问题A： 自动地震水平跟踪
点击链接获取更多资料：数维杯资料获取
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点击链接获取更多资料：数维杯资料获取
随着我国经济和社会的发展，地质工作的重要性也在不断增加。地震资料解释 是地震勘探工程的一个重要阶段，它可以明确油气勘探的地下构造特征，为油气勘 探提供良好、有利的储层；准确的地层信息是地震资料解释的基础，是储量预测的 重要依据。地震水平跟踪是地震数据解释的关键技术之一，一种较好的地震水平跟 踪方法可以大大提高地震数据解释的效率和准确性。
地震勘探的主要目标是获取地下结构岩性和储层的信息， 由于主地层界面一般 是良好的波阻抗界面，地震波在地下介质中传播时受到地层界面的影响，最终表现 出均质地震反射轴的形态、强度、频率和连续性等不同的地震反射特征。地层界面 的形状和埋深等结构信息。由于这类结构信息是地震资料中最直观、最容易使用的 信息， 自地震勘探技术诞生以来，从地震资料中提取结构信息已成为地震勘探的重 要目标之一。
在反射地震数据中，地震波阻抗界面通常对应于地层界面或岩性界面，但岩性 界面并不能总是形成波阻抗界面，只有在波阻抗差足够大的相邻地层中才能形成波 阻抗界面。虽然不同地质时代形成的地层的岩性通常不同，但只有通过数百万年的 沉积压实和沉积间断的交替，才能产生岩石物理性质的差异
(密度、孔隙度等) 在相邻地层之间，岩性和岩石物理性质 (差异) 的结合将形成 显著的波阻抗差异，因此，地震剖面上的地震反射事件轴通常对应于沉积的等时面 ，而不是宏观的岩性界面。根据这一理论，地震事件轴所表示的地层界面是地层沉 积过程的不连续性，由于其相对同步，该沉积不连续性与地层的构造特征基本一致 ，因此，地震事件轴是识别地层界面的主要标志。地震事件轴的空间分布特征和时 域变化特征是水平解释的主要依据。地震事件轴也可用于获取地层倾角和方位角等 信息。
在二维地震勘探时代和三维地震勘探早期，地震资料的水平解释主要是单层， 即从地震剖面中选择若干与强地层反射界面相对连续性良好的地震事件轴进行跟踪 。由于这种层数解释方法效率低，在地震剖面上容易追踪的地震事件轴的数量较少 ，可得到的层数有限，导致传统的地震构造解释模型无法获得详细的地质构造信息 ，因此对地质构造特征的详细描述不够清楚。也就是说，传统的地震层解释方法忽 略或浪费了大量的地震信息，在准确性和效率方面都无法满足现代地震结构解释和 地质综合研究的要求。随着三维地震勘探的发展，特别是高密度地震勘探技术，获 得的地震资料的精度越来越高，地震资料的数量越来越多，从地震资料中自动提取 结构、岩性、流体等信息已成为现代地震资料解释进展的关键，这也是一个目标：
地球物理学家和地质学家正在努力争取。
现有的地震层跟踪方法通常由地震层解释器手动完成。在解释地震资料时， 对事件轴的跟踪是非常重要的。解释器主要基于地震波动力学和运动学特征，即 振幅、同相或连续性、波形相似度三个标准和人工对比跟踪。人工水平跟踪是利 用波形相似度手动跟踪二维地震剖面上底层的连续反射事件轴，得到水平线 (地 层界面) ，然后对所有水平线进行插值形成水平面。然而，人工水平跟踪人工成 本需求很大，不仅耗时，而且对地震勘探的效率也有很大的影响。
为了克服跟踪时间效率低、结果可靠性差的问题，近年来研究者开始高度重 视自动水平跟踪方法。 自动水平跟踪方法是在地震迹道上搜索具有相似特征的“ 种子点” ，搜索这些特征，并在满足条件后反复搜索下一个区域。该方法解决了 当地形较复杂时，难以获得人工获取的水平信息，且获得的信息比人工获取的信 息更准确的问题。
目前，有两种更好的自动水平跟踪标准，即基于波形特征的自动跟踪和基于相 关性的自动跟踪。基于波形特征的自动跟踪是只寻找相似的波形结构 (波峰、波谷 、零交叉等) 。在搜索时间窗中的特征点，但没有在地震迹线之间进行相关计算， 并将定义的波谷、波峰和交叉点逐一搜索。由于地下局部区域之间的连续性和稳定 性反映在地震时间剖面上，这是地震波反射层与相邻地震航道上地震波反射层振幅 的相似性和连续性。因此，基于相关的水平进行自动跟踪
算法中，以种子点为中心，根据相关的时间窗范围，选择一个地震通道，并将该段 地震数据的地震数据与相邻通道的搜索时间窗中的地震数据相关联，如果在搜索时 间窗中找到满足条件的特征点，则将该点固定为新的种子点，然后选取下一条轨迹
在上面
请根据所附数据建立数学模型，以解决以下问题：
(1)地震资料中噪声较大，请采用有效方法对附件数据进行去噪。
(2)建立地震层自动跟踪模型的相关性或设计相应的新跟踪算法，并对附件数 据进行跟踪。
(3)建立基于波形特征的自动跟踪模型，或设计相应的新的跟踪算法，并对附 件数据进行跟踪。
(4)对两种自动跟踪模型 (或算法) 的结果进行评价，验证模型的合理性，分 析实验数据与实际数据之间的误差，并做出合理的解释。
(5)建立了一个基于相关性和波形的三维水平自动跟踪模型，并根据该算法中 给出的数据实现了一种算法
附件，实现水平跟踪和故障数据的识别和分析。
记下
剖面由一组曲线组成，水平跟踪，是跟踪事件轴。纵断面上的事件轴是曲线， 多个事件轴条曲线构成视界。
2022_“
ShuWei Cup**”**
Problem A**：Automatic seismic horizon tracking**
With the economic and social development of our country, the importance of
geological work is also increasing. Seismic data interpretation is an important stage of
seismic exploration engineering, which can clarify subsurface tectonic features for oil
and gas exploration and can provide good and favorable reservoirs for oil and gas
exploration; accurate stratigraphic information is the basis of seismic data
interpretation and is an important basis for storage prediction. Seismic horizon
tracking is one of the key technologies in seismic data interpretation, a good seismic
horizon tracking method can greatly improve the efficiency and accuracy of seismic
data interpretation.
It is the main goal of seismic exploration to obtain the information of
underground structure lithology and reservoir, because the main formation interface is
generally a good wave impedance interface, the seismic wave is affected by the
formation interface when it propagates in the underground medium, and finally shows
different seismic reflection characteristics, such as the morphology, intensity,
frequency and continuity of the homogeneous axis of seismic reflection. Structural
information such as the shape and burial depth of the stratigraphic interface can be
obtained directly from seismic data. Since this kind of structural information is the
most intuitive and easily used information of seismic data, it has become one of the
most important targets of seismic exploration to extract structural information from
seismic data since the birth of seismic exploration technology.
In reflected seismic data, the seismic wave impedance interface usually
corresponds to the formation interface or lithologic interface, but the lithologic
interface can not always form wave impedance interface, only in those adjacent
formations with large enough wave impedance difference can form wave impedance
interface. Although the lithology of strata formed in different geological ages is
usually different, only through the alternation of sedimentary compaction and
sedimentary hiatus in millions of years can the differences in rock physical properties(density, porosity, etc.) between adjacent strata be revealed, the combination of
lithology and rock physical properties (differences) will form significant wave
impedance differences, therefore, seismic reflection events axis on seismic profiles
usually correspond to sedimentary isochronous surfaces rather than macroscopic
lithological interfaces. According to this theory, the stratigraphic interface indicated
by seismic events axis is the discontinuity of the stratigraphic deposition process,
because of its relative isochronism, this sedimentary discontinuity is basically
consistent with the structural characteristics of the stratum, therefore, seismic events
axis is the main signs to identify the stratigraphic interface. The spatial distribution
characteristics and time domain variation characteristics of seismic events axis are the
main basis for horizon interpretation. Seismic events axis can also be used to obtain
information such as stratigraphic dip and azimuth.
In the era of two-dimensional seismic exploration and the early stage of
three-dimensional seismic exploration, the horizon interpretation of seismic data is
mainly single-layer, that is, several seismic events axes with good continuity
corresponding to the strong stratigraphic reflection interface are selected from the
seismic profile for tracking. Because of the low efficiency of this horizon
interpretation method and the small number of seismic event axis that can be easily
traced on the seismic profile, the number of horizons that can be obtained is limited,
resulting in the traditional seismic structure interpretation model unable to obtain
detailed geological structure information, so the detailed description of geological
structure characteristics is not clear enough. In other words, the traditional seismic
horizon interpretation method ignores or wastes a lot of seismic information, and it
has been unable to meet the requirements of modern seismic structure interpretation
and geological comprehensive research in terms of accuracy and efficiency. With the
development of three-dimensional seismic exploration, especially high-density
seismic exploration technology, the accuracy of seismic data obtained is getting
higher and higher, and the number of seismic data is increasing, automatic extraction
of structural, lithological, fluid and other information from seismic data has become
the key to the progress of modern seismic data interpretation, it is also the goal thatgeophysicists and geologists are striving for.
The existing seismic horizon tracking methods are usually done manually by
seismic horizon interpreters. In the interpretation of seismic data, the tracking of the
event axis is very important. Interpreters are mainly based on seismic wave dynamics
and kinematics characteristics, namely amplitude, in-phase or continuity, waveform
similarity three criteria, and artificial contrast tracking. The artificial horizon tracking
is to manually track the continuous reflection events axes of the bottom layer on the
two-dimensional seismic profile by using the waveform similarity to obtain the
horizon line (stratigraphic interface), and then interpolate all the horizon lines to form
the horizon surface. However, artificial horizon tracking labor cost demand is large,
not only time-consuming, but also has a great impact on the efficiency of seismic
exploration.
In order to overcome the problems of low tracking time efficiency and poor
reliability of results, researchers have begun to attach great importance to the
automatic horizon tracking method in recent years. The automatic horizon tracking
method is to search for ' seed points ' with similar characteristics on seismic traces,
search through these characteristics, and search the next region repeatedly after
meeting the conditions. This method solves the problem that it is difficult to obtain
artificial horizon information when the terrain is more complex, and the information
obtained is more accurate than that obtained manually.
At present, there are two better automatic horizon tracking criteria, namely
automatic tracking based on waveform characteristics and automatic tracking based
on correlation. Automatic tracking based on waveform features is to find only similar
waveform structures (crests, troughs, zero crossings, etc.) of feature points in the
search time window, but no correlation calculations are performed between the
seismic traces, and the defined troughs, crests, and crossings are searched one by one.
Because the continuity and stability between the local areas of the underground are
reflected in the seismic time profile, it is the similarity and continuity of the seismic
wave reflection layer in the amplitude of the seismic wave reflection layer on the
adjacent seismic channel. Therefore, based on the relevant horizon automatic trackingalgorithm, the seed point is taken as the center, according to the relevant time window
range, a seismic channel is selected, and the seismic data of this section of seismic
data is correlated with the seismic data in the search time window of the adjacent
channel, if the characteristic point that meets the conditions is found in the search
time window, the point is fixed as a new seed point, and then the next trace is picked
up.
Please establish a mathematical model based on the attached data to solve the
following problems:
（1）There are often a lot of noise in seismic data, please use effective methods
to denoise the accessory data.
（
2）Establish the correlation of seismic strata automatic tracking model or
design the corresponding new tracking algorithm, and track the attachment data.
（
3）Establish an automatic tracking model based on waveform features or
design a corresponding new tracking algorithm, and track the attachment data.
（
4）Evaluate the results of two automatic tracking models (or algorithms),
verify the rationality of the model, analyze the error between the data obtained from
the experiment and the actual data, and make a reasonable explanation.
（5）Establish a three-dimensional horizon automatic tracking model based on
correlation and waveform, and an algorithm is implemented on the data given in the
annex to realize horizon tracking and identify and analyze the fault data.
Notes:
A profile is made up of a set of curves, horizon tracking, is to trace the event axis.
An event axis on a profile is a curve, and multiple event axis bar curves, make up the
horizon.
