Method and device for denoising horizon data based on ice radar and electronic equipment

Abstract

The invention provides a denoising method and device for horizon data based on an ice radar and electronic equipment, and can be applied to the technical field of glacier detection and the technical field of signal processing. The method comprises the following steps: carrying out progressive denoising processing on initial horizon data from an ice radar to obtain noise estimation data; performing double transform domain processing on the initial horizon data and the noise estimation data to obtain a first curvelet component corresponding to the initial horizon data and a second curvelet component corresponding to the noise signal; obtaining curvelet sensitivity and a sensitivity threshold according to the first curvelet component and the second curvelet component; removing a random noise component from the first curvelet component to obtain a target curvelet component; performing discrete inverse curvelet transform on the target curvelet component to obtain a primary shear wave component and a threshold function; removing the residual noise component according to the threshold function to obtain a target shear wave component; and performing inverse shear wave transformation on the target shear wave component to obtain target horizon data.

Description

Technical Field

[0001] The present disclosure relates to the fields of glacier detection technology and signal processing technology, and more specifically, to a denoising method, device and electronic equipment for layer data based on ice radar. Background Art

[0002] Sea level change is an indicator and amplifier of global climate, and ice sheets are an important factor affecting sea level change. Therefore, analyzing and predicting the mass balance of ice sheet growth or contraction is an important means to quantitatively assess sea level change.

[0003] The accumulation rate is a key parameter for calculating the surface mass balance of glaciers and ice sheets and estimating sea level rise. Since electromagnetic waves have good penetration in ice layers, ice-penetrating radar can be used to detect glacier layer data corresponding to the accumulation rate.

[0004] In the process of realizing the concept of the present disclosure, the inventors discovered that there are at least technical problems in the related art, namely, weak layer echo signals and low layer data signal-to-noise ratio. Summary of the invention

[0005] In view of this, the present disclosure provides a method, device and electronic device for denoising layer data based on ice radar.

[0006] One aspect of the present disclosure provides a denoising method for layer data based on ice radar, comprising:

[0007] The initial layer data from the ice radar are progressively de-noised to obtain noise estimation data corresponding to the noise signal, wherein the initial layer data include the noise signal; the initial layer data and the noise estimation data are respectively processed in the double transform domain to obtain a plurality of first curvelet components corresponding to the initial layer data and a plurality of second curvelet components corresponding to the noise signal, wherein the double transform domain includes a shear wave domain and a curvelet domain; curvelet sensitivity and sensitivity threshold are obtained based on the plurality of first curvelet components and the plurality of second curvelet components; random noise components are removed from the plurality of first curvelet components based on the curvelet sensitivity and the sensitivity threshold to obtain target curvelet components; discrete inverse curvelet transform is performed on the target curvelet components to obtain primary shear wave components and a threshold function corresponding to the primary shear wave components; residual noise components are removed from the primary shear wave components based on the threshold function to obtain target shear wave components; inverse shear wave transform is performed on the target shear wave components to obtain target layer data.

[0008] According to an embodiment of the present disclosure, initial layer data acquired by an ice radar are progressively denoised to obtain noise estimation data corresponding to a noise signal, including: normalizing the initial layer data to obtain layer data to be estimated, so that the layer data to be estimated are all within a target amplitude range; progressively denoising the layer data to be estimated according to a predetermined number of iterations to obtain noise estimation components corresponding to each iteration in turn; and accumulating the noise estimation components corresponding to each iteration to obtain noise estimation data corresponding to the noise signal.

[0009] According to an embodiment of the present disclosure, the initial layer data and the noise estimation data are respectively processed in a dual transform domain to obtain a plurality of first curvelet components corresponding to the initial layer data and a plurality of second curvelet components corresponding to the noise signal, including: transforming the initial layer data and the noise estimation data to the shear wave domain according to a predetermined transformation scale to obtain a plurality of first shear components corresponding to the initial layer data and a plurality of second shear components corresponding to the noise signal; converting the plurality of first shear components to the curvelet domain to obtain a plurality of first curvelet components corresponding to the first shear components; converting the plurality of second shear components to the curvelet domain to obtain a plurality of second curvelet components corresponding to the second shear components.

[0010] According to an embodiment of the present disclosure, curvelet sensitivities and sensitivity thresholds are obtained based on multiple first curvelet components and multiple second curvelet components, including obtaining multiple curvelet sensitivities based on the ratio of the absolute values ​​of the first curvelet components and the second curvelet components of the same transformation scale; and determining the sensitivity threshold based on the curvelet sensitivities.

[0011] According to an embodiment of the present disclosure, based on the curvelet sensitivity and the sensitivity threshold, random noise components are removed from multiple first curvelet components to obtain target curvelet components, including: comparing the curvelet sensitivity and the sensitivity threshold at each transformation scale; when the curvelet sensitivity is greater than or equal to the sensitivity threshold, determining that the first curvelet component corresponding to the current transformation scale is the target curvelet component; when the curvelet sensitivity is less than the sensitivity threshold, determining that the first curvelet component corresponding to the current transformation scale is the random noise component.

[0012] According to an embodiment of the present disclosure, a discrete inverse curvelet transform is performed on the target curvelet component to obtain a primary shear wave component and a threshold function corresponding to the primary shear wave component, including: performing a discrete inverse curvelet transform on the target curvelet component to obtain a primary shear wave component corresponding to each target curvelet component; acquiring characteristic data of each primary shear wave component; and constructing a threshold function corresponding to each primary shear wave component based on the characteristic data of each primary shear wave component.

[0013] According to an embodiment of the present disclosure, the residual noise component is removed from the primary shear wave component according to a threshold function to obtain a target shear wave component, including: comparing the primary shear wave component and the threshold function at each transformation scale; when the primary shear wave component is greater than or equal to the threshold function, determining that the primary shear wave component at the current transformation scale is the target shear wave component; when the primary shear wave component is less than the threshold function, determining that the primary shear wave component at the current transformation scale is the residual noise component.

[0014] According to an embodiment of the present disclosure, an inverse shear wave transform is performed on the target shear wave component to obtain target layer data, including: performing an inverse shear wave transform on the target shear wave component to obtain the target layer component; and performing splicing processing on the target layer component to obtain target layer data corresponding to the initial layer data.

[0015] A second aspect of the present disclosure provides a denoising device for layer data based on ice radar, comprising:

[0016] An estimation module, used for performing a progressive denoising process on the initial layer data acquired by the ice radar to obtain noise estimation data corresponding to the noise signal, wherein the initial layer data includes the noise signal;

[0017] A double transform module is used to perform double transform domain processing on the initial layer data and the noise estimation data respectively, to obtain a plurality of first curvelet components corresponding to the initial layer data and a plurality of second curvelet components corresponding to the noise signal, wherein the double transform domain includes a shear wave domain and a curvelet domain;

[0018] An obtaining module, used for obtaining a curvelet sensitivity and a sensitivity threshold according to a plurality of first curvelet components and a plurality of second curvelet components;

[0019] A first removal module is used to remove the random noise component from the plurality of first curvelet components based on the curvelet sensitivity and the sensitivity threshold to obtain a target curvelet component;

[0020] An inverse curvelet transform module is used to perform a discrete inverse curvelet transform on the target curvelet component to obtain a primary shear wave component and a threshold function corresponding to the primary shear wave component;

[0021] A second removal module is used to remove the residual noise component from the primary shear wave component according to the threshold function to obtain the target shear wave component;

[0022] The inverse shear wave transform module is used to perform inverse shear wave transform on the target shear wave component to obtain the target layer data.

[0023] Another aspect of the present disclosure provides an electronic device, comprising:

[0024] one or more processors;

[0025] a memory for storing one or more programs,

[0026] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0027] According to the embodiments of the present disclosure, a plurality of first curvelet components and a plurality of second curvelet components are obtained by performing double transform domain processing on the noise estimation data and the noisy initial layer data obtained by progressive denoising processing, and the correspondence between the noise signal and the target signal and the sensitivity is used to remove the random noise component from the plurality of first curvelet components, and then the obtained target curvelet component is converted to the shear wave domain to remove the residual noise component from the primary shear wave component according to the threshold function. Since the edge extraction capabilities of the shear wave transform and the curvelet transform are utilized, and the noise signal is removed according to the curvelet sensitivity and the sensitivity threshold, the problem that the denoising method in the spatiotemporal domain is difficult to retain the detailed structure of the layer signal with obvious edge features is overcome, that is, the layer characteristics of the weak layer signal are effectively retained. At the same time, the loss of effective signals and the generation of pseudo-noise are avoided, the signal-to-noise ratio is improved, and the quality of the glacier layer data obtained by ice radar detection is thereby improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0029] Figure 1 A flowchart of a method for denoising ice radar-based horizon data according to an embodiment of the present disclosure is schematically shown.

[0030] Figure 2a The measured initial layer data of the ice sheet surface of a certain island according to an embodiment of the present disclosure are schematically shown.

[0031] Figure 2b The target layer data of a certain island after denoising according to an embodiment of the present disclosure is schematically shown.

[0032] Figure 3a The measured initial layer data of the ice sheet surface of an island according to another embodiment of the present disclosure are schematically shown.

[0033] Figure 3b The target layer data of a certain island after denoising according to another embodiment of the present disclosure is schematically shown.

[0034] Figure 4 A block diagram of a device for denoising ice radar-based horizon data according to an embodiment of the present disclosure is schematically shown.

[0035] Figure 5A block diagram of an electronic device suitable for implementing a denoising method for ice radar-based horizon data according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0036] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0037] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0038] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0039] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0040] In the embodiments of the present disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the data involved (for example, including but not limited to user personal information) are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information and network security.

[0041] In the embodiments of the present disclosure, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0042] Since the last century, the global average temperature has risen by about 1.2 degrees, and the melting of glaciers has accelerated. According to statistics, in the past, the global average sea level has risen by about 3.2 mm per year. The rise in sea level will not only flood a large amount of land, but also destroy the existing ecosystem, making extreme weather and climate disasters more frequent and severe.

[0043] In order to assess the rise in sea levels, related technologies use ice radar to detect the internal layers of glaciers. However, due to the small electromagnetic difference between the internal ice layer medium and the surrounding environment, the layer echo signal is weak and often mixed with various noises. The presence of noise seriously reduces the signal-to-noise ratio and resolution of the layer profile, which greatly hinders the subsequent data interpretation. In addition, most traditional denoising methods suppress random noise in the time-space domain or transform domain. For layer signals with obvious edge features, the time-space domain method may be difficult to retain the detailed structure. Traditional transform domain methods may lose effective signals or introduce pseudo-noise in low signal-to-noise ratio modes.

[0044] In view of this, an embodiment of the present disclosure provides a denoising method for layer data based on ice radar, comprising:

[0045] The initial layer data from the ice radar are progressively de-noised to obtain noise estimation data corresponding to the noise signal, wherein the initial layer data include the noise signal; the initial layer data and the noise estimation data are respectively processed in the double transform domain to obtain a plurality of first curvelet components corresponding to the initial layer data and a plurality of second curvelet components corresponding to the noise signal, wherein the double transform domain includes a shear wave domain and a curvelet domain; curvelet sensitivity and sensitivity threshold are obtained based on the plurality of first curvelet components and the plurality of second curvelet components; random noise components are removed from the plurality of first curvelet components based on the curvelet sensitivity and the sensitivity threshold to obtain target curvelet components; discrete inverse curvelet transform is performed on the target curvelet components to obtain primary shear wave components and a threshold function corresponding to the primary shear wave components; residual noise components are removed from the primary shear wave components based on the threshold function to obtain target shear wave components; inverse shear wave transform is performed on the target shear wave components to obtain target layer data.

[0046] The following will be passed Figure 1 to Figure 3b The denoising method of ice radar-based stratigraphic data according to an embodiment of the present disclosure is described.

[0047] Figure 1 A flowchart of a method for denoising ice radar-based horizon data according to an embodiment of the present disclosure is schematically shown.

[0048] like Figure 1 As shown, the embodiment 100 includes operations S110 to S170.

[0049] In operation S110, a progressive denoising process is performed on initial layer data from the ice radar to obtain noise estimation data corresponding to the noise signal.

[0050] In operation S120, the initial layer data and the noise estimation data are respectively processed in the dual transform domain to obtain a plurality of first curvelet components corresponding to the initial layer data and a plurality of second curvelet components corresponding to the noise signal.

[0051] In operation S130, a curvelet sensitivity and a sensitivity threshold are obtained according to the plurality of first curvelet components and the plurality of second curvelet components.

[0052] In operation S140 , random noise components are removed from the plurality of first curvelet components based on the curvelet sensitivity and the sensitivity threshold to obtain a target curvelet component.

[0053] In operation S150 , a discrete inverse curvelet transform is performed on the target curvelet component to obtain a primary shearlet component and a threshold function corresponding to the primary shearlet component.

[0054] In operation S160, the residual noise component is removed from the primary shear wave component according to the threshold function to obtain a target shear wave component.

[0055] In operation S170, an inverse shear wave transform is performed on the target shear wave component to obtain target layer data.

[0056] According to an embodiment of the present disclosure, ice radar is an important tool for detecting ice layer structure and sub-ice terrain, which detects the structure inside the ice layer by emitting and receiving electromagnetic waves. The initial layer data is the original data corresponding to the glacier layer detected by the ice radar. The initial layer data includes a noise signal.

[0057] According to an embodiment of the present disclosure, the progressive denoising process can use an iterative filtering algorithm based on deterministic annealing and robust noise estimation to estimate the noise signal more accurately.

[0058] According to an embodiment of the present disclosure, an iterative filtering algorithm is used to progressively estimate the differential of noise data, and the noise differential is iteratively subtracted from the noise image to perform gradient descent; then bilateral filtering is performed, and a robust noise estimator is constructed, a robust kernel is used to remove large-amplitude gradients in the spatial domain, medium-amplitude waves in the frequency domain, and the scale parameter of the robust kernel is modified according to an annealing schedule. The whole process is repeated until the last iteration is reached to obtain the final noise estimation data.

[0059] According to an embodiment of the present disclosure, the dual transform domain processing includes shearlet domain transform and curvelet domain transform. The transform scale of the shearlet domain transform may be 6. When the transform scale is 6, 113 first curvelet components and 113 second curvelet components may be obtained through calculation.

[0060] According to an embodiment of the present disclosure, the first curvelet component includes image feature information obtained by processing the initial layer data in the double transform domain, and the second curvelet component includes image feature information obtained by processing the noise estimation data in the double transform domain.

[0061] According to the embodiments of the present disclosure, the curvelet sensitivity is related to the judgment of the layer signal. For the layer signal, the curvelet sensitivity is greater, while for the noise signal, the curvelet sensitivity is smaller. Therefore, the sensitivity threshold can be set to divide the noise signal and the layer signal in the initial layer data.

[0062] According to the embodiments of the present disclosure, it has been experimentally verified that when the sensitivity threshold is set to 75%, the weak layer characteristics of the first curvelet component can be better retained to obtain the target curvelet component.

[0063] According to an embodiment of the present disclosure, the target curvelet component is reconverted into the shearlet domain by discrete inverse curvelet transform, and the primary shearlet component is further denoised.

[0064] According to an embodiment of the present disclosure, the threshold function is dynamically adjusted according to the scale characteristics of each primary shear wave component to remove the residual noise signal, so that the denoising process can be more finely controlled to retain useful signals while removing noise.

[0065] According to an embodiment of the present disclosure, an inverse shearlet transform is performed on the target shearlet component to transform it back into the image domain, thereby obtaining the target layer data.

[0066] According to the embodiments of the present disclosure, a plurality of first curvelet components and a plurality of second curvelet components are obtained by performing double transform domain processing on the noise estimation data and the noisy initial layer data obtained by progressive denoising processing, and the correspondence between the noise signal and the target signal and the sensitivity is used to remove the random noise component from the plurality of first curvelet components, and then the obtained target curvelet component is converted to the shear wave domain to remove the residual noise component from the primary shear wave component according to the threshold function, and the edge extraction capabilities of the shear wave transform and the curvelet transform are utilized, and the noise signal is removed according to the curvelet sensitivity and the set sensitivity threshold, thereby overcoming the problem that the denoising method in the spatiotemporal domain is difficult to retain the details of the layer signal with obvious edge features, that is, the layer characteristics of the weak layer signal are effectively retained, and at the same time, the loss of effective signals and the generation of pseudo-noise are avoided, thereby improving the signal-to-noise ratio, and thus improving the quality of the glacier layer data obtained by ice radar detection.

[0067] According to an embodiment of the present disclosure, initial layer data acquired by an ice radar are progressively denoised to obtain noise estimation data corresponding to a noise signal, including: normalizing the initial layer data to obtain layer data to be estimated, so that the layer data to be estimated are all within a target amplitude range; progressively denoising the layer data to be estimated according to a predetermined number of iterations to obtain noise estimation components corresponding to each iteration in turn; and accumulating the noise estimation components corresponding to each iteration to obtain noise estimation data corresponding to the noise signal.

[0068] According to an embodiment of the present disclosure, the layer data to be estimated can be expressed as the following formula (1).

[0069] (1)

[0070] Among them, F(x,y) represents the layer data to be estimated, L(x,y) represents the noise-free layer data, and n(x,y) represents the variance. Additive Gaussian white noise, x represents the sampling in the fast time dimension, x=1,2,3...m, y=1,2,3...n. The matrix size of the layer data to be estimated is m×n.

[0071] According to an embodiment of the present disclosure, the predetermined number of iterations is a smaller number of iterations, for example, 12.

[0072] According to an embodiment of the present disclosure, the noise estimation data may be expressed as the following formula (2).

[0073] (2)

[0074] Where i is the iteration index, is the step size factor, represents the noise estimation data, represents the noise estimation component corresponding to each iteration.

[0075] According to an embodiment of the present disclosure, the progressive denoising process may mean that the noise signal is progressively removed in each iteration, wherein the estimated component obtained in each iteration is used as input data for the next iteration.

[0076] According to the embodiments of the present disclosure, the noise signal in the initial layer data is estimated through progressive denoising processing, laying a foundation for denoising in subsequent double transform processing.

[0077] According to an embodiment of the present disclosure, the initial layer data and the noise estimation data are respectively processed in a dual transform domain to obtain a plurality of first curvelet components corresponding to the initial layer data and a plurality of second curvelet components corresponding to the noise signal, including: transforming the initial layer data and the noise estimation data to the shear wave domain according to a predetermined transformation scale to obtain a plurality of first shear components corresponding to the initial layer data and a plurality of second shear components corresponding to the noise signal; converting the plurality of first shear components to the curvelet domain to obtain a plurality of first curvelet components corresponding to the first shear components; converting the plurality of second shear components to the curvelet domain to obtain a plurality of second curvelet components corresponding to the second shear components.

[0078] According to an embodiment of the present disclosure, the predetermined transformation scale may be 6.

[0079] According to an embodiment of the present disclosure, the first shear component corresponding to the initial horizon data can be expressed as , the second shear component corresponding to the noise signal can be expressed as .

[0080] Where z is the index of the shear component.

[0081] According to an embodiment of the present disclosure, for each first shearing component, it is converted into the curvelet domain to better extract the edge feature information in the corresponding shearing component, and obtain the first curvelet component corresponding to the first shearing component. The first curvelet component can be expressed as the following formula (3).

[0082] (3)

[0083] Where j is the scale of the curvelet transform, l is the angular direction of the transform, k is the spatial position, is the first curve component, is the discretized curvelet basis function, for The complex conjugate of .

[0084] According to an embodiment of the present disclosure, based on a transformation process similar to that of the first shear wave component, a second curvelet component corresponding to the second shear wave component can be obtained according to the following formula (4).

[0085] (4)

[0086] in, is the second wave component.

[0087] According to an embodiment of the present disclosure, curvelet sensitivity and sensitivity threshold are obtained based on multiple first curvelet components and multiple second curvelet components, including: obtaining multiple curvelet sensitivities based on the ratio of the absolute values ​​of the first curvelet components and the second curvelet components of the same transformation scale; and determining the sensitivity threshold based on the curvelet sensitivity.

[0088] According to an embodiment of the present disclosure, the curve sensitivity It can be expressed as the following formula (5).

[0089] (5)

[0090] According to an embodiment of the present disclosure, based on the curvelet sensitivity and the sensitivity threshold, random noise components are removed from multiple first curvelet components to obtain target curvelet components, including: comparing the curvelet sensitivity and the sensitivity threshold at each transformation scale; when the curvelet sensitivity is greater than or equal to the sensitivity threshold, determining that the first curvelet component corresponding to the current transformation scale is the target curvelet component; when the curvelet sensitivity is less than the sensitivity threshold, determining that the first curvelet component corresponding to the current transformation scale is the random noise component.

[0091] According to an embodiment of the present disclosure, when the curvelet sensitivity is greater than or equal to the sensitivity threshold, the current target curvelet component will be retained, and when the curvelet sensitivity is less than the sensitivity threshold, the random noise component will be assigned 0 to remove the random noise.

[0092] According to an embodiment of the present disclosure, the process of removing the random noise component can be expressed as the following formula (6).

[0093] (6)

[0094] in, is the sensitivity threshold, is the target curvelet component obtained after processing.

[0095] According to the embodiments of the present disclosure, by comparing the sensitivity threshold and the curve sensitivity, random noise can be effectively removed from the ice radar layer data while retaining important edge features, which not only improves the quality of the layer data but also provides a more accurate basis for subsequent data analysis and interpretation.

[0096] According to an embodiment of the present disclosure, a discrete inverse curvelet transform is performed on the target curvelet component to obtain a primary shear wave component and a threshold function corresponding to the primary shear wave component, including: performing a discrete inverse curvelet transform on the target curvelet component to obtain a primary shear wave component corresponding to each target curvelet component; acquiring characteristic data of each primary shear wave component; and constructing a threshold function corresponding to each primary shear wave component based on the characteristic data of each primary shear wave component.

[0097] According to an embodiment of the present disclosure, the process of performing discrete inverse curvelet transform on the target curvelet component to obtain the primary shearlet component can be expressed as the following formula (7).

[0098] (7)

[0099] in, represents the zth primary shear wave component.

[0100] According to an embodiment of the present disclosure, the characteristic data may be the standard deviation of the shear wave coefficient of the random noise in each subband, each subband represents the characteristics of the primary shear wave component in a specific scale and direction, and the standard deviation may be estimated based on the median estimator. The characteristic data may be expressed as the following formula (8).

[0101] (8)

[0102] in, represents the standard deviation, 0.6745 is a constant, and median() represents the median absolute deviation.

[0103] According to an embodiment of the present disclosure, based on the characteristic data of each primary shear wave component, constructing a threshold function corresponding to each primary shear wave component can be expressed as the following formula (9).

[0104] (9)

[0105] in, Represents the threshold function.

[0106] According to an embodiment of the present disclosure, the residual noise component is removed from the primary shear wave component according to a threshold function to obtain a target shear wave component, including: comparing the primary shear wave component and the threshold function at each transformation scale; when the primary shear wave component is greater than or equal to the threshold function, determining that the primary shear wave component at the current transformation scale is the target shear wave component; when the primary shear wave component is less than the threshold function, determining that the primary shear wave component at the current transformation scale is the residual noise component.

[0107] According to an embodiment of the present disclosure, the target shear wave component The determination process of can be expressed as the following formula (10).

[0108] (10)

[0109] According to an embodiment of the present disclosure, when the primary shear wave component is smaller than the threshold function, the primary shear wave component at the current transformation scale is determined to be a residual noise component, that is, 0 is assigned to the primary shear wave component at the current transformation scale.

[0110] According to the embodiments of the present disclosure, by constructing a threshold function and comparing the primary shear wave component of each scale with the threshold function to obtain the target shear wave component, the primary shear wave component can be adaptively denoised more accurately, further improving the quality of the stratigraphic data.

[0111] According to an embodiment of the present disclosure, an inverse shear wave transform is performed on the target shear wave component to obtain target layer data, including: performing an inverse shear wave transform on the target shear wave component to obtain the target layer component; and performing splicing processing on the target layer component to obtain target layer data corresponding to the initial layer data.

[0112] According to an embodiment of the present disclosure, the target shear wave components are subjected to inverse shear wave transform to transform them into the image domain, and each target shear wave component is spliced, that is, the target shear wave components in multiple scales or directions are fused to obtain denoised target layer data.

[0113] In order to better reflect the effect of the denoising method based on ice radar layer data of the embodiment of the present disclosure, the following will be Figure 2a~Figure 3b Two groups of images illustrate the effects of the embodiments of the present disclosure.

[0114] Figure 2a The measured initial layer data of the ice sheet surface of a certain island according to an embodiment of the present disclosure are schematically shown.

[0115] Figure 2b The target layer data of a certain island after denoising according to an embodiment of the present disclosure is schematically shown.

[0116] like Figure 2a and Figure 2b As shown, the horizontal axis is distance and the vertical axis is depth. Figure 2a and Figure 2b After denoising in the above embodiment 100, the layer data is clearer and the influence of noise is effectively reduced.

[0117] Figure 3a The measured initial layer data of the ice sheet surface of an island according to another embodiment of the present disclosure are schematically shown.

[0118] Figure 3b Schematically illustrates target layer data of an island after denoising according to another embodiment of the present disclosure.

[0119] like Figure 3a and Figure 3b As shown, the horizontal axis is distance and the vertical axis is depth. Figure 3a and Figure 3bAfter denoising in the above embodiment 100, the layer data is clearer and the influence of noise is effectively reduced.

[0120] Figure 4 A block diagram of a device for denoising ice radar-based horizon data according to an embodiment of the present disclosure is schematically shown.

[0121] like Figure 4 As shown, the apparatus 400 includes an estimation module 410 , a dual transformation module 420 , an acquisition module 430 , a first removal module 440 , an inverse curvelet transformation module 450 , a second removal module 460 and an inverse shearlet transformation module 470 .

[0122] The estimation module 410 is used to perform progressive denoising on the initial layer data acquired by the ice radar to obtain noise estimation data corresponding to the noise signal, wherein the initial layer data includes the noise signal.

[0123] The dual transform module 420 is used to perform dual transform domain processing on the initial layer data and the noise estimation data respectively to obtain multiple first curvelet components corresponding to the initial layer data and multiple second curvelet components corresponding to the noise signal, wherein the dual transform domain includes a shear wave domain and a curvelet domain.

[0124] The obtaining module 430 is used to obtain the curvelet sensitivity and the sensitivity threshold according to the plurality of first curvelet components and the plurality of second curvelet components.

[0125] The first removal module 440 is configured to remove the random noise component from the plurality of first curvelet components based on the curvelet sensitivity and the sensitivity threshold, so as to obtain a target curvelet component.

[0126] The inverse curvelet transform module 450 is used to perform a discrete inverse curvelet transform on the target curvelet component to obtain a primary shearlet component and a threshold function corresponding to the primary shearlet component.

[0127] The second removal module 460 is used to remove the residual noise component from the primary shear wave component according to the threshold function to obtain the target shear wave component.

[0128] The inverse shear wave transform module 470 is used to perform an inverse shear wave transform on the target shear wave component to obtain target layer data.

[0129] According to an embodiment of the present disclosure, the estimation module includes a processing submodule, a denoising submodule, and an accumulation submodule.

[0130] The processing submodule is used to perform normalization processing on the initial layer data to obtain the layer data to be estimated, so that the layer data to be estimated are all within the target amplitude range.

[0131] The denoising submodule is used to perform progressive denoising on the layer data to be estimated according to a predetermined number of iterations, and obtain the noise estimation component corresponding to each iteration in turn.

[0132] The accumulation submodule is used to accumulate the noise estimation component corresponding to each iteration to obtain noise estimation data corresponding to the noise signal.

[0133] According to an embodiment of the present disclosure, the double conversion module 420 includes a conversion submodule, a first conversion submodule and a second conversion submodule.

[0134] The transformation submodule is used to transform the initial layer data and the noise estimation data into the shear wave domain according to a predetermined transformation scale, so as to obtain a plurality of first shear components corresponding to the initial layer data and a plurality of second shear components corresponding to the noise signal.

[0135] The first conversion submodule is used to convert the multiple first shearing components into the curvelet domain respectively to obtain multiple first curvelet components corresponding to the first shearing components.

[0136] The second conversion submodule is used to convert the multiple second shearing components into the curvelet domain respectively to obtain multiple second curvelet components corresponding to the second shearing components.

[0137] According to an embodiment of the present disclosure, the obtaining module 430 includes an obtaining submodule and a determining submodule.

[0138] The obtaining submodule is used to obtain a plurality of curvelet sensitivities according to the ratio of the absolute values ​​of the first curvelet component and the second curvelet component of the same transformation scale.

[0139] The determination submodule is used to determine a sensitivity threshold according to the curve wave sensitivity.

[0140] According to an embodiment of the present disclosure, the first removal module 440 includes a comparison submodule, a first determination submodule, and a second determination submodule.

[0141] The comparison submodule is used to compare the curvelet sensitivity and the sensitivity threshold at each transformation scale.

[0142] The first determination submodule is used to determine, when the curvelet sensitivity is greater than or equal to a sensitivity threshold, a first curvelet component corresponding to the current transformation scale as a target curvelet component.

[0143] The second determination submodule is used to determine that the first curvelet component corresponding to the current transformation scale is a random noise component when the curvelet sensitivity is less than the sensitivity threshold.

[0144] According to an embodiment of the present disclosure, the inverse curvelet transform module 450 includes a get submodule, an acquire submodule and a build submodule.

[0145] A submodule is obtained, which is used to perform a discrete inverse curvelet transform on the target curvelet component to obtain a primary shear wave component corresponding to each target curvelet component.

[0146] The acquisition submodule is used to acquire characteristic data of each primary shear wave component.

[0147] The construction submodule is used to construct a threshold function corresponding to each primary shear wave component according to the characteristic data of each primary shear wave component.

[0148] According to an embodiment of the present disclosure, the second removing module 460 includes a comparing submodule, a first determining submodule, and a second determining submodule.

[0149] The comparison submodule is used to compare the primary shear wave component and the threshold function at each transformation scale.

[0150] The first determination submodule is used to determine the primary shear wave component at the current transformation scale as the target shear wave component when the primary shear wave component is greater than or equal to the threshold function.

[0151] The second determination submodule is used to determine that the primary shear wave component at the current transformation scale is a residual noise component when the primary shear wave component is smaller than the threshold function.

[0152] According to an embodiment of the present disclosure, the inverse shearlet transform module 470 includes a transform submodule and a stitching submodule.

[0153] The transformation submodule is used to perform inverse shear wave transformation on the target shear wave component to obtain the target layer component.

[0154] The splicing submodule is used to perform splicing processing on the target layer components to obtain the target layer data corresponding to the initial layer data.

[0155] According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging circuits, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, submodules, units, and subunits can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0156] For example, any multiple of the estimation module 410, the dual transformation module 420, the acquisition module 430, the first removal module 440, the inverse curvelet transform module 450, the second removal module 460, and the inverse shearlet transform module 470 can be combined in one module / unit / subunit for implementation, or any one of the modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least part of the functions of one or more of these modules / units / subunits can be combined with at least part of the functions of other modules / units / subunits and implemented in one module / unit / subunit. According to an embodiment of the present disclosure, at least one of the estimation module 410, the dual transformation module 420, the obtaining module 430, the first removal module 440, the inverse curvelet transform module 450, the second removal module 460 and the inverse shearlet transform module 470 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in an appropriate combination of any of them. Alternatively, at least one of the estimation module 410, the dual transformation module 420, the obtaining module 430, the first removal module 440, the inverse curvelet transform module 450, the second removal module 460 and the inverse shearlet transform module 470 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be performed.

[0157] Figure 5A block diagram of an electronic device suitable for implementing a denoising method for ice radar-based horizon data according to an embodiment of the present disclosure is schematically shown.

[0158] Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0159] like Figure 5 As shown, the electronic device 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 to a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include an onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0160] In RAM 503, various programs and data required for the operation of electronic device 500 are stored. Processor 501, ROM 502 and RAM 503 are connected to each other via bus 504. Processor 501 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 502 and / or RAM 503. It should be noted that the program can also be stored in one or more memories other than ROM 502 and RAM 503. Processor 501 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0161] According to an embodiment of the present disclosure, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to the bus 504. The electronic device 500 may further include one or more of the following components connected to the input / output (I / O) interface 505: an input portion 506 including a keyboard, a mouse, etc.; an output portion 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 508 including a hard disk, etc.; and a communication portion 509 including a network interface card such as a LAN card, a modem, etc. The communication portion 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage portion 508 as needed.

[0162] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the processor 501, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0163] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0164] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0165] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 502 and / or the RAM 503 described above and / or one or more memories other than the ROM 502 and the RAM 503 .

[0166] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, which contains program code for executing the method provided by the embodiment of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the ice radar-based layer data denoising method provided by the embodiment of the present disclosure.

[0167] When the computer program is executed by the processor 501, the above functions defined in the system / device of the embodiment of the present disclosure are executed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0168] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 509, and / or installed from the removable medium 511. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0169] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0170] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0171] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A denoising method for layer data based on ice radar, comprising: Performing progressive denoising on initial layer data from an ice radar to obtain noise estimation data corresponding to a noise signal, wherein the initial layer data includes the noise signal; The initial layer data and the noise estimation data are processed in a dual transform domain respectively to obtain a plurality of first curvelet components corresponding to the initial layer data and a plurality of second curvelet components corresponding to the noise signal, wherein the dual transform domain includes a shear wave domain and a curvelet domain; Obtaining a curvelet sensitivity and a sensitivity threshold according to the plurality of first curvelet components and the plurality of second curvelet components; Based on the curvelet sensitivity and the sensitivity threshold, removing random noise components from the plurality of first curvelet components to obtain a target curvelet component; Performing a discrete inverse curvelet transform on the target curvelet component to obtain a primary shear wave component and a threshold function corresponding to the primary shear wave component; According to the threshold function, removing the residual noise component from the primary shear wave component to obtain the target shear wave component; An inverse shear wave transform is performed on the target shear wave component to obtain target layer data.

2. The method according to claim 1, wherein: The step of performing progressive denoising on the initial layer data acquired by the ice radar to obtain noise estimation data corresponding to the noise signal includes: Normalizing the initial layer data to obtain the layer data to be estimated, so that the layer data to be estimated are all within the target amplitude range; Performing progressive denoising on the to-be-estimated layer data according to a predetermined number of iterations, and sequentially obtaining noise estimation components corresponding to each iteration; The noise estimation component corresponding to each iteration is accumulated to obtain noise estimation data corresponding to the noise signal.

3. The method according to claim 1, wherein: The step of performing double transform domain processing on the initial layer data and the noise estimation data to obtain a plurality of first curvelet components corresponding to the initial layer data and a plurality of second curvelet components corresponding to the noise signal comprises: According to a predetermined transformation scale, the initial layer data and the noise estimation data are respectively transformed into a shear wave domain to obtain a plurality of first shear components corresponding to the initial layer data and a plurality of second shear components corresponding to the noise signal; Converting the plurality of first shearing components to the curvelet domain respectively to obtain a plurality of first curvelet components corresponding to the first shearing components; The plurality of second shearing components are respectively converted into a curvelet domain to obtain a plurality of second curvelet components corresponding to the second shearing components.

4. The method according to claim 1, wherein: The step of obtaining a curvelet sensitivity and a sensitivity threshold according to the plurality of first curvelet components and the plurality of second curvelet components comprises: Obtaining a plurality of the curvelet sensitivities according to a ratio of the absolute values ​​of the first curvelet component and the second curvelet component of the same transformation scale; The sensitivity threshold is determined according to the curvelet sensitivity.

5. The method according to claim 4, wherein: The removing random noise components from the plurality of first curvelet components based on the curvelet sensitivity and the sensitivity threshold to obtain a target curvelet component comprises: comparing the curvelet sensitivity at each transformation scale with the sensitivity threshold; In a case where the curvelet sensitivity is greater than or equal to the sensitivity threshold, determining the first curvelet component corresponding to the current transformation scale as the target curvelet component; When the curvelet sensitivity is less than the sensitivity threshold, it is determined that the first curvelet component corresponding to the current transformation scale is the random noise component.

6. The method according to claim 1, wherein: The step of performing a discrete inverse curvelet transform on the target curvelet component to obtain a primary shearlet component and a threshold function corresponding to the primary shearlet component includes: Performing a discrete inverse curvelet transform on the target curvelet components to obtain the primary shear wave components corresponding to each target curvelet component; Acquiring characteristic data of each of the primary shear wave components; The threshold function corresponding to each of the primary shear wave components is constructed according to the characteristic data of each of the primary shear wave components.

7. The method according to claim 6, wherein: The step of removing the residual noise component from the primary shear wave component according to the threshold function to obtain the target shear wave component comprises: comparing the primary shear wave component at each transformation scale with the threshold function; In the case where the primary shear wave component is greater than or equal to the threshold function, determining the primary shear wave component at the current transformation scale as a target shear wave component; When the primary shear wave component is smaller than the threshold function, it is determined that the primary shear wave component at the current transformation scale is a residual noise component.

8. The method according to claim 1, wherein: The step of performing an inverse shear wave transform on the target shear wave component to obtain target layer data includes: Performing inverse shear wave transformation on the target shear wave component to obtain a target layer component; The target layer components are spliced ​​to obtain target layer data corresponding to the initial layer data.

9. A denoising device for layer data based on ice radar, comprising: An estimation module, used for performing a progressive denoising process on the initial layer data acquired by the ice radar to obtain noise estimation data corresponding to the noise signal, wherein the initial layer data includes the noise signal; A double transform module is used to perform double transform domain processing on the initial layer data and the noise estimation data respectively to obtain a plurality of first curvelet components corresponding to the initial layer data and a plurality of second curvelet components corresponding to the noise signal, wherein the double transform domain includes a shear wave domain and a curvelet domain; An obtaining module, configured to obtain a curvelet sensitivity and a sensitivity threshold according to the plurality of first curvelet components and the plurality of second curvelet components; A first removal module, configured to remove random noise components from the plurality of first curvelet components based on the curvelet sensitivity and the sensitivity threshold, to obtain a target curvelet component; an inverse curvelet transform module, configured to perform a discrete inverse curvelet transform on the target curvelet component to obtain a primary shearlet component and a threshold function corresponding to the primary shearlet component; A second removal module, configured to remove a residual noise component from the primary shear wave component according to the threshold function to obtain a target shear wave component; The inverse shear wave transform module is used to perform inverse shear wave transform on the target shear wave component to obtain target layer data.

10. An electronic device, comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 8.

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