismic velocity model, a corresponding velocity value is assigned to each grid cell; Calculate the travel time from each grid cell to the adjacent grid cells based on the velocity value assigned to each grid cell; Each grid cell is regarded as a node, the connection between adjacent grid cells is regarded as an edge, and the travel time between adjacent grid cells is regarded as the weight of the edge to generate a strip diagram of the target work area; The strip diagram of the target work area is processed based on the Dijkstra algorithm to obtain the shortest streamer seismic wave propagation path from the shot point to the streamer detection point and the shortest OBN seismic wave propagation path from the shot point to each OBN detection point.

4. The method according to claim 3, characterized in that The final seismic velocity model is: Among them, s min To detect the minimum value of slowness data, s max To detect the maximum value of slowness data, z i is the depth of the geological layer, z min To detect the minimum value of depth data, Δs n is the slowness correction value of the nth iteration, v n+1 is the seismic velocity of the n+1th iteration.

5. The method according to claim 4, characterized in that The towline deviation weight is shown in formula (3); The OBN deviation weight is calculated as shown in formula (4); in, is the streamer chi-square value, is the OBN chi-square value.

6. The method according to claim 5, characterized in that The chi-square value of the streamer is shown in formula (5); Among them, N TS is the number of streamer detection points, σ TS is the streamer standard deviation, k is the streamer detection point number; The OBN chi-square value is shown in formula (6); Among them, N OBN is the number of OBN detection points, σ OBN is the OBN standard deviation, and l is the OBN detection point number.

7. The method according to claim 6, characterized in that The iterative convergence condition is: The streamer chi-square value and the OBN chi-square value are less than or equal to 1 or the preset upper limit of the number of iterations is reached.

8. The method according to claim 3, characterized in that The target area strip diagram is processed based on the Dijkstra algorithm to obtain the shortest streamer seismic wave propagation path from the shot point to the streamer receiver point and the shortest OBN seismic wave propagation path from the shot point to each OBN receiver point, including: For any streamer or OBN receiver, the shortest propagation path from the shot point to the receiver is obtained by the following steps: Create a visited node set and an unvisited node set. The initial visited node set includes the gun points, and the initial unvisited node set includes all nodes except the gun points. Search the unvisited node set for the node closest to the shot point, remove it from the unvisited node set, and add it to the visited node set; for each node added to the visited node set, continue searching for the node closest to it; repeat this operation until the detection point is added to the visited node set; Generate the shortest propagation path from the shot point to the receiver point based on the set of visited nodes.

9. A combined first arrival travel time tomography system based on streamer and OBN data, characterized in that: The system comprises: The streamer seismic data acquisition module is used to obtain the original streamer seismic data of the target work area, and after preprocessing, pick up the first arrival time of the streamer seismic data of each detection point from the original streamer seismic data; The OBN seismic data acquisition module is used to obtain the original OBN seismic data of the target work area, and after preprocessing, pick up the first arrival time of the OBN seismic data of each detection point from the original OBN seismic data; A travel time calculation module for calculating the streamer travel time of each detection point based on the first arrival time of the streamer seismic data and the recorded shot firing time of each detection point, and for calculating the OBN travel time of each detection point based on the first arrival time of the OBN seismic data and the recorded shot firing time of each detection point; The model training module is used to construct an initial seismic velocity model of the target work area based on the geological structure information of the target work area obtained from exploration, iteratively update the initial seismic velocity model until the iterative convergence condition is met to obtain a final seismic velocity model, and perform first-arrival travel time tomography on the target work area based on the final seismic velocity model; in each iteration, the streamer deviation is obtained based on the initial seismic velocity model and the streamer travel time, and the OBN deviation is obtained based on the initial seismic velocity model and the OBN travel time; the slowness correction is calculated based on the streamer deviation and the OBN deviation, and the slowness of the initial seismic velocity model is updated based on the slowness correction to obtain an updated initial seismic velocity model as the initial seismic velocity model for the next iteration; the calculation of the slowness correction based on the streamer deviation and the OBN deviation includes: Construct the seismic tomography equation as shown below; Where ΔS is the slowness correction, ΔT TS is the towline deviation; ΔT OBN is the OBN deviation, L TS is the shortest streamer seismic wave propagation path, L OBN is the shortest OBN seismic wave propagation path, is the streamer bias weight, is the OBN bias weight, n is the number of iterations; The slowness correction is solved based on the seismic tomography equation.

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