Abstract：
Microorganisms play a role in the construction or modulation of various types of landforms. They are especially notable for forming microbially induced sedimentary structures (MISS). Such microbial structures have been considered to be among the most likely biosignatures that might be encountered on the martian surface. Twenty-nine algorithms have been tested with images taken during a laboratory experiment for testing their performance in discriminating mat cracks (MISS) from abiotic mud cracks. Among the algorithms, neural network types produced excellent predictions with similar precision of 0.99. Following that step, a convolutional neural network (CNN) approach has been tested to see whether it can conclusively detect MISS in images of rocks and sediment surfaces taken at different natural sites where present and ancient (fossil) microbial mat cracks and abiotic desiccation cracks were observed. The CNN approach showed excellent prediction of biotic and abiotic structures from the images (global precision, sensitivity, and specificity, respectively, 0.99, 0.99, and 0.97). The key areas of interest of the machine matched well with human expertise for distinguishing biotic and abiotic forms (in their geomorphological meaning). The images indicated clear differences between the abiotic and biotic situations expressed at three embedded scales: texture (size, shape, and arrangement of the grains constituting the surface of one form), form (outer shape of one form), and pattern of form arrangement (arrangement of the forms over a few square meters). The most discriminative components for biogenicity were the border of the mat cracks with their tortuous enlarged and blistered morphology more or less curved upward, sometimes with thin laminations. To apply this innovative biogeomorphological approach to the images obtained by rovers on Mars, the main physical and biological sources of variation in abiotic and biotic outcomes must now be further considered.
摘要：
微生物在构造或调节各种类型的地貌中起作用。它们在形成微生物诱导的沉积结构（MISS）方面尤其值得注意。这种微生物结构被认为是在火星表面可能遇到的最可能的生物特征之一。已经使用实验室实验期间拍摄的图像测试了29种算法，以测试它们在将垫子裂缝（MISS）与非生物泥浆裂缝区分开方面的性能。在算法中，神经网络类型产生了出色的预测，相似的精度为0.99。在这一步之后，卷积神经网络（CNN）方法已经过测试，以确定它是否可以在不同的自然地点拍摄的岩石和沉积物表面图像中最终检测到MISS，在这些地点观察到存在和古老（化石）微生物垫裂缝和非生物干燥裂缝。CNN方法显示了从图像中对生物和非生物结构的出色预测(全局精度，灵敏度,和特异性，分别,0.99、0.99和0.97）。机器感兴趣的关键领域与人类在区分生物和非生物形式（在其地貌意义上）方面的专业知识非常匹配。图像显示了在三个嵌入尺度上表达的非生物和生物情况之间的明显差异：纹理(大小，形状,以及构成一种形式的表面的颗粒的排列)，形式（一种形式的外部形状），和形式安排的模式（在几平方米上的形式安排）。生物源性的最有区别的成分是垫子裂纹的边界，其曲折的扩大和起泡的形态或多或少地向上弯曲，有时有薄的叠片。为了将这种创新的生物地貌方法应用于火星上的火星车获得的图像，现在必须进一步考虑非生物和生物结果变化的主要物理和生物来源。
