WO2024060427A1

WO2024060427A1 - River channel and fault synchronous test method and apparatus

Info

Publication number: WO2024060427A1
Application number: PCT/CN2022/138860
Authority: WO
Inventors: 李海山; 杨午阳; 魏新建
Original assignee: 中国石油天然气股份有限公司
Priority date: 2022-09-22
Filing date: 2022-12-14
Publication date: 2024-03-28
Also published as: CN117784225A

Abstract

A river channel and fault synchronous test method, belonging to the technical field of seismic data interpretations. The method comprises: acquiring three-dimensional post-stack seismic data; on the basis of the minimum amplitude value and the maximum amplitude value in the three-dimensional post-stack seismic data, processing the three-dimensional post-stack seismic data; and using the processed three-dimensional post-stack seismic data as the input of a river channel and fault test model, so as to obtain a river channel and fault test result. The present invention further relates to a test apparatus, an electronic device, and a machine-readable storage medium.

Description

Synchronous detection method and device for river channels and faults

Technical field

The invention relates to the field of seismic data interpretation, and specifically to a method for synchronous detection of river channels and faults, a device for synchronous detection of river channels and faults, an electronic device and a machine-readable storage medium.

Background technique

River channels are one of the important geological targets in oil and gas exploration. It is crucial to accurately detect the location of river channels and reveal their development processes. In terms of basin and prospect scale analysis, mastering the channel characteristics is helpful for quantitative analysis of landforms and accurate understanding of the deposition process, and helps to understand the depositional environment and the main directions of sediments over time; in terms of reservoir scale analysis, channel sandstone It is crucial to determine the volume of the river and the properties of the fluid filling it; in the oil and gas field development stage, accurately depicting the macroscopic distribution characteristics, sedimentation and change patterns of the river channel can provide guidance for the selection of development target blocks, preparation of oil and gas development plans, well locations and wells Provide important basis for trajectory design, etc.

In the existing technology, three-dimensional post-stack seismic data are widely used for river channel interpretation. However, manual interpretation of river channels takes a long time, and the interpretation results are inaccurate and affected by human subjectivity. The use of conventional convolutional neural networks for river channel detection is highly dependent on labels and label generation methods, resulting in widespread problems in other work areas. The detection ability is very poor, and only the influence of folds is considered, while the influence of faults is ignored, resulting in a greatly reduced detection accuracy.

Contents of the invention

The purpose of the embodiments of the present invention is to provide a method and device for synchronous detection of river channels and faults to solve the above problem of manually interpreting river channels, which is time-consuming, inaccurate, and subject to human subjectivity; using conventional convolutional neural networks River detection is highly dependent on labels and label generation methods, resulting in poor generalization ability in other work areas, and only considering the impact of folds and ignoring the impact of faults, resulting in low detection accuracy.

In order to achieve the above objectives, embodiments of the present invention provide a method for synchronous detection of river channels and faults, including:

Obtain three-dimensional post-stack seismic data;

Process the three-dimensional post-stack seismic data based on the minimum amplitude value and the maximum amplitude value in the three-dimensional post-stack seismic data;

Use the processed three-dimensional post-stack seismic data as the input of the river channel and fault detection model to obtain the river channel and fault detection results;

Based on the minimum amplitude value and the maximum amplitude value in the three-dimensional post-stack seismic data, the three-dimensional post-stack seismic data is processed, including:

Determine the minimum amplitude value and the maximum amplitude value of the three-dimensional post-stack seismic data;

Based on the minimum amplitude value and the maximum amplitude value, determine a normalized amplitude value;

Transform the amplitude value of each data in the three-dimensional post-stack seismic data based on the minimum amplitude value to obtain first transformed data;

Based on the normalized amplitude value and the first transformed data, processed three-dimensional post-stack seismic data is obtained.

Optionally, determine the normalized amplitude value based on the minimum amplitude value and the maximum amplitude value, including:

Use the following calculation formula to calculate the normalized amplitude value:

ΔA= _Amax - _Amin ;

Among them, ΔA is the normalized amplitude value; A _max is the maximum amplitude value; A _min is the minimum amplitude value.

Optionally, transform the amplitude value of each data in the three-dimensional post-stack seismic data based on the minimum amplitude value to obtain the first transformed data, including:

Use the following calculation formula to obtain the first transformed data:

A′ _i =A _i -A _min ;

Among them, A′ _i is the first transformed data; A _i is the maximum amplitude value; A _min is the minimum amplitude value.

Optionally, based on the normalized amplitude value and the first transformed data, the processed three-dimensional post-stack seismic data is obtained, including:

The processed three-dimensional post-stack seismic data is calculated using the following calculation formula:

Among them, A″ _i is the processed three-dimensional post-stack seismic data; A′ _i is the first transformed data; ΔA is the normalized amplitude value.

Optionally, the method also includes:

Synthesize multiple sample data based on geological and geophysical theories and construct a training sample set;

The training sample set is used as the input of the deep learning neural network, and the river channel and fault detection model is trained.

The invention also provides a device for synchronizing detection of river channels and faults, including:

Data acquisition module, used to acquire three-dimensional post-stack seismic data;

A data processing module, used for processing the three-dimensional post-stack seismic data based on the minimum amplitude value and the maximum amplitude value in the three-dimensional post-stack seismic data;

The result output module is used to use the processed three-dimensional post-stack seismic data as the input of the river channel and fault detection model to obtain the river channel and fault detection results;

The data processing module specifically includes:

A data amplitude determination module, used to determine the minimum amplitude value and the maximum amplitude value of the three-dimensional post-stack seismic data;

A normalized amplitude value determination module, configured to determine a normalized amplitude value based on the minimum amplitude value and the maximum amplitude value;

An amplitude value transformation module, configured to transform the amplitude value of each data in the three-dimensional post-stack seismic data based on the minimum amplitude value to obtain the first transformed data;

A data output module is used to obtain processed three-dimensional post-stack seismic data based on the normalized amplitude value and the first transformed data.

Optionally, use the following calculation formula to calculate the normalized amplitude value:

ΔA= _Amax - _Amin ;

Optionally, use the following calculation formula to obtain the first transformed data:

A′ _i =A _i −A _min ;

On the other hand, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned steps are implemented. Synchronous detection method of river channels and faults.

On the other hand, the present invention also provides a machine-readable storage medium. Instructions are stored on the machine-readable storage medium. The instructions are used to cause the machine to execute the above-mentioned synchronized detection method of river channels and faults.

This technical solution performs data processing on the 3D post-stack seismic data by obtaining the minimum amplitude value and maximum amplitude value of the 3D post-stack seismic data, and obtains the processed 3D post-stack seismic data, and uses the processed 3D post-stack seismic data to Input the pre-trained detection model of river channels and faults to obtain the detection results of river channels and faults. This technical solution can be adapted to the detection of different lithological river channel positions formed in different sedimentary environments. In addition, it can eliminate the problems of accurate detection of river channels caused by faults. Impact, the detection of complex rivers is realized, and the detection process is short, efficient, accurate, and has strong generalization ability.

Other features and advantages of embodiments of the present invention will be described in detail in the detailed description that follows.

Description of drawings

The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

Figure 1 is a flow chart of the method for synchronous detection of river channels and faults provided by the present invention;

Figure 2 is a flow chart of data processing in the method for synchronous detection of river channels and faults provided by the present invention;

Figure 3 is a schematic structural diagram of the device for synchronous detection of river channels and faults provided by the present invention;

Figure 4 is a schematic structural diagram of the data processing module in the device for synchronous detection of river channels and faults provided by the present invention;

Figure 5 is a schematic diagram of the first synthetic three-dimensional post-stack seismic data provided by the present invention;

Figure 6 is a schematic diagram of three-dimensional river channel data provided by the present invention based on the synthesized three-dimensional post-stack seismic data in Figure 5;

Figure 7 is a schematic diagram of three-dimensional fault data obtained based on the synthesized three-dimensional post-stack seismic data in Figure 5 provided by the present invention;

Figure 8 is a schematic diagram of the second type of synthetic three-dimensional post-stack seismic data provided by the present invention;

Figure 9 is a schematic diagram of three-dimensional river channel data obtained based on the synthesized three-dimensional post-stack seismic data in Figure 8 provided by the present invention;

Figure 10 is a schematic diagram of three-dimensional fault data obtained based on the synthesized three-dimensional post-stack seismic data in Figure 8 provided by the present invention;

Figure 11 is a schematic diagram of three-dimensional post-stack seismic data of a certain work area provided by the present invention;

Figure 12 is a schematic diagram of the overlay display based on the three-dimensional post-stack seismic data and corresponding river channel detection results provided by the present invention in Figure 11;

Figure 13 is a schematic diagram of a superposition display based on the three-dimensional post-stack seismic data and corresponding fault detection results in Figure 11 provided by the present invention.

Explanation of reference signs

10-Data acquisition module; 20-Data processing module;

30-Result output module; 21-Data amplitude determination module;

22-Normalized amplitude value determination module; 23-Amplitude value conversion module;

24-Data output module.

Detailed ways

Specific implementation modes of the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific implementations described here are only used to illustrate and explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention.

The terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance.

In addition, terms such as "roughly" and "basically" are intended to illustrate that the relevant content does not require absolute accuracy, but may have certain deviations. For example: "roughly equal" does not only mean absolute equality. Since it is difficult to achieve absolute "equal" during actual production and operation, there is generally a certain deviation. Therefore, in addition to absolute equality, "approximately equal" also includes the above-mentioned situations where there is a certain deviation. Taking this as an example, in other cases, unless otherwise specified, terms such as "roughly" and "basically" have similar meanings to the above.

Figure 1 is a flow chart of the method for synchronous detection of river channels and faults provided by the present invention; Figure 3 is a schematic structural diagram of the device for synchronous detection of river channels and faults provided by the present invention; Figure 4 is a diagram of data processing in the device for synchronous detection of river channels and faults provided by the present invention. A schematic structural diagram of the module; Figure 5 is a schematic diagram of the first synthesized three-dimensional post-stack seismic data provided by the present invention; Figure 6 is a schematic diagram of the three-dimensional river channel data obtained based on the synthesized three-dimensional post-stack seismic data in Figure 5 provided by the present invention; Figure 7 is a schematic diagram of the three-dimensional fault data obtained based on the synthesized three-dimensional post-stack seismic data in Figure 5 provided by the present invention; Figure 8 is a schematic diagram of the second type of synthesized three-dimensional post-stack seismic data provided by the present invention; Figure 9 is a schematic diagram of the second synthesized three-dimensional post-stack seismic data provided by the present invention based on Figure 5 A schematic diagram of the three-dimensional river channel data obtained by synthesizing the three-dimensional post-stack seismic data in Figure 8; Figure 10 is a schematic diagram of the three-dimensional fault data obtained by synthesizing the three-dimensional post-stack seismic data in Figure 8 provided by the present invention; Figure 11 is a three-dimensional schematic diagram of a certain work area provided by the present invention A schematic diagram of post-stack seismic data; Figure 12 is a schematic diagram of a superimposed display based on the three-dimensional post-stack seismic data in Figure 11 and the corresponding channel detection results provided by the present invention; Figure 13 is a schematic diagram of a superposition display based on the three-dimensional post-stack seismic data in Figure 11 provided by the present invention Schematic diagram showing the overlay of data and corresponding fault detection results.

Example 1

As shown in Figure 1, an embodiment of the present invention provides a method for synchronous detection of river channels and faults, including:

Step 1: Obtain three-dimensional post-stack seismic data;

Specifically, the relevant steps for obtaining three-dimensional post-stack seismic data based on existing seismic data are well known to those skilled in the art, and will not be described again here.

Step 2: Process the three-dimensional post-stack seismic data based on the minimum amplitude value and the maximum amplitude value in the three-dimensional post-stack seismic data;

Specifically, as shown in Figure 2, the specific steps of step two include:

Step 201, determining the minimum amplitude and the maximum amplitude of the three-dimensional post-stack seismic data;

Step 202: Determine a normalized amplitude value based on the minimum amplitude value and the maximum amplitude value;

Step 203: Transform the amplitude value of each data in the three-dimensional post-stack seismic data based on the minimum amplitude value to obtain first transformed data, where the first transformed data is a three-dimensional post-stack seismic with a minimum amplitude value of 0. data;

Step 204: Obtain processed three-dimensional post-stack seismic data based on the normalized amplitude value and the first transformation data, and the amplitude range of the processed three-dimensional post-stack seismic data is between 0 and 1.

Further, based on the minimum amplitude value and the maximum amplitude value, determining a normalized amplitude value includes:

ΔA= _Amax - _Amin ;

Further, based on the amplitude minimum value, the amplitude value of each data in the three-dimensional post-stack seismic data is transformed to obtain first transformed data, including:

Use the following calculation formula to obtain the first transformed data:

A′ _i =A _i -A _min ;

Further, based on the normalized amplitude value and the first transformation data, processed three-dimensional post-stack seismic data is obtained, including:

Therefore, the specific steps of step two can also be described as:

The minimum amplitude value and the maximum amplitude value of the obtained three-dimensional post-stack seismic data are compared with the amplitude minimum value of the three-dimensional post-stack seismic data to obtain three-dimensional post-stack seismic data with a minimum amplitude value of 0 (i.e. first transformed data); make a difference between the maximum amplitude value and the minimum amplitude value to obtain a normalized amplitude value; compare the three-dimensional post-stack seismic data with the minimum amplitude value of 0 (i.e., the first transformed data) and The normalized amplitude value is used to obtain the processed three-dimensional post-stack seismic data, and the amplitude range of the data is between 0 and 1. Specifically, the following calculation formula is used to express:

Where A″ _i is the processed 3D post-stack seismic data; _Ai is the maximum amplitude; _Amax is the maximum amplitude; _Amin is the minimum amplitude.

Step 3: Use the processed three-dimensional post-stack seismic data as the input of the river channel and fault detection model to obtain the river channel and fault detection results.

Specifically, as shown in Figure 11, Figure 12 and Figure 13, the data size is 700*1600*400, the spatial sampling interval is 12.5m, the time sampling interval is 2ms, the vertical coordinate is the longitudinal measurement line number, and the horizontal coordinate is the contact measurement Line number, vertical coordinate is time. The above method is used for detection and the detection results are obtained. As shown in Figure 12 and Figure 13, all river channel positions (dark positions) and fault positions (dark positions) in the three-dimensional post-stack seismic data with complex structural background are obtained. accurate detection.

Further, the method also includes:

Specifically, in order to ensure the generalization ability of the river channel and fault detection model, the training sample set for training the river channel and fault detection model is also processed (normalized), so that each sample is composed of a numerical range between 0 and 1. It consists of synthetic 3D post-stack seismic data and corresponding accurately labeled 3D channel data and accurately labeled 3D fault data. Therefore, in order to ensure the consistency of the input of the pre-trained river channel and fault detection models, the input three-dimensional post-stack seismic data was also normalized. Considering that manual annotation of 3D seismic data is time-consuming, inaccurate and highly subjective, in order to overcome this problem, based on geological and geophysical theories, 9600 pairs of synthetic 3D data with a size of 96*96*96 with diverse and complex structural patterns were automatically generated. Post-stack seismic data and corresponding accurately labeled three-dimensional channel data and accurately labeled three-dimensional fault data constitute the training sample set.

In the embodiment of the present invention, the river channel and fault detection model has two task outputs, that is, the processed three-dimensional post-stack seismic data is input into the pre-trained river channel and fault detection model for prediction, and the river channel and fault detection model can be used for prediction. One branch of the model outputs the river channel detection result, and another branch of the network model outputs the fault detection result. It should be noted that in the embodiment of the present invention, the feasibility and necessity of the number of convolution layers, convolution kernel size, step size, activation function and other elements of the river channel and fault detection model are carefully determined according to the needs of the present invention. design. The river channel and fault detection model in this invention sequentially builds an encoder part, a decoder part and an output part based on the principle of deep learning:

The encoder part consists of five stages and is used to extract features at different scales from the input 3D post-stack seismic data. Each stage contains one or two convolutional layers and learns a residual function that adds the input of each stage to the output of the last convolutional layer of that stage. The introduction of the residual learning mechanism helps overcome performance degradation and improve accuracy. In each stage, the convolution kernel size of the convolution layer is 3*3*3, and pooling is implemented through a convolution operation with a convolution kernel size of 2*2*2 and a stride of 2. It can be noticed that the number of features at each stage is doubled and the resolution is halved. After each convolutional layer, batch normalization (Batch Nom1alization) and ReLU (Rectified Linear Unit) activation functions are applied to improve the stability and nonlinear approximation capabilities of the network.

The decoder part consists of four stages for systematically aggregating multi-scale information. Similar to the encoder part, each stage also contains one or two convolutional layers, also using a residual learning mechanism. The size of the convolution kernel of the convolutional layer at each stage is also 3*3*3, and upsampling is achieved through a deconvolution operation with a convolution kernel size of 2*2*2 and a stride of 2. Through layer-hopping connections, the features extracted from the encoder part are connected to the corresponding stages of the decoder part for spatial resolution compensation.

The output part consists of two branches sharing the same decoding features. Each branch consists of two convolution layers with a convolution kernel size of 3*3*3 and a convolution layer with a convolution kernel size of l*l*l. , then batch normalization (Batch Nom1alization) and sigmoid activation function. The first branch is used for river detection tasks, and the second branch is used for fault detection tasks.

During training, the normalized synthetic 3D post-stack seismic data is input into the channel and fault detection model in batches, and the batch size is set to 4. The adaptive moment estimation optimization algorithm is used to optimize the network, the learning rate is set to 0.0001, the total training period is set to 100, and all samples in the training sample set are traversed in one period.

More specifically, the longitudinal coordinate in Figures 5, 6 and 7 is the longitudinal survey line number, the horizontal coordinate is the contact survey line number, and the vertical coordinate is time. As shown in Figures 5, 6 and 7, in the first synthetic three-dimensional post-stack seismic data generated by this invention that has undergone complex structural deformation (including a series of alternating fold deformations and fault deformations) in this sample, the channel position and The fault locations have been accurately marked; among them, the river channel location in Figure 6 is marked as 1, and other locations are marked as 0; among them, the fault location in Figure 7 is marked as 1, and other locations are marked as 0.

More specifically, the vertical coordinate in Figures 8, 9 and 10 is the longitudinal survey line number, the horizontal coordinate is the contact survey line number, and the vertical coordinate is time. As shown in Figures 8, 9 and 10, the fold and fault structure information in the second synthetic three-dimensional post-stack seismic data generated by the present invention is rich and diverse, and can highly approximate the actual data, and the positions of river channels and faults are obtained. Accurate labeling, where the river channel position in Figure 9 is labeled as 1 and other positions are labeled as 0; among them, the fault position in Figure 10 is labeled as 1 and other positions are labeled as 0.

Taking the above-mentioned first synthetic three-dimensional post-stack seismic data and second synthetic three-dimensional post-stack seismic data as examples, accurate annotation samples are provided for the training of river channel and fault detection models. The existence of a sufficient number of label data in the training sample set lays a solid big data foundation for the rapid and accurate implementation of the deep learning-based simultaneous detection method of river channels and faults in the present invention.

Example 2

As shown in Figure 3, an embodiment of the present invention also provides a device for synchronizing detection of river channels and faults, including:

Data acquisition module 10, used to acquire three-dimensional post-stack seismic data;

The data processing module 20 is used to process the three-dimensional post-stack seismic data based on the minimum amplitude value and the maximum amplitude value in the three-dimensional post-stack seismic data;

The result output module 30 is used to use the processed three-dimensional post-stack seismic data as the input of the river channel and fault detection model to obtain the river channel and fault detection results.

Further, as shown in Figure 4, the data processing module 20 specifically includes:

The data amplitude determination module 21 is used to determine the minimum amplitude value and the maximum amplitude value of the three-dimensional post-stack seismic data;

The normalized amplitude value determination module 22 is used to determine the normalized amplitude value based on the minimum amplitude value and the maximum amplitude value;

The amplitude value transformation module 23 is used to transform the amplitude value of each data in the three-dimensional post-stack seismic data based on the minimum amplitude value to obtain the first transformed data;

The data output module 24 is used to obtain processed three-dimensional post-stack seismic data based on the normalized amplitude value and the first transformed data.

Further, the normalized amplitude value is calculated using the following calculation formula:

ΔA= _Amax - _Amin ;

Furthermore, the first transformation data is obtained using the following calculation formula:

A′ _i =A _i -A _min ;

Example 3

An embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned river channel and Fault synchronization detection method.

Example 4

Embodiments of the present invention provide a machine-readable storage medium. Instructions are stored on the machine-readable storage medium. The instructions are used to cause the machine to execute the above-mentioned synchronized detection method of river channels and faults.

Through the above technical solutions, both the detection accuracy and detection efficiency of river and fault detection have been significantly improved:

1. Since in the process of constructing a dual-task accurate annotation data set, river channels of different shapes are added to the model in the form of relative impedance, the river channel and fault detection model can be adapted to different lithological channels formed in different depositional environments. Location detection.

2. In the process of constructing the dual-task accurate annotation dataset, the number of different river forms simulated in the model is randomly set, and the direction, width, length, thickness and relative impedance value of the river vary with the spatial position. Therefore, the river channel and fault detection model can adapt to the detection of complex river channel locations such as closely spaced or intertwined ones.

3. Due to the various folds and fracture deformations carried out in the process of constructing the dual-task accurate annotation data set, coupled with data augmentation and knowledge sharing between tasks, the river channel and fault detection model can simultaneously detect complex structural backgrounds. The underlying river channels and faults are effectively eliminated, thereby effectively eliminating the impact of faults on accurate detection of river channels;

4. Since this invention is based on deep learning algorithms and operates on GPU, it has high computing efficiency. For a 4.56GB three-dimensional seismic data volume, it only takes 4.4 minutes to complete the entire three-dimensional seismic data volume using NVIDIA V100 GPU. Detection of all channels and faults in the system, while manual interpretation takes days and accuracy cannot be guaranteed.

The optional implementations of the embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above-mentioned implementations. Within the scope of the technical concept of the embodiments of the present invention, the embodiments of the present invention can be modified. The technical solution is subjected to various simple modifications, and these simple modifications all belong to the protection scope of the embodiments of the present invention.

In addition, it should be noted that the specific technical features described in the above-mentioned specific embodiments can be combined in any suitable manner as long as there is no contradiction. In order to avoid unnecessary repetition, various possible combinations will not be further described in the embodiments of the present invention.

Those skilled in the art can understand that all or part of the steps in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program. The program is stored in a storage medium and includes several instructions to cause the microcontroller, chip or processor to (processor) executes all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code. .

In addition, any combination of different implementation modes of the embodiments of the present invention can also be performed. As long as they do not violate the ideas of the embodiments of the present invention, they should also be regarded as the content disclosed in the embodiments of the present invention.

Claims

A method for synchronous detection of river channels and faults, characterized by comprising:

Acquire 3D post-stack seismic data;

Processing the three-dimensional post-stack seismic data based on the minimum amplitude value and the maximum amplitude value in the three-dimensional post-stack seismic data;

Use the processed three-dimensional post-stack seismic data as the input of the river channel and fault detection model to obtain the river channel and fault detection results;

Based on the minimum amplitude value and maximum amplitude value in the three-dimensional post-stack seismic data, the three-dimensional post-stack seismic data is processed, including:

Determining a minimum amplitude value and a maximum amplitude value of the three-dimensional post-stack seismic data;

Based on the minimum amplitude value and the maximum amplitude value, determine a normalized amplitude value;

Transform the amplitude value of each data in the three-dimensional post-stack seismic data based on the minimum amplitude value to obtain first transformed data;

Based on the normalized amplitude value and the first transformed data, processed three-dimensional post-stack seismic data is obtained.
The method according to claim 1, characterized in that, based on the minimum amplitude value and the maximum amplitude value, determining the normalized amplitude value includes:

Use the following calculation formula to calculate the normalized amplitude value:

ΔA= Amax - Amin ;

Among them, ΔA is the normalized amplitude value; A max is the maximum amplitude value; A min is the minimum amplitude value.
The method according to claim 1, characterized in that, based on the minimum amplitude value, the amplitude value of each data in the three-dimensional post-stack seismic data is transformed to obtain the first transformed data, including:

Use the following calculation formula to obtain the first transformed data:

A′ i =A i -A min ;

Among them, A′ i is the first transformed data; A i is the maximum amplitude value; A min is the minimum amplitude value.
The method according to claim 1, characterized in that, based on the normalized amplitude value and the first transformation data, the processed three-dimensional post-stack seismic data is obtained, including:

The processed three-dimensional post-stack seismic data is calculated using the following calculation formula:

Among them, A″ i is the processed three-dimensional post-stack seismic data; A′ i is the first transformed data; ΔA is the normalized amplitude value.
The method of claim 1, further comprising:

Synthesize multiple sample data based on geological and geophysical theories and construct a training sample set;

The training sample set is used as the input of the deep learning neural network, and the river channel and fault detection model is trained.
A device for synchronous detection of river channels and faults, characterized by comprising:

Data acquisition module, used to acquire three-dimensional post-stack seismic data;

A data processing module, used for processing the three-dimensional post-stack seismic data based on the minimum amplitude value and the maximum amplitude value in the three-dimensional post-stack seismic data;

The result output module is used to use the processed three-dimensional post-stack seismic data as the input of the river channel and fault detection model to obtain the river channel and fault detection results;

The data processing module specifically includes:

A data amplitude determination module, used to determine the minimum amplitude value and the maximum amplitude value of the three-dimensional post-stack seismic data;

A normalized amplitude value determination module, configured to determine a normalized amplitude value based on the minimum amplitude value and the maximum amplitude value;

An amplitude value transformation module, configured to transform the amplitude value of each data in the three-dimensional post-stack seismic data based on the minimum amplitude value to obtain the first transformed data;

A data output module, configured to obtain processed three-dimensional post-stack seismic data based on the normalized amplitude value and the first transformed data.
The device according to claim 6, characterized in that the normalized amplitude value is calculated using the following calculation formula:

ΔA= Amax - Amin ;

Among them, ΔA is the normalized amplitude value; A max is the maximum amplitude value; A min is the minimum amplitude value.
The device according to claim 6, characterized in that the first transformed data is obtained using the following calculation formula:

A′ i =A i -A min ;

Among them, A′ i is the first transformed data; A i is the maximum amplitude value; A min is the minimum amplitude value.
An electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the computer program, claims 1-5 are implemented The method for simultaneous detection of river channels and faults described in any one of the above.
A machine-readable storage medium having instructions stored on the machine-readable storage medium, the instructions being used to cause the machine to execute the method for synchronous detection of river channels and faults according to any one of claims 1-5.