Denoising methods, devices, and electronic equipment based on ice radar stratigraphic data

Abstract

This disclosure provides a denoising method, apparatus, and electronic device for stratigraphic data based on ice radar, which can be applied in the fields of glacier exploration technology and signal processing technology. The method includes: performing progressive denoising processing on initial stratigraphic data from ice radar to obtain noise estimation data; performing dual transform domain processing on the initial stratigraphic data and the noise estimation data respectively to obtain a first curvilinear component corresponding to the initial stratigraphic data and a second curvilinear component corresponding to the noise signal; obtaining a curvilinear sensitivity and a sensitivity threshold based on the first and second curvilinear components; removing random noise components from the first curvilinear component to obtain a target curvilinear component; performing a discrete inverse curvilinear transform on the target curvilinear component to obtain a primary shear wave component and a threshold function; removing residual noise components based on the threshold function to obtain the target shear wave component; and performing an inverse shear wave transform on the target shear wave component to obtain the target stratigraphic data.

Description

Technical Field

[0001] This disclosure relates to the fields of glacier detection technology and signal processing technology, and more specifically, to a denoising method, apparatus and electronic equipment based on ice radar stratigraphic data. Background Technology

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

[0003] Accumulation rate is a key parameter for calculating the surface mass balance of glaciers and ice sheets, as well as for estimating sea-level rise. Due to the good penetrating power of electromagnetic waves in ice layers, ice-penetrating radar can be used to detect glacier stratigraphic data corresponding to the accumulation rate.

[0004] In realizing the concept disclosed herein, the inventors discovered that the related technologies have at least the technical problem of weak stratigraphic echo signals and low signal-to-noise ratio of stratigraphic data. Summary of the Invention

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

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

[0007] The initial stratigraphic data from the ice radar is progressively denoised to obtain noise estimation data corresponding to the noise signal. The initial stratigraphic data includes the noise signal. Both the initial stratigraphic data and the noise estimation data are processed using a dual transform domain to obtain multiple first curvelet components corresponding to the initial stratigraphic data and multiple second curvelet components corresponding to the noise signal. The dual transform domain includes a shear wave domain and a curvelet wave domain. Based on the multiple first and second curvelet components, curvelet sensitivity and sensitivity threshold are obtained. Based on the curvelet sensitivity and sensitivity threshold, random noise components are removed from the multiple first curvelet components to obtain the target curvelet component. The target curvelet component undergoes a discrete inverse curvelet transform to obtain a primary shear wave component and a threshold function corresponding to the primary shear wave component. Based on the threshold function, residual noise components are removed from the primary shear wave component to obtain the target shear wave component. Finally, the target shear wave component undergoes an inverse shear wave transform to obtain the target stratigraphic data.

[0008] According to embodiments of this disclosure, the initial stratigraphic data acquired by ice radar is subjected to progressive denoising processing to obtain noise estimation data corresponding to the noise signal. This includes: normalizing the initial stratigraphic data to obtain stratigraphic data to be estimated, such that the stratigraphic data to be estimated are all within the target amplitude range; progressively denoising the stratigraphic data to be estimated according to a predetermined number of iterations to obtain noise estimation components corresponding to each iteration; and accumulating the noise estimation components corresponding to each iteration to obtain noise estimation data corresponding to the noise signal.

[0009] According to embodiments of this disclosure, initial stratigraphic data and noise estimation data are subjected to dual transform domain processing to obtain multiple first curvilinear components corresponding to the initial stratigraphic data and multiple second curvilinear components corresponding to the noise signal. This includes: transforming the initial stratigraphic data and noise estimation data to the shear domain according to a predetermined transform scale to obtain multiple first shear components corresponding to the initial stratigraphic data and multiple second shear components corresponding to the noise signal; transforming the multiple first shear components to the curvilinear domain to obtain multiple first curvilinear components corresponding to the first shear components; and transforming the multiple second shear components to the curvilinear domain to obtain multiple second curvilinear components corresponding to the second shear components.

[0010] According to embodiments of this disclosure, a curvature sensitivity and a sensitivity threshold are obtained based on a plurality of first curvature components and a plurality of second curvature components, including obtaining a plurality of curvature sensitivities based on the ratio of the absolute values ​​of the first and second curvature components with the same transformation scale; and determining the sensitivity threshold based on the curvature sensitivity.

[0011] According to embodiments of this disclosure, a target curved wave component is obtained by removing random noise components from a plurality of first curved wave components based on curved wave sensitivity and sensitivity threshold. This includes: comparing the curved wave sensitivity and sensitivity threshold at each transform scale; determining the first curved wave component corresponding to the current transform scale as the target curved wave component when the curved wave sensitivity is greater than or equal to the sensitivity threshold; and determining the first curved wave component corresponding to the current transform scale as a random noise component when the curved wave sensitivity is less than the sensitivity threshold.

[0012] According to embodiments of this disclosure, 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 includes: performing a discrete inverse curvelet transform on the target curvelet component to obtain a primary shear wave component corresponding to each target curvelet component; obtaining feature data of each primary shear wave component; and constructing a threshold function corresponding to each primary shear wave component based on the feature data of each primary shear wave component.

[0013] According to embodiments of this disclosure, removing residual noise components from primary shear wave components based on a threshold function to obtain target shear wave components includes: comparing the primary shear wave components at each transform scale with the threshold function; determining the primary shear wave components at the current transform scale as target shear wave components if the primary shear wave components are greater than or equal to the threshold function; and determining the primary shear wave components at the current transform scale as residual noise components if the primary shear wave components are less than the threshold function.

[0014] According to embodiments of this disclosure, inverse shear wave transformation is performed on the target shear wave component to obtain target stratigraphic data, including: performing inverse shear wave transformation on the target shear wave component to obtain target stratigraphic components; and performing stitching processing on the target stratigraphic components to obtain target stratigraphic data corresponding to the initial stratigraphic data.

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

[0016] The estimation module is used to perform progressive denoising processing on the initial stratigraphic data acquired by the ice radar to obtain noise estimation data corresponding to the noise signal, wherein the initial stratigraphic data includes the noise signal.

[0017] The dual transform module is used to perform dual transform domain processing on the initial stratigraphic data and the noise estimation data respectively, to obtain multiple first curvelet components corresponding to the initial stratigraphic data and multiple second curvelet components corresponding to the noise signal. The dual transform domain includes the shear wave domain and the curvelet domain.

[0018] The module is used to obtain the curve sensitivity and sensitivity threshold based on multiple first curve components and multiple second curve components;

[0019] The first removal module is used to remove random noise components from multiple first curved wave components based on curved wave sensitivity and sensitivity threshold to obtain the target curved wave component;

[0020] The inverse warp transform module is used to perform discrete inverse warp transform on the target warp component to obtain the primary shear wave component and the threshold function corresponding to the primary shear wave component.

[0021] The second removal module is used to remove residual noise components from the primary shear wave components according to a threshold function to obtain the target shear wave components.

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

[0023] Another aspect of this disclosure provides an electronic device comprising:

[0024] One or more processors;

[0025] Memory, used to store 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 described above.

[0027] According to embodiments of this disclosure, by performing dual transform domain processing on the noise estimation data obtained from the progressive denoising process and the noisy initial stratigraphic data, multiple first curvature components and multiple second curvature components are obtained. Utilizing the correspondence between the noise signal, the target signal, and the sensitivity, random noise components are removed from the multiple first curvature components. Then, the obtained target curvature components are transformed to the shear wave domain to remove residual noise components from the primary shear wave components according to a threshold function. Because the edge extraction capabilities of shear wave transform and curvature transform are utilized, and noise signals are removed based on curvature sensitivity and sensitivity thresholds, this overcomes the difficulty of spatiotemporal domain denoising methods in preserving the detailed structure of stratigraphic signals with obvious edge features. That is, it effectively preserves the stratigraphic features of weak stratigraphic signals, while avoiding the loss of effective signals and the generation of false noise, thus improving the signal-to-noise ratio and consequently enhancing the quality of glacier stratigraphic data obtained by ice radar. Attached Figure Description

[0028] The above and other objects, features and advantages of this disclosure will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

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

[0030] Figure 2a The illustration schematically shows measured initial stratigraphic data of the surface of an island ice sheet according to an embodiment of the present disclosure.

[0031] Figure 2b The illustration schematically shows the target stratigraphic data of an island after denoising processing according to an embodiment of the present disclosure.

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

[0033] Figure 3b The illustration schematically shows target stratigraphic data of an island after denoising processing according to another embodiment of the present disclosure.

[0034] Figure 4 A block diagram of a noise reduction apparatus for ice radar-based stratigraphic data according to an embodiment of the present disclosure is shown schematically.

[0035] Figure 5A block diagram of an electronic device suitable for implementing a denoising method for ice radar-based stratigraphic data, according to embodiments of the present disclosure, is shown schematically. Detailed Implementation

[0036] The embodiments of the present disclosure will now 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 disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0037] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

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

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

[0040] In the embodiments disclosed herein, the collection, updating, analysis, processing, use, transmission, provision, disclosure, and storage of data (e.g., including but not limited to user personal information) comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures have been taken to prevent unauthorized access to user personal information data and to safeguard user personal information security and network security.

[0041] In the embodiments disclosed herein, user authorization or consent is obtained before acquiring or collecting user personal information.

[0042] Since the last century, the global average temperature has risen by about 1.2 degrees Celsius, and the rate of glacial melting has accelerated. Statistics show that the global average sea level has risen by about 3.2 millimeters per year over the past period. This sea-level rise not only inundates vast amounts of land but also damages existing ecosystems, leading to more frequent and severe extreme weather and climate disasters.

[0043] To assess sea-level rise, ice radar is used to probe the inner layers of glaciers. However, due to the small electromagnetic differences between the inner ice layer and the surrounding environment, the stratigraphic echo signals are weak and often mixed with various types of noise. The presence of noise significantly reduces the signal-to-noise ratio and resolution of the stratigraphic profile, greatly hindering subsequent data interpretation. Furthermore, traditional denoising methods mostly suppress random noise in the spatiotemporal or transform domains. For stratigraphic signals with distinct edge features, spatiotemporal domain methods may struggle to preserve detailed structure. Traditional transform domain methods, under low signal-to-noise ratio conditions, may lose effective signals or introduce spurious noise.

[0044] In view of this, embodiments of this disclosure provide a denoising method for layer data based on ice radar, including:

[0045] The initial stratigraphic data from the ice radar is progressively denoised to obtain noise estimation data corresponding to the noise signal. The initial stratigraphic data includes the noise signal. Both the initial stratigraphic data and the noise estimation data are processed using a dual transform domain to obtain multiple first curvelet components corresponding to the initial stratigraphic data and multiple second curvelet components corresponding to the noise signal. The dual transform domain includes a shear wave domain and a curvelet wave domain. Based on the multiple first and second curvelet components, curvelet sensitivity and sensitivity threshold are obtained. Based on the curvelet sensitivity and sensitivity threshold, random noise components are removed from the multiple first curvelet components to obtain the target curvelet component. The target curvelet component undergoes a discrete inverse curvelet transform to obtain a primary shear wave component and a threshold function corresponding to the primary shear wave component. Based on the threshold function, residual noise components are removed from the primary shear wave component to obtain the target shear wave component. Finally, the target shear wave component undergoes an inverse shear wave transform to obtain the target stratigraphic data.

[0046] The following will be through Figures 1-3b The denoising method for layer data based on ice radar according to embodiments of this disclosure will be described.

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

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

[0049] In operation S110, the initial stratigraphic data from the ice radar is subjected to progressive denoising processing to obtain noise estimation data corresponding to the noise signal.

[0050] In operation S120, the initial stratigraphic data and noise estimation data are processed by dual transform domain to obtain multiple first curvature components corresponding to the initial stratigraphic data and multiple second curvature components corresponding to the noise signal.

[0051] In operation S130, the curvature sensitivity and sensitivity threshold are obtained based on multiple first curvature components and multiple second curvature components.

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

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

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

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

[0056] According to embodiments of this disclosure, ice radar is an important tool for detecting ice structure and subglacial topography. It detects the internal structure of ice by emitting and receiving electromagnetic waves. The initial stratigraphic data consists of the raw data corresponding to the glacier layer detected by the ice radar. This initial stratigraphic data includes noise signals.

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

[0058] According to embodiments of this disclosure, the derivative of the noise data is progressively estimated using an iterative filtering algorithm, and the denoised derivative is iteratively subtracted from the noise image to perform gradient descent; then bilateral filtering is performed, by constructing a robust noise estimator, using a robust kernel to remove large-amplitude gradients in the spatial domain and medium-amplitude waves in the frequency domain, and modifying the scale parameter of the robust kernel according to the annealing schedule, repeating the entire process until the last iteration is reached to obtain the final noise estimation data.

[0059] According to embodiments of this disclosure, the dual transform domain processing includes shear wave domain transformation and curve wave domain transformation. The shear wave domain transformation can have a transform scale of 6. With a transform scale of 6, 113 first curve wave components and 113 second curve wave components can be calculated.

[0060] According to embodiments of this disclosure, the first curvature component includes image feature information obtained after processing initial stratigraphic data in a double transform domain, and the second curvature component includes image feature information obtained after processing noise estimation data in a double transform domain.

[0061] According to embodiments of this disclosure, the curve sensitivity is related to the determination of the stratigraphic signal. The curve sensitivity is greater for the stratigraphic signal and less for the noise signal. Therefore, a sensitivity threshold can be set to distinguish between noise signals and stratigraphic signals in the initial stratigraphic data.

[0062] According to embodiments of this disclosure, experimental verification shows that when the sensitivity threshold is set to 75%, the weak layer characteristics of the first curvature component can be better preserved, thus obtaining the target curvature component.

[0063] According to embodiments of this disclosure, the target curved wave component is re-converted to the shear wave domain through discrete inverse curved wave transform, and the primary shear wave component is further denoised.

[0064] According to embodiments of this disclosure, the threshold function is dynamically adjusted based on the scale characteristics of each primary shear wave component to remove residual noise signals, allowing for more precise control of the denoising process, preserving useful signals while removing noise.

[0065] According to embodiments of this disclosure, inverse shear wave transform is performed on the target shear wave component to transform it back to the image domain, thereby obtaining the target layer data.

[0066] According to embodiments of this disclosure, by performing dual transform domain processing on the noise estimation data obtained from the progressive denoising process and the noisy initial stratigraphic data, multiple first curvature components and multiple second curvature components are obtained. Utilizing the correspondence between noise signals, target signals, and sensitivity, random noise components are removed from the multiple first curvature components. Then, the obtained target curvature components are transformed to the shear wave domain to remove residual noise components from the primary shear wave components according to a threshold function. By utilizing the edge extraction capabilities of shear wave transform and curvature transform, and based on curvature sensitivity and a set sensitivity threshold, noise signals are removed. Therefore, this overcomes the problem that spatiotemporal denoising methods struggle to preserve the details of stratigraphic signals with obvious edge features. That is, it effectively preserves the stratigraphic features of weak stratigraphic signals, while avoiding the loss of effective signals and the generation of false noise, thus improving the signal-to-noise ratio and consequently enhancing the quality of glacier stratigraphic data obtained by ice radar.

[0067] According to embodiments of this disclosure, the initial stratigraphic data acquired by ice radar is subjected to progressive denoising processing to obtain noise estimation data corresponding to the noise signal. This includes: normalizing the initial stratigraphic data to obtain stratigraphic data to be estimated, such that the stratigraphic data to be estimated are all within the target amplitude range; progressively denoising the stratigraphic data to be estimated according to a predetermined number of iterations to obtain noise estimation components corresponding to each iteration; 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 this disclosure, the stratigraphic data to be estimated can be expressed as the following formula (1).

[0069] (1)

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

[0071] According to embodiments of this disclosure, the predetermined number of iterations is a small number of iterations, for example, 12.

[0072] According to embodiments of this disclosure, noise estimation data can be expressed as the following formula (2).

[0073] (2)

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

[0075] According to embodiments of this disclosure, progressive denoising can be represented as progressively removing noise signals in each iteration. The estimated components obtained in each iteration are used as input data for the next iteration.

[0076] According to embodiments of this disclosure, noise signals in the initial layer data are estimated through progressive denoising processing, laying the foundation for subsequent double transform processing for denoising.

[0077] According to embodiments of this disclosure, initial stratigraphic data and noise estimation data are subjected to dual transform domain processing to obtain multiple first curvilinear components corresponding to the initial stratigraphic data and multiple second curvilinear components corresponding to the noise signal. This includes: transforming the initial stratigraphic data and noise estimation data to the shear domain according to a predetermined transform scale to obtain multiple first shear components corresponding to the initial stratigraphic data and multiple second shear components corresponding to the noise signal; transforming the multiple first shear components to the curvilinear domain to obtain multiple first curvilinear components corresponding to the first shear components; and transforming the multiple second shear components to the curvilinear domain to obtain multiple second curvilinear components corresponding to the second shear components.

[0078] According to embodiments of this disclosure, the predetermined transformation scale can be 6.

[0079] According to embodiments of this disclosure, the first shear component corresponding to the initial stratigraphic data can be expressed as: The second shearing component corresponding to the noise signal can be expressed as: .

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

[0081] According to embodiments of this disclosure, for each first shear component, the process is transformed into the curvelet domain to better extract the edge feature information in the corresponding shear component, thereby obtaining the first curvelet component corresponding to the first shear 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, and k is the spatial position. For the first curvilinear component, The discretized curve wave basis functions are... for .

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

[0085] (4)

[0086] in, This is the second curvilinear component.

[0087] According to embodiments of this disclosure, obtaining a curve wave sensitivity and a sensitivity threshold based on a plurality of first curve wave components and a plurality of second curve wave components includes: obtaining a plurality of curve wave sensitivities based on the ratio of the absolute values ​​of the first and second curve wave components with the same transformation scale; and determining a sensitivity threshold based on the curve wave sensitivities.

[0088] According to embodiments of this disclosure, curvature sensitivity It can be expressed as the following formula (5).

[0089] (5)

[0090] According to embodiments of this disclosure, a target curved wave component is obtained by removing random noise components from a plurality of first curved wave components based on curved wave sensitivity and sensitivity threshold. This includes: comparing the curved wave sensitivity and sensitivity threshold at each transform scale; determining the first curved wave component corresponding to the current transform scale as the target curved wave component when the curved wave sensitivity is greater than or equal to the sensitivity threshold; and determining the first curved wave component corresponding to the current transform scale as a random noise component when the curved wave sensitivity is less than the sensitivity threshold.

[0091] According to embodiments of this disclosure, when the curvature sensitivity is greater than or equal to the sensitivity threshold, the current target curvature component is retained, while when the curvature sensitivity is less than the sensitivity threshold, the random noise component is assigned 0 to remove random noise.

[0092] According to embodiments of this disclosure, the process of removing random noise components can be expressed as the following formula (6).

[0093] (6)

[0094] in, The sensitivity threshold, The target curved component is obtained after processing.

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

[0096] According to embodiments of this disclosure, 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 includes: performing a discrete inverse curvelet transform on the target curvelet component to obtain a primary shear wave component corresponding to each target curvelet component; obtaining feature data of each primary shear wave component; and constructing a threshold function corresponding to each primary shear wave component based on the feature data of each primary shear wave component.

[0097] According to an embodiment of this disclosure, the process of performing discrete inverse curvilinear transformation on the target curvilinear component to obtain the primary shear wave component can be expressed as the following formula (7).

[0098] (7)

[0099] in, This represents the z-th primary shear wave component.

[0100] According to embodiments of this disclosure, the feature data can be the standard deviation of the shear wave coefficients of random noise in each subband, each subband representing the characteristics of the primary shear wave component at a specific scale and direction, and the standard deviation can be estimated using a median estimator. The feature data can be expressed as follows (8).

[0101] (8)

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

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

[0104] (9)

[0105] in, This represents the threshold function.

[0106] According to embodiments of this disclosure, removing residual noise components from primary shear wave components based on a threshold function to obtain target shear wave components includes: comparing the primary shear wave components at each transform scale with the threshold function; determining the primary shear wave components at the current transform scale as target shear wave components if the primary shear wave components are greater than or equal to the threshold function; and determining the primary shear wave components at the current transform scale as residual noise components if the primary shear wave components are less than the threshold function.

[0107] According to embodiments of this disclosure, the target shear wave component The process of determining can be expressed as the following formula (10).

[0108] (10)

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

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

[0111] According to embodiments of this disclosure, inverse shear wave transformation is performed on the target shear wave component to obtain target stratigraphic data, including: performing inverse shear wave transformation on the target shear wave component to obtain target stratigraphic components; and performing stitching processing on the target stratigraphic components to obtain target stratigraphic data corresponding to the initial stratigraphic data.

[0112] According to embodiments of this disclosure, the target shear wave components are transformed into the image domain by inverse shear wave transformation, and the target shear wave components are stitched together, that is, the target shear wave components at multiple scales or directions are fused to obtain the denoised target layer data.

[0113] To better demonstrate the effectiveness of the denoising method based on ice radar stratigraphic data in the embodiments of this disclosure, the following will be explained... Figures 2a-3b Two sets of images illustrate the effects of the embodiments of this disclosure.

[0114] Figure 2a The illustration schematically shows measured initial stratigraphic data of the surface of an island ice sheet according to an embodiment of the present disclosure.

[0115] Figure 2b The illustration schematically shows the target stratigraphic data of an island after denoising processing according to an embodiment of the present disclosure.

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

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

[0118] Figure 3b The illustration schematically shows target stratigraphic data of an island after denoising processing according to another embodiment of the present disclosure.

[0119] like Figure 3a and Figure 3b As shown, the horizontal axis represents distance, and the vertical axis represents depth. (By comparing...) Figure 3a and Figure 3bThe layer data after denoising in the above embodiment 100 is clearer, and the impact of noise is effectively reduced.

[0120] Figure 4 A block diagram of a noise reduction apparatus for ice radar-based stratigraphic data according to an embodiment of the present disclosure is shown schematically.

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

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

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

[0124] Module 430 is used to obtain the curvature sensitivity and sensitivity threshold based on multiple first curvature components and multiple second curvature components.

[0125] The first removal module 440 is used to remove random noise components from multiple first curved wave components based on curved wave sensitivity and sensitivity threshold to obtain the target curved wave component.

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

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

[0128] The inverse shear wave transformation module 470 is used to perform inverse shear wave transformation on the target shear wave components to obtain target stratigraphic data.

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

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

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

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

[0133] According to embodiments of this disclosure, the dual-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 stratigraphic data and noise estimation data to the shear wave domain according to a predetermined transformation scale, so as to obtain multiple first shear components corresponding to the initial stratigraphic data and multiple second shear components corresponding to the noise signal.

[0135] The first conversion submodule is used to convert multiple first shear components to the curve wave domain to obtain multiple first curve wave components corresponding to the first shear components.

[0136] The second conversion submodule is used to convert multiple second shear components to the curve wave domain to obtain multiple second curve wave components corresponding to the second shear components.

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

[0138] A submodule is obtained to obtain multiple curvature sensitivities based on the ratio of the absolute values ​​of the first and second curvature components with the same transformation scale.

[0139] The determination submodule is used to determine the sensitivity threshold based on the curvature sensitivity.

[0140] According to embodiments of this 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 curve wave sensitivity and sensitivity threshold at each transformation scale.

[0142] The first determining submodule is used to determine the first curvilinear component corresponding to the current transform scale as the target curvilinear component when the curvilinear sensitivity is greater than or equal to the sensitivity threshold.

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

[0144] According to embodiments of this disclosure, the inverse curve transform module 450 includes a obtaining submodule, an acquiring submodule, and a constructing submodule.

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

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

[0147] A submodule is constructed to build a threshold function corresponding to each primary shear wave component based on the characteristic data of each primary shear wave component.

[0148] According to embodiments of this disclosure, the second removal module 460 includes a comparison submodule, a first determination submodule, and a second determination submodule.

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

[0150] The first determining submodule is used to determine the primary shear wave component at the current transform 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 the primary shear wave component at the current transform scale as a residual noise component when the primary shear wave component is less than the threshold function.

[0152] According to embodiments of this disclosure, the inverse shear wave transformation module 470 includes a transformation submodule and a splicing 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 stitching submodule is used to stitch together the target layer components to obtain the target layer data corresponding to the initial layer data.

[0155] Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure, or at least part of the functions of any one or more of them, can be implemented in one module. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure can be implemented by dividing them into multiple modules. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure can be at least partially implemented as hardware circuitry, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a System-on-Chip, a System-on-a-Substrate, a System-on-Package, an Application-Specific Integrated Circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure can be at least partially implemented as computer program modules, which, when run, can perform corresponding functions.

[0156] For example, any multiple of the estimation module 410, double transform module 420, obtaining module 430, first removal module 440, inverse warp transform module 450, second removal module 460, and inverse shear transform module 470 can be combined into one module / unit / subunit, or any one of these modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least some of the functionality of one or more of these modules / units / subunits can be combined with at least some of the functionality of other modules / units / subunits and implemented in one module / unit / subunit. According to embodiments of this disclosure, at least one of the estimation module 410, double-conversion module 420, obtaining module 430, first removal module 440, inverse warp transform module 450, second removal module 460, and inverse shear transform module 470 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), programmable logic array (PLA), system-on-a-chip, system-on-a-substrate, system-on-package, application-specific integrated circuit (ASIC), or any other reasonable method of integrating or packaging the circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three methods. Alternatively, at least one of the estimation module 410, double-conversion module 420, obtaining module 430, first removal module 440, inverse warp transform module 450, second removal module 460, and inverse shear transform module 470 can be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

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

[0158] Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0159] like Figure 5 As shown, an electronic device 500 according to an embodiment of this 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 portion 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include 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 this disclosure.

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

[0161] According to embodiments of this disclosure, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to a bus 504. The electronic device 500 may also include one or more of the following components connected to the input / output (I / O) interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 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 disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 510 as needed so that computer programs read from it can be installed into the storage section 508 as needed.

[0162] According to embodiments of this disclosure, the method flow according to embodiments of this disclosure can be implemented as a computer software program. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by processor 501, it performs the functions defined in the system of embodiments of this disclosure. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0163] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0164] According to embodiments of this disclosure, the computer-readable storage medium can be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

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

[0166] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods provided in the embodiments of this disclosure. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the denoising method for ice radar-based stratigraphic data provided in the embodiments of this disclosure.

[0167] When the computer program is executed by the processor 501, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

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

[0169] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on a user's computing device, partially on a user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0170] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features described in the various embodiments of the present disclosure can be combined and / or combined in various ways, even if such combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of this disclosure may be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0171] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A denoising method for stratigraphic data based on ice radar, comprising: The initial stratigraphic data from the ice radar is subjected to progressive denoising processing to obtain noise estimation data corresponding to the noise signal, wherein the initial stratigraphic data includes the noise signal; The initial stratigraphic data and the noise estimation data are processed by dual transform domain to obtain multiple first curvelet components corresponding to the initial stratigraphic data and multiple second curvelet components corresponding to the noise signal. The dual transform domain includes a shear wave domain and a curvelet domain. Based on the plurality of first curved wave components and the plurality of second curved wave components, the curved wave sensitivity and sensitivity threshold are obtained; Based on the curved wave sensitivity and the sensitivity threshold, random noise components are removed from the plurality of first curved wave components to obtain the target curved wave component; The target curve component is subjected to discrete inverse curve transform to obtain the primary shear wave component and the threshold function corresponding to the primary shear wave component. According to the threshold function, the residual noise component is removed from the primary shear wave component to obtain the target shear wave component; The target shear wave component is subjected to inverse shear wave transform to obtain the target stratigraphic data.

2. The method according to claim 1, wherein, The process of performing progressive denoising on the initial stratigraphic data acquired by the ice radar to obtain noise estimation data corresponding to the noise signal includes: The initial stratigraphic data is normalized to obtain the stratigraphic data to be estimated, so that the stratigraphic data to be estimated are all within the target range. The data of the layer to be estimated is subjected to gradual denoising processing according to a predetermined number of iterations, and noise estimation components corresponding to each iteration are obtained sequentially. The noise estimation components corresponding to each iteration are accumulated to obtain noise estimation data corresponding to the noise signal.

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

4. The method according to claim 1, wherein, The step of obtaining the curvature sensitivity and sensitivity threshold based on the plurality of first curvature components and the plurality of second curvature components includes: Multiple curvature sensitivities are obtained based on the ratio of the absolute values ​​of the first curvature component and the second curvature component at the same transformation scale. The sensitivity threshold is determined based on the curve sensitivity.

5. The method according to claim 4, wherein, The step of removing random noise components from the plurality of first curved wave components based on the curved wave sensitivity and the sensitivity threshold to obtain the target curved wave component includes: The curvilinear sensitivity and the sensitivity threshold are compared at each transformation scale; If the curvature sensitivity is greater than or equal to the sensitivity threshold, the first curvature component corresponding to the current transform scale is determined as the target curvature component; If the curvature sensitivity is less than the sensitivity threshold, the first curvature component corresponding to the current transform scale is determined to be 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 the primary shear wave component and the threshold function corresponding to the primary shear wave component includes: Perform a discrete inverse curvature transform on the target curvature component to obtain the primary shear wave component corresponding to each target curvature component; Obtain the characteristic data of each primary shear wave component; Based on the characteristic data of each primary shear wave component, a threshold function corresponding to each primary shear wave component is constructed.

7. The method according to claim 6, wherein, The step of removing residual noise components from the primary shear wave components according to the threshold function to obtain the target shear wave component includes: The primary shear wave component and the threshold function are compared at each transformation scale; If the primary shear wave component is greater than or equal to the threshold function, the primary shear wave component at the current transform scale is determined as the target shear wave component. If the primary shear wave component is less than the threshold function, the primary shear wave component at the current transform scale is determined to be a residual noise component.

8. The method according to claim 1, wherein, The step of performing inverse shear wave transform on the target shear wave component to obtain target stratigraphic data includes: The target shear wave component is subjected to inverse shear wave transform to obtain the target layer component; The target layer components are combined to obtain target layer data corresponding to the initial layer data.

9. A denoising device based on ice radar stratigraphic data, comprising: An estimation module is used to perform progressive denoising processing on the initial stratigraphic data acquired by the ice radar to obtain noise estimation data corresponding to the noise signal, wherein the initial stratigraphic data includes the noise signal; The dual transform module is used to perform dual transform domain processing on the initial stratigraphic data and the noise estimation data respectively to obtain multiple first curvelet components corresponding to the initial stratigraphic 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. The module is used to obtain the curve sensitivity and sensitivity threshold based on the plurality of first curve components and the plurality of second curve components; The first removal module is used to remove random noise components from the plurality of first curved wave components based on the curved wave sensitivity and the sensitivity threshold to obtain the target curved wave component; The inverse curve transform module is used to perform discrete inverse curve transform on the target curve component to obtain the primary shear wave component and the threshold function corresponding to the primary shear wave component. The second removal module is used to remove residual noise components from the primary shear wave component according to the threshold function to obtain the target shear wave component; The inverse shear wave transformation module is used to perform inverse shear wave transformation on the target shear wave component to obtain target stratigraphic data.

10. An electronic device, comprising: One or more processors; Memory, used to store one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 8.

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